In The Name of God The Compationate, The Merciful
2nd Iranian Biennial Seminar of C H E M O M E T R I C S
Chemistry Department of Urmia University
Sponsors:
ﺮ ﭼﯿﺰی رﺍ ﺯﻧﺪه ب ﻫ ﮔﺮد آ ﺍﻧﺪ ﺍﺯ ﯾﻢ ﭘﺎرك ﻋﻠﻢ و ﻓﻦ آوری دﺍﻧﺸﮕﺎه ﺻﻨﻌﺘﯽ ﺍروﻣﯿﻪ ﺍﺳﺘﺎن آذرﺑﺎﯾﺠﺎن Urmia University Of ﺷﺮﮐﺖ آب و ﻓﺎﺿﻼب ﻏﺮﺑﯽ ﺍﺳﺘﺎن آذرﺑﺎﯾﺠﺎن ﻏﺮﺑﯽ Technology
وﺯﺍرت ﮐﺸﻮر ﺷﺮﮐﺖ ﻣﻠﯽ ﺻﻨﺎﯾﻊ ﺍﺳﺘﺎﻧﺪﺍری ﺍﺳﺘﺎن آذرﺑﺎﯾﺠﺎن ﻏﺮﺑﯽ ﭘﺘﺮوﺷﯿﻤﯽ ﺍﯾﺮﺍن IV
In The Name of God
Praise and thank God who led mankind into thought, wisdom and recognition of enormity of creation, and made knowledge the ray and cause of salvation and placed scholars and learned men as the bright lights in the path of seekers of eminent center of humanity which are insight into the perceiving of the nature of entity. For the advancement of chemometrics as well as other sciences, Iranian professors of chemometrics applied all efforts to present new discoveries in the field of chemometrics that were affected through these discoveries and their significance considering economic and efficiency issues. Therefore comprehensive attempts should be carried out in ways relating to analytical chemistry. It is hoped that the seminar will open the path to take further steps towards new approaches under Divine Kindnesses. It is our great pleasure to host the 2nd biennial seminar of chemometrics in a true collaboration with Iranian Society of chemistry and participation of most experienced and outstanding Iranian colleagues in this beautiful and historic city of Urmia. Undoubtedly, with increasing attention to economic issues during the recent years, it is required that exchange of knowledge and increase of the national collaborations to improve human efforts in the field of CHEMOMETRICS be carried out. We would like to express our appreciation to respectable Vice Chancellors of Urmia University, board of directors of Iranian Society of Chemistry, all members of scientific and organizing committees and also my colleagues for their dedicated efforts to present and manage this seminar. We wish you all a pleasant stay in Urmia and hope that you will take advantage of this opportunity.
Morteza Bahram-Ph.D Scientific Secretary of IBSC 2009 V
Dear Colleagues
Thank God who created the universes and put the responsibility and burden of discovering the facts and knowledge to mankind. We are pleased to welcome everybody present in the seminar. We hope that your 3-day stay in Urmia will be pleasant. We wish to express our gratitude for your presence and congratulate the coincidence of the birthday of Imam Reza and 2nd Iranian Biennial Seminar of Chemometrics. We hope that the seminar will meet your expectations.
Hasan Sedghi-Ph.D President of Urmia University VI
Executive Committee Scientific Committee of Seminar of Seminar
Dr. M. Bahram Dr. H. Abdollahi Dr. A. Naseri Urmia University Institute for Advanced Studies in Tabriz University Basic Sciences, Zanjan Dr. Kh. Farhadi Dr. Zeinali Urmia University Dr. G. Azimi Islamic Azad University, Arak University Arak Branch Dr. H. Rezaee Urmia University Dr. M. Bahram Urmia University Dr. R. Sabzi Urmia University Dr. M. Fatemi Mazandaran University Dr. N. Samadi Urmia University Dr. J. Ghasemi K.N. Toosi University of M.Sc. F. Hajilari Technology West Azerbaijan Water and Wastewater Company Dr. B. Hematinejad Shiraz University M.Sc. F. Khalilzade West Azerbaijan Water and Dr. M. Jalali-Heravi Wastewater Company Sharif University of Technoloy
M.Sc. Y. Shamchi Dr. T. Khayamian West Azerbaijan Water and Isfahan University of Technology Wastewater Company Dr. M. Kompany M.Sc. S. Talebi Institute for Advanced Studies in West Azerbaijan Water and Basic Sciences, Zanjan Wastewater Company XI
Referee Committee Organizer Committee of Seminar of Seminar
Dr. H. Abdollahi Dr. T. Khayamian Dr. H. Sedghi Institute for Advanced Studies I s f a h a n U n i v e r s i t y o f President of Urmia University Technology in Basic Sciences, Zanjan Dr. N. Samadi Dr. K. Asadpur Dr. M. Kompany Financial Vice-President of Urmia Unicersity Tabriz University Institute for Advanced Studies in Basic Sciences, Zanjan Dr. M. Maham Dr. G. Azimi Dr. M. Mousavi Research and Technology Vice- Arak University President of Urmia University hahid Bahonar University of Dr. M. Bahram Kerman Dr. H. Ghahramanlo Urmia University Head of Faculty of Science of Dr. A. Naseri Urmia University Dr. M. Fatemi Tabriz University Mazandaran University Dr. H. Abdollahi Dr. A. Niazi Chairman of Chemometrics Dr. J. Ghasemi Committee of Iranian Society of K.N. Toosi University of Islamic Azad University, Arak Chemistry Technology Branch
Dr. B. Hemmateenejad Dr. R. Tabaraki Shiraz University Ilam University Dr. M. Jalali-Heravi Sharif University of Technoloy
Dr. G. Jouyban Tabriz University of Medical Science XI
Content
The Story of Chemometrics 1 Mehdi Jalali-Heravi
What is the Meaning of Feasible Band Boundaries in Self-Modeling/Multivariate Curve Resolution? 2 Hamid Abdollahi
Orthogonalization in Variable Reduction and Selection 3 Mohsen Kompany-Zareh
Orthogonal Signal Correction in Spectrophotometric and Voltammetric Data 4 Ali Niazi, Jahanbakhsh Ghasemi
Chemometrics Methods for Determination of Kinetic Parameters of Different Enzymatic Reactions 5 A. Naseri
On the Effect of Mean Centering of Ratio Spectra as a Preprocessing Method Prior to Soft Modeling Approach: An 6 Introduction Morteza Bahram
The Use of Chemometrics Methods in Electroanalytical Chemistry 7 Karim Asadpour-Zeynali
Applications of Chemometrics in Water and Wastewater Analysis; Iranian Water and Wastewater industries needs 8 Fatemeh Hajilari, Sohrab Talebi
Resolving Factor Analysis Using Chaotic Particle Swarm Optimization 11 Hamid Abdollahi, Samira Beyramy soltan
Uncertainties and error propagation in kinetic and equilibrium hard-modelling of spectroscopic and pH-metric data 12 Hamid Abodollahi, Parvin Darabi
Application of Multivariate Curve Resolution based on Alternative Least Square assisted with Trilinearity Constraint (TC- MCR-ALS) for Resolution of Multi-Way Rank Deficient Systems 13 Mohsen Kompany-Zareh, Fatemeh Ghasemi-Moghadam
Classification of Drugs by Means of Their Milk/Plasma Concentration Ratio Using Supervised Chemometric Procedures 14 M.H Fatemi, M. Ghorbanzad'e, E. Baher
Application of Successive Projections Algorithm (SPA) as a Variable Selection in a QSPR Study to Predict of the 15 Octanol/Water Partition Coefficients (Kow) of Some Halogenated Organic Compounds Mohammad Goodarzi, Nasser Goudarzi
Second-Order Advantage From Micelle Concentration Gradual Change–Visible Spectra Data 16 Hamid Abdollahi, Mahmoud Chamsaz, Tahereh Heidari XII
Partial Swarm Optimization Approach for Training of an Artificial Neural Network Applied in Thermal Investigation of Nanocomposites 17 Mohammadreza Khanmohammadi, Nafiseh Khoddami, Mohammad Hossein Ahmadi Azghandi, A m i r Bagheri Garmarudi, Masumeh Foroutan, Mahdieh Ansaryan
Application of Standardization Methods in Simple Kinetic and Equilibrium Studies 18 Mohsen Kompany-Zareh, Maryam Khoshkam
Random Forests, a Novel Approach for Prediction of the Acute Toxicity of Substituted Benzenes to Tetrahymena 19 Pyriformis Anahita Kyani 20 Application of Bayesian Adaptive Regression Splines for QSAR Modeling of Glutamate Inhibitors Mehdi Jalali-Heravi, Ahmad Mani-Varnosfaderani
Simultaneous Spectrophotometric Determination of 2-Furaldehyde and 5-Hydroxymethyl-2-Furaldehyde by Using Ant Colony Algorithm-Based Wavelength Selection-Partial Least Squares Regression 21 M. Shamsipur, A.A. Miran Beigi, V. Zare-Shahabadi, M. Teymouri, S. Ghahremani
Theoretical Study of Inhibition Effect of Some Imidazole Derivatives on Mild Steel 22 Mehdi Mousavi, Mohammad Mohammadalizadeh
Mean Field Independent Component Analysis (MF-ICA) as a Self-Modeling Curve Resolution (SMCR) Technique 23 Mehdi Jalali-Heravi, Hadi Parastar
Application of Multiple Regression Systems in Mixture Analysis Using Non-Selective Spectral Data 24 Hamid. Abdollahi, Akram. Rostami
New QSPR Model for Aqueous Solubility Prediction of Drugs 25 Ali Shayanfar, Abolghasem Jouyban
Prediction of Some Thermodynamic Properties forBinary Mixtures of Water and Ionic Liquids of Pyridinium-Based 26 A. Naseri, M. H. Soleimanian
Quantitative Structure-Inhibition Relationship Studies of Trifluoromethylimidazoles and Phenylpyrazoles for Xanthine Oxidase by MLR and WNN 27 Shahin Salimpour, Reza Tabaraki
Use of Self-Training Artificial Neural Networks in Modeling of SPME–GC–MS Relative Retention Times of the Constituents of Saffron Aroma 28 Karim Asadpour-Zeynali, Naser Jalili-Jahani, Javad Vallipour XIII
Quantitative Analysis of Ternary Organic Mixture by Multivariate Curve Resolution 29 Karim Asadpour-Zeynali, Javad Vallipour
Mean Centering of the Ratio Spectra for Preprocessing of Spectrophotometric Complexometric Data to Determine the Stability Constants 30 Morteza Bahram, Setareh Gorji, Mehdi Mabhooti, Abdolhosein Naseri, Nader Norouzi- Pesian
An Investigation on the Macroscopic and Microscopic Acidity Constants of Benzene Tricarboxylic Acids by NMR 31 Spectroscopy Method; a Model Based Analysis Azimi Gholamhassan, Azadi Marzieh, Zolgharnein Javad, Sangi Mohammad Reza
Hard-Modeling Thermodynamic Characterization of Methylene Blue Dimerization and Complexation with Some Cyclodextrins 32 H. Abdollahi, F. Rabbani
Thermodynamic Characterization of Benzoylacetone Tautomerization Equilbrium in the Presence of b- Cyclodextrin 33 H. Abdollahi, A. Safavi, S. Zeinali
QSAR Studies on Benzodiazepine Classes as a Selective GABAA a5 Inverse Agonist Using Homology Modeling, Molecular Dynamic Simulation, Docking and Support Vector Machine 34 S. Gharaghani, T. Khayamian, F. Keshavarz
Combining Hard and Soft Modelling Parallel Factor Analysis to Solve Equilibrium Process 35 H. Abdollahi, S.M. Sajjadi
QSAR Analysis of Diaryl COX-2 Inhibitors: Comparison of Feature Selection Methods 39 Hoda Abolhasani, Somaieh Soltani, Abolghasem Jouyban
Using of Box Behnken Design Method to Optimize Effective Parameters for Removal of Ni+2 from Aqueous 40 Solution by ZSM-5 Zeolite M. Abrishamkar, S. N. Azizi, H. Kazemian 41 Theoretical Determination of the Number of Branches in the PAMAM Dendrimers A.H. Massoudi, J. Lari, O. Louie, S.Sajjadifar, A. Agah
Spectrophotometric Simultaneous Determination Cobalt and Nickel Using 5-Br-PADAB in Alloys by Partial Least 42 Squares Z. Aghajani, M. Bordbar, M. M. Ahari-Mostafavi, M. Rezai-Bina XIV
Using Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to the Qquantitative Analysis of Retinoic Acid Isomers (Tretinoin, Isotretinoin and Alitretinoin) in Lotion Formulations 43 M. Bordbar, A. Yeganeh faal, M. M. Ahari- Mostafavi
QSRR Study of Benzenoid, Aldehyde, Ketone, Cycloalka/Enes and Heterocyclic Aromates Derivatives Using Linear and Nonlinear Chemometrics Methods 44 Zahra Garkani-Nejad, Behzad Ahmadi-Roudi
Prediction of Retention Times of Benzenoid, Aldehyd, Ketone, Cycloalka/Enesand Heterocyclic Aromates 45 Derivatives Using Different Chemometrics Methods Zahra Garkani-Nejad, behzad Ahmadi-Roudi
Molecular Recognition of Arginine and Lysine Complexes Toward CalixCrown-Biolinker: FT-IR Vibration Analysis 46 Afsaneh Amiri, Mehri Abdollahi fard, mona damavandi
Comparison of Artificial Neural Network With Multivariate Linear Models for Prediction of Retention Times of Chlorinated Pesticides, Herbicides, and Organohalides 47 Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour, Mehdi Alizadeh
Prediction of Retention Times of Phenols Based on Quantitative Structure-Retention Relationships 48 Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour, Mehdi Alizadeh
Application of Response Surface Methodology (RSM) for Optimization of Thallium (I) Removal by Modified Ulmus Carpinifolia Tree Leaves 49 Javad Zolgharnein, Neda Asanjarani, Tahere Shariatmanesh
The Hydrogen Perturbation in Molecular Connectivity Indices and Their Application to QSPR Study 50 M. Atabati, K. Zarei, R. Emamalizadeh
Prediction Drug Aqueous Solubility by Support Vector Machine from Their Theoretical Molecular Descriptors 51 M.H. Fatemi, E. Baher, M. Ghorbanzade
Simultaneous Spectrophotometric Determination of Atenolol and Propranolol in Combined Tablet Preparation by Partial Least Square Regression Method 52 Amir H.M .Sarrafi, Masoumeh Bakhtiari
Development and Validation of a Method for Fast Chromatographic Determination of Aflatoxins in Iranian 53 Pistachio Nuts from Complex HPLC-DAD Signals Maryam Vosough, Mahin Bayat
Quantitative Structure Property Relationships Study of Air to Liver Partition Coefficients for Volatile Organic 54 Compounds Using Partial Least Squares and Artificial Neural Network Zahra Dashtbozorgi, Hassan Golmohammadi XV
Response Surface Method for Simultaneous Optimization of VariousExperimental Parameters in Cloud Point Extraction and Determination of Cd(II),Cr(III), Fe(II) and Ni(II) in Water Samples by Flame Atomic Absorption Spectrometry 55 N. Samadi, M.R. Vardast, B. Mehrara, M. Bahram
Experimental Design for the Optimization of Cloud Point Extraction andDetermination of Co(II), Cu(II) and Ag(I) 56 by Flame Atomic Absorption Spectrophotometry Naser Samadi, Mohammad Reza Vardast, Amir Chehrehgani, Morteza Bahram
Optimization of Dispersive Liquid-Liquid Microextraction Followed by Flame Atomic Absorption Determination of Cu(II), Zn(II) and Cd(II) Based on the Complexation Reaction With 2,3,3-Trimethyl-3H-Pyrrolo (3,2-h) Quinoline by Experimental Design 57 N. Samadi, M.R. Vardast, B. Mehrara, M.A. Farajzadeh
Central Composite Design and Response Surface Methodaology for the Optimization of Dispersive Liquid- Liquid Microextraction and Analysis of Organophosphorus Pesticides by High-Performance Liquid 58 Chromatography M. A. Farajzadeh, M. R. Vardast
Quantitative Structure-Activity Relationship Study of HIV-1 Integrase Inhibitors Using Particle Swarm 59 Optimization M. Jalali-Heravi, H. Ebrahimi-Najafabadi
Utilization of Central Composite Design Methodin the Optimization of a Chemiluminescence Reaction Parameters of Penicillin G Potassium Determination in Real Samples 60 M.H. Sorouraddin, M. Fadakar-Sardroud, M. Iranifam, A. Imani-Nabiyyi
The Effect of Surfactant Micelles on Acidity Constant of Bromothymol Blue-Sodium Salt 61 Amir H. M. Sarrafi, Samane Famili
Application of ACA-PLS and GA-PLS for Simultaneous Spectrophotometic Determination of Thiophene, 2- Methyl Thiophene and 3-Methyl Thiophene 62 N.Farzin-Nejad, E.Shams Solari1, M.K.Amini, A.A.Miran Beigi, V. Zare-Shahabadi
Multiwavelength Spectrophotometric Determination of Acidity Constant of 5-Nitro-2-(2-Nitro-Phenyleazo)- Phenol,(4-e) in Water, Water SDS and Water-Triton X-100 Micellar Media Solutions 63 Mohammad Ghalei, Amir Hosein Moohsen Sarafi
Determination of Acidity Constant of 2-(2H-Benzo[d] [1, 2, 3] Triazol-2-yl) Phenol in Water and Micellar Media 64 Solutions Amir H. M. Sarrafi, Negin Ghorashi XVI
Spectrophotometric Determination of Acidity Constant of Bromocresol Purple in Water, Water-Brij-35 and Water-SDS 65 Amir H. M. Sarrafi, Negin Ghorashi, Mahboobeh Nimroozi
Monitoring of Some Pesticides in Water Samples With SPE- HPLC Method Including an Uncertainty Estimation of the Analytical Results 66 A. Ghorbani, F. Aflaki, M. Aghaei 67 Prediction of Anti HIV-1 Activity of Non-Nucleoside Inhibitors by QSAR Approaches Mohammad Hossein Fatemi, Zahra Ghorbannezhad 68 Optimization Of Theoretical Plate Heights in Chromatography Kiumars Ghowsi, Hossein Ghowsi 69 QSPR Modeling of Optical Rotation for Biodegradable Polymers Using an Artificial Neural Network Hassan Golmohammadi, Zahra Hassanzadeh 70 Prediction of Inherent Viscosity for Optically Active Polymers from the Theoretical Derived Molecular Descriptors M. A. Farajzadeh, M. R. Vardast, Hassan Golmohammadib
Prediction of Water-to-Polydimethylsiloxane Partition Coefficient for Some Organic Compounds Using QSPR 71 Approaches Hassan Golmohammadi, Zahra Dashtbozorgi
Quantitative Structure-Property Relationship Study of Electrophoretic Mobilities of Some Organic and Inorganic Compounds Using SVM 72 Nasser Goudarzi, Mohammad Goodarzi, M. H. Fatemi
Simultaneous Spectrophotometric Determination of Uranium and Zirconium Using Cloud Point Extraction and Multivariate Methods 73 Jahanbakhsh Ghasemi, Beshare Hashemi
Simultaneous Determination of Paracetamol, Phenylephrine Hydrochloride and Chlorpheniramine Maleate Using Partial Least Squares-1 (PLS-1) Regression 74 Abdolraouf Samadi–Maybodi, Seyed Karim Hassani Nejad–Darzi
In Silico Prediction of Aqueous Solubility of Some Organic Compounds 75 Mohammad Hossein Fatemi, Afsane Heidari
Artificial Neural Networks and Least-Square Support Vector Machine Applied for Simultaneous Analysis of 76 Mixtures of Nitrophenols by Conductometric Acid-Base Titration Gholamhossein Rounaghi, Roya Mohammad Zadeh, Tahereh Heidari XVII
Spectrophotometric Determination of Trace Amounts of Beryllium in Natural Water Using Mean Centering of Ratio Spectra Method and Orthogonal Signal Correction-Partial Least Squares Regression 77 Zeinab Rohbakhsh, Akram Hajinia, Tahereh Heidari
QSAR Study of Some Anti Fungous Benzofurans Using Artificial Neural Networks 78 Zakieh Izakian
H-point Standard Addition Method Applied to Simultaneous Kinetic Determination of Antimony(III) and 79 Antimony(V) by Adsorptive Linear Sweep Voltammetry K. Zarei, M. Atabati, M. Karami
Simultaneous Spectrophotometric Determination of Lead, Copper and Nickel Using Xylenol Orange by Partial 80 Least Squares Calibration Method Jahan Bakhsh. Ghasemi, Samira. Kariminia
Simultaneous Kinetic Spectrophotometic Determination of Levodopa and Benserazide Based on the Surface Plasmon Resonance Band of Silver Nanoparticle and Artificial Neural Network 81 M.Reza Hormozi Nezhad, J.Tashkhourian, J. Khodaveisi
Application of Artificial Neural Network in Infrared Spectrometric Quality Control of Dairy Products 82 Mohammadreza Khanmohammadi, Amir Bagheri Garmarudi, Keyvan Ghasemi
Speciation and Determination of Inorganic Selenium Species by a Simple and Rapid Technique Using Selective Separation on Mercury Coated Electrode Coupled With Electrothermal Atomic Absorption Spectroscopy (ED- ETAAS) in Water Samples 83 Nahid Mashkouri Najafi, Shahram Seidi, Alireza Ghasempour, Reza Alizadeh, Hamed Tavakoli, Ensieh Ghasemi
Simultaneous Extractive Spectrophotometric Determination of Fe(II) and Fe(III) Using PAR and HDPB by Partial Least Squares Method 84 J. Ghasemi, S. H. Kiaee
Prediction of the Peptides' Affinities for Carbon Nanotubes Using Linear Interaction Energy Model 85 Anahita Kyani, Bahram Goliaei
-1 Prediction of Log (IGC50) for Benzene Derivatives to Ciliate Tetrahymena Pyriformis from Their Molecular Descriptors. 86 Mohammad H. Fatemi, Hanieh Malekzadeh
Simultaneous Spectrophotometric Determination of Ascorbic Acid and Epinephrine by Kinetic H-Point Standard Addition Method 87 Alireza Mohadesi, Hamideh Mirzaabdollahi XVIII
Determination of PABA Concentration in B-Complex Tablets by MCR-ALS Method 88 Mohammad Mirzaei, Mehdi Khayyati
Study of Synthesis of Biologically Active Pyrimido [2,1-b]Benzothiazoles from Propiolic Acid and Benzotiazol- 2Amino by Chemometrics 89 Mohammad Mohammadalizadeh, Mehdi Mousavi, Hassan Sheibani
Application of Soft-Modeling Approaches to Resolution of Electron Donor- Acceptor Complex Formation of Morpholine and 90 2,4,6-Trimorpholino-1,3,5-Triazin With Iodine in Different Solutions Tayyebeh Madrakian, Masoumeh Mohammadnejad, Faezeh Hojati 91 Ab initio Calculation of Absolute pKb Value in Aqueous Solution for Nicotine Moradi Robati Gh R., Moradi Sh., Asni Ashari M B
Studies on the Quantitative Relationship Between the Retention Indices of Essential Oils and Their Molecular 92 Structures Mehdi Nekoei, Majid Mohammadhosseini, Farzad Sadeghi
Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) Technique for Quantitative Determination of Acetaminophen in Pharmaceutical Tablets 93 Mohammadreza Khanmohammadi, Hamid Abdollahi, Hossein Nemati1
Simultaneous Spectrophotometric Determination of Lead and Mercury in Waste Water by Least-Squares Support Vector Machine and Partial Least Squares Methods 94 Ali Niazi, Ateesa Yazdanipour, Zahra Ahmari
Prediction of Binding Affinity of Pharmaceutical Compounds Using Different Chemometrics Methods 95 Sasan Sharifi, Ali Niazi, Amir Ezatpanah
Spectrophotometric and Thermodynamic Study of Praseodymium with 4-(2-Pyridylazo) Resorcinol Complex using Chemometrics Methods 96 Ali Niazi, Bahareh Yasar, Mehrana Motiee
A Comparative Study Between PLS, GA-PLS, OSC-PLS and GA-OSC-PLS in the Simultaneous Voltammetric 97 Determination of Antimony and Bismuth: Effect of Variable Selection Ali Niazi, Faezeh Jaberi, Samira Sadeghi, Riccardo Leardi
QSAR/QSPR Study of Toxicity of Nitrobenzene Derivatives and Alcohols by Mechanic Quantum and Structure 98 Descriptor by Chemometrics Methods Sasan Sharifi, Ali Niazi, Fahimeh Rezaei XIX
Quantitative Structure-Activity Relationships (QSAR) Study of Phenol Heterogenic by Orthogonal Descriptor Correction-Partial Least Squares Method 99 Sasan Sharifi, Ali Niazi, Farnaz Samnejad
Simultaneous Spectrophotometric Determination of Cobalt, Copper and NickelUsing 4-(2-thiazolylazo)- resorcinol by Partial Least Squares and Parallel Factor Analysis 100 Ali Niazi, Giti Yamini
Cloud Point Extraction for Pre-concentration and Simultaneous Spectrophotometric Determination of Trace 101 Amounts of Bismuth and Copper by PLS and OSC-PLS Ali Niazi, Kobra Karimi
Orthogonal Signal Correction- Partial Least Squares Method for Simultaneous Spectrophotometric Determination of Cobalt, Copper and Nickel 102 Ali Niazi, Marjan Mehran, Masomeh Asgari
A Novel Quantitative Structure-Property Relationship Model for Prediction of Depletion Percentage of Skin Allergic of Glutathione Compounds: A Combined Data Splitting-Feature Selection Strategy 103 Ali Niazi, Maryam Ghiasi, Mina Montazeri, Shamsi Rafatpanah
Successive Projection Algorithm-Based Wavelength Selection in Multi-component Spectrophotometric Determination by PLS: Application on Copper, Nickel and Zinc Mixture 104 Ali Niazi, Masomeh Asgari, Marjan Mehran
Quantitative Structure Retention Relationship Study of Linear Alkanes and Alkenes using Different 105 Chemometrics Methods Mehrana Motiee, Ali Niazi
Quantitative Structure Retention Relationship Study of Linear Alkanes and Alkenes using Different 106 Chemometrics Methods Mehrana Motiee, Ali Niazi
Principal Component-Wavelet-Neural Network as Multivariate Calibration Method for Simultaneous Spectrophotometric Determination of Folic Acid, Thiamine, Riboflavin and Pyridoxal 107 Ali Niazi, Pegah Saligheh Fard, Jahanbakhsh Ghasem
Extraction and Simultaneous Spectrophotometric Determination of Copper and Cobalt by TAN With Partial Least Squares 108 Ali Niazi, Reza Moradi XX
A Comparative Study Between Least-Squares SupportVector Machine and Partial Least Squares in Simultaneous Spectrophotometric Determination of Cobalt, Cadmium and Nickel 109 Ali Niazi, Samira Sadeghi, Faezeh Jaberi
Spectrophotometric and Thermodynamic Determination of Acidity Constants of Hydroxy Naphthol Blue in Different Solvents by DATAN 110 Ali Niazi, Simin Moradi, Sadaf Mahmoudzadeh 111 Nondestructive Quantitative Analysis of Tomato Fruit Using Raman Spectroscopy and Chemometrics A.M. Nikbakht, R. Malekfar, T. Tavakoli Hashtjin, B. Gobadian, N. Mohammadi
Identification of Binding Mode and Determination of Binding Constant Between DNA and Quinones by 112 Chemimetrics Programs Hossein Peyman, Mohammad Bagher Gholivand, Soheila Kashanian, Hamideh Roshanfekr
Sepctrophotometric Studies of Complexationof Co2+, Cr3+, Ni2+, Pb2+ and Zn2+ With Para-Tert-Butyl Calix[n]arene 113 Amir H. M. Sarafi, Afsaneh Amiri, Fatemeh Pirouzi
Prediction of the Retention Time GC-MS of Organic Compounds Based on Molecular Structural Descriptors Using MLR and Wavelet-Neural Network Methods 114 Z. Garkani-Nejad, H. Rashidi-Nodeh
Comparison of ANN and WT-ANN in Calculatingof Half-Wave Potential of Some Organic Compounds 115 Z. Garkani-Nejad, H. Rashidi-Nodeh
Simultaneous Spectrophotometric Determination of Silicate and Phosphate in Boiler Water of Power Plant andSewage Sample by Partial Least Squares and Simplex Design Methods 116 M. Rohani, S. Dadfarnia, M. A. Haji Shabani, Jahan B. Ghasemi
Design of a New Thallium(I)-Selective Electrode Based on Calix[6]arene using Experimental Design 117 Sayed Yahya Kazemi, Akram Sadat Hamidi
The Components of the Iranian Rosemary Essential Oil Characterized and Identified Using (GC-MS) Combined 118 With the Curve Resolution Techniques Mehdi Jalali – Heravi, Rudabeh–Sadat Moazeni, Hassan Sereshti
Prediction of Retention Factor of Organic Compounds in Different Mobile Phase Compositions in RP-LC by LFER 119 Parameters Seyedeh Maryam Sadeghi, Mohammad Hossein Fatemi XXI
Development and Validation of a Reversed-Phase HPLC Method for Simultaneous Estimation of Carbamazepine 120 and Phenytoin Using an Experimental Design E. Konoz, M.H. Fatemi, H. Baghri sadeghi, Sh. Lashgari
A Comparison of Partial Least Squares Regression and Artificial Neural Networks for Kinetic Spectrophotometric Determination of Selenium and Tellurium Mixture in Alloy Samples 121 Nahid Sarlak, Abbas Afkhami, Ali Reza Zarei
Modeling of Methylene Blue Electroactive Label Signal in Pencil Graphite Based DNA Biosensors M.S. Hejazi , R.E. Sabzi , F. Golabi, B. Sehatnia 122
Simultaneous Determination of Thorium(IV) and Zirconium(IV) Ions Using Partial Least Squares Method Behnaz Shafiee, Hamid Reza Pouretedal 123
Prediction of IAM-LC Retention of Some Drugs From Their Molecular Structure Descriptors and LFER Parameters Hoda Shamseddin, Mohammad Hossein Fatemi 124
A Simple Variable Selection Method Based on the Partial Least Squares Loadings: Application to Quantitative Structure-Activity Relationships Data 125 Masoumeh Hasani, Masoud Shariati-Rad
Investigation of Optimum Extraction Conditions for Determination of Quercetin in Sea Parsnip (Echinophora Spinosa L.) by Using Experimental Design and HPLC. 126 Mohammadreza Hadjmohammadi, Vahid Sharifi
QSAR Study of Substituted Pteridin-4[3H]-One and Dihydroxypyrazolo [1, 5 -α] Pyrimidine Derivatives, Two Novel Classes of Xanthine Oxidase Inhibitors 127 Shahin Salimpour, Reza Tabarak
Application of Orthogonal Array Design for the Optimization of Sample Preparation for Determination of Chromium, Copper, Lead, Iron, Manganese, Molybdenum, Nickel and Zinc in Human Hair by Flame and 128 Electrothermal Atomic Absorption Spectrometry Fariba Tadayon, Mohammad Saber Tehrani, Mahmod. R. Sohrabi, Shiva Motahar
Measurement Uncertainty of Co, Cr, Mo, and Zn Determination in Human Hair by Electrothermal Atomic Absorption Spectrometry 129 F. Tadayon, N. Mashkouri Najafi, M. Saber-Tehrani, A. Ghorbani
Statistical Process Control of Edible Salt Production to Improve Salt Quality at National Standard Level Gholamreza Vatankhah, Nahid Tavakkoli, efat Asghari 130 XXII
Determination of Some Volatile Organic Compound in Honey Samples Using Hollow Fiber- Ultrasound Assisted Emulsification Microextraction (HF-USAEME) Comparative With Conventional Headspace Single Drop Microextractio With the Aid of Response Surface Methodology and Experimental Design 131 Yadollah Yamini, Shahram Seidi, Abolfazl saleh, Mahnaz Ghambarian
Classification of Iranian Bottled Waters as Indicated by Manufacturer’s Labellings K. Yekdeli Kermanshahi, R. Tabaraki 132
Development and Validation of Chemometrics-Assisted Spectrophotometry for Determination of Water Soluble Vitamins in B-Complex Tablets 133 Fereshteh Zandkarimi, Maryam Shekarchi, Ali Akbar Tajali
A Comparison Between LS-SVM and BP-ANN for Simultaneous Spectrophotometric Determination of Some 134 Ingredients in Detergent Powder Mohammadreza Khanmohammadi, Mohammadhossein Ahmadi Azghandi, Nafiseh Khoddami, Amir Bagheri Garmarudi
Application of Artificial Neural Network and Near IR Diffuse Reflectance Spectroscopy for Estimation the Range 135 of Particle Size of Nano-TiO2 Mohammadreza Khanmohammadi, Nafiseh Khoddami, Amir Bagheri Garmarudi
Simulation of Precipitation Titration for Some Cations Using pH Glass Electrode 136 A. Nezhadali; B. Ahmadi
Simultaneous Determination of 2-Nitrophenol and 4-Nitrophenol by Bismuth Modified Pencil Lead Electrode 137 With Net Analyte Signal Standard Addition Method Karim Asadpour-Zeynali, Parvaneh Najafi
Simultaneous Polarographic Determination of Antazoline and Naphazoline by Differential Pulse Polarograhy 138 Method and Support Vector Regression Karim Asadpour-Zeynali, Payam Soheyli-Azaz
Multivariate Curve Resolution of Overlapping Polarograms to the Quantitative Analysis of Metals Mixture 139 Karim Asadpour-Zeynali, Javad Vallipour
Application of Parallel Factor Analysis and Multivariate Curve Resolution-Alternating Least Square for Resolution of Kinetic Data of L-ascorbic Acid Oxidation in Multivitamin Tablets by UV Spectrophotometry 140 Mohammadreza khanmohammadi. Mohammad Babaei Roochi. Nafise khoddami. Zahra Amani XXIII
Application of Experimental Design Methodology to the Optimization of Catalytic Kinetic Determination of Osmium by Janus Green-Hydrogen Peroxide System 141 Hasan Bagheri, Parviz Shahbazikhah, Masoud Reza Shishehbore, Mehdi Nekoei
A New Spectrophotometric Study on the Simultaneous Determination of Benzodiazepines in Plasma employing Multivariate Calibration Methods Combined with Genetic Algorithm on Ordinary and Derivative Spectra 142 Siavash Riahi, Kowsar Bagherzadeh, Mohammad Reza Ganjali, Parviz Norouzi
Rapid Chemometric Method for Simultaneous Determination of Imipramine and Clomipramine in Serum and 143 Validation by HPLC Siavash Riahi, Kowsar Bagherzadeh, Behrouz Akbari-Adergani, Mohammad Reza Ganjali, Parviz Norouzi
Simultaneous Spectrophotometric Determination of Co(II) and Ni(II) Based on the Complexation Reaction With Phenylfluorone Using Partial Least Squares Regression 144 Mohammad Alizadeh, Hamid Daryani, Morteza Bahram, Reza E. Sabzi
Application of Experimental Design Methodology in Optimization and Determination of Trace Amount of Nitrite Using Dispersive Liquid-Liquid Microextraction Followed by Spectrophotometric Detection 145 M. Bahram, M.R Vardast, F. Eshghian, M.A Farajzadeh
Prediction of Receptor Binding Constant of 6-Methoxy Benzamides, Using ANN and MLR 146 Mohammad Hossein Fatemi, Fereshteh Dorostkar
Optimization of Quercetin Nanoparticle Emulsion Preparation Using Experimental Design and Multiple Linear 147 Regression Pouneh Ebrahimi, Fereshteh Pourmorad, Soheila honary, Bahar Ebrahim magham
Comparing Different Subset Selection Methods for Nonlinear Modeling the Acidity Constants of Some Organic 148 Compound in DMSO Gholamhasan Azimi, Sara Ebrahimi, Mohsen Kompany-Zareh, Yousef Akhlaghi
QSPR Studies of Refractive Indices of Polymers by GA-MLR and ANN 149 M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi and Z. Kalantar
Prediction of Aqueous Solubility of Drug-Like Compounds Based on Multilayer Regression and Neural Network 150 Modeling M. Ali Ferdowsi , H. Nikoofard and N. Goudarzi and Z. Kalantar 151 Application of Topological Index in Description of Chemical Properties M.Ali Ferdowsi, H. Nikoofard , N. Goudarzi and Z. Kalantar XXIV
2-Dimensional Quantitative Structure-Property Relationship Modeling Study of Some Organic Compounds Henry's Law Constant Based on GA-MLR and MLR 152 M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi, Z. Kalantar
Application of Response Ssurface Methodology and Central Composite Design for Modeling and Optimization of Hollow Fiber Liquid Phase Microxtraction for Selenium and Tellurium Speciation 153 Nahid Mashkouri Najafi, Ensieh Ghasemi, Farhad Raofie, Alireza Ghassempour 154 Prediction of Retention Indices of Some Essential Oils Using Linear and Nonlinear QSPR Methods Nasser Goudarzi, H. Salimi and M. Arab Chamjangali
A New Method for Simultaneous Spectrophotometric Determination of Psuedoephedrine and Guaifenesin in Pharmacuticals Products: Chemometrics and Derivative Spectroscopy 155 Farshad hadiloo, Siavash riahi, Mohamad reza milani
Application of Box-Behnken Design in the Optimization of Catalytic Behavior of a New Mixed Chelate of Copper (II) Complex in Chemiluminescence Reaction of Luminol 156 Tahereh Khajvand, OmLeila Nazari, Mohammad Javad Chaichi, Hamid Golchoubian
Optimization of Dispersive Liquid Microextraction Based on Ionic Liquid for Preconcentration and Determination of Copper in Water Samples Using Response Surface Methodology and Experimental Design Roohollah khani, Farzaneh Shemirani, Behrooz majidi 157
Classification of Iranian Bottled Mineral Waters Using Chemometrics Methods Mohammad Reza Khoshayand, Hamid Abdollahi, Seyed Mohammad Shariatpanahi, and 158 Hasan Akbari
Development of Comprehensive Descriptors for Multiple Linear Regression and Artificial Neural Network Modeling of Drug Bioavailability 159 E. Konoz, M.H. Fatemi, Sh. Lashgari
Application of Response Surface Methodology (RSM) for Optimization of Carrier Mediated Hollow Fiber Liquid Phase Microextraction Combined With HPLC–UV for Preconcentration and Determination of Dexamethasone in Biological Samples 160 Katayoun Mahdavi Ara, Homeyra Ebrahimzadeh, Shahram Seidi
Application of Response Surface Method for Determination and Preconcentration of Lead Using Dispersive Liquid-Liquid Microextraction Based on Ionic Liquid and Flame Atomic Absorbtion 161 Behrooz majidi, Farzaneh Shemirani, Roohollah khani
Prediction of Voltametric Oxidation of Catecol Derivatives Using DFT Calculation and Linear Regression (LR) Mansouri Ailin, Hokmi Akram, Nematollahi Davood, Jamehbozorghi Saeed 162 XXV
Structure-Property Modelling of Complex Formation of Potassium With Diverse 18-Crown-6 Ethers in Methanol 163 Shahin Ahmadi, Zohreh Mehri
+2 Comparative Studies of Univariate and Multivariate Optimizations for Determination of Drugs by Ru(phen)3 - Ce(IV) Chemiluminescence System 164 A. Mokhtari, B. Rezaei
Simultaneous Spectrophotometric Determination of Copper (II) and Nickel(II) Using Partial Least-Squares Calibration Method 165 Shahla Mozaffari, Maryam Mohammadzadeh
Simultaneous Determination of Cobalt (II) and Zinc (II) by Partial Least-Squares Calibration Method Shahla Mozaffari, Zahra Dini Khezri 166
Simultaneous Spectrophotometric Determination of A.C Red 27 and Methyl Red Using Multivariate Calibration 167 Methods A. Naseri, H. Ayadi, A. Parchehbaf Jadid
Application of Rank Annihilation Factor Analysis (RAFA) to the Quantitative Analysis of Pharmaceutical Samples 168 H. Abdollahi, F. Norooz Yeganeh, M. R. Khoshayand
Simple and Fast QSAR Method for Prediction of HIV-1 PR Inhibitory of Novel Fullerene (C60) Analogues 169 Eslam Pourbasheer, Mohammad Reza Ganjali, Siavash Riahi, Parviz Norouzi
Application of Genetic Algorithm-Support Vector Machine (GA-SVM) for Prediction of BK Channels Activity 170 Eslam Pourbasheer, Mohammad Reza Ganjali, Siavash Riahia, Parviz Norouzi
Quantum Chemical Calculations to Reveal the Relationship Between the Chemical Structure and the Fluorescence Characteristics of Phenylquinolinylethynes and Phenylisoquinolinylethynes Derivatives, and to Predict their Relative Fluorescence Intensity 171 Abolghasem Beheshti, Siavash Riahi, Mohammad Reza Ganjali, Parviz Norouzi
Improving a Drawback in QSPR Study; QSPR Study of Fluorescence Characteristic of Six 4, 7-Disubstituted Benzofurazan Compounds in 20 Different Solvents 172 Abolghasem Beheshti, Siavash Riahi, Mohammad Reza Ganjali, Parviz Norouzi
A Novel Technique by Using a CCD Camera for Kinetic Determination of Iron(III) M Kompany-Zareh, H Tavallali, N Shakernasab 173
The Use of CCD Camera and RGB Model for Kinetic Determination of Vanadium (V) H. Tavalli, M. Kompany Zare, S.E Shamsdin 174 XXVI
Applied Artificial Neural Networks Modeling to Quantitative Structure-Properties Relationship Study of Lipophilicity Activity of Some Long Hydrocarbon Chain Keto-Diols and Their Phosphates Esters and Acids Derivation M.R.Sohrabi, Nasser Goudarzi, F.Hamidi, 175
Voltammetry Determination of Stability Constants of Cadmium Complexes with Diallyl Disulfide by Electroanalytical Technique: Hard and Soft-Modeling Approaches 176 M.A. Kamyabi, F. Soleymani Bonuti
Modeling and Optimization of Dispersive Liquid-Liquid Microextraction for Speciationof Tellurium with the Aid of Response Surface Methodology and Experimental Design Nahid Mashkouri Najafi, Hamed Tavakoli, Reza alizade, Shahram seidi 177 Response Surface Methodology (RSM) Based on BoxBehnken Design as a Chemometric Tool for Optimization of Dispersive-Solidificative Solvent Microextraction for Speciation of Selenium Nahid Mashkouri Najafi, Hamed Tavakoli, Reza alizadeh 178 Prediction of Retention of LC-MS Pesticides in Water Using QSRR Approach Amir H. M. Sarrafi, Fateme Yaghoobi 179
Factorial Analysis and Response Surface Optimization of a Peroxyoxalate Chemiluminescence of Trazinyl Derivative in the Presence and Absence of Some Surfactants A. Yeganeh-faal, T. H. Shayeste , J. Ghasemi, M. Bordbar 180
Super Modified Simplex Optimization Chemiluminescence from Reaction of Peroxyoxalate Ester (TCPO), Hydrogen Peroxide and tetraazapentacyclo Derivative as Fluorescer and Study Quenching Effect of Some Cations and Amino Acids on Optimized Chemiluminescence System. 181 A. Yeganeh-faal, B. Jamalian, J. Ghasemi, M. Salavati
Determination of Dissociasion Constant of Preotonated Form a Triazin Derivative Dye by Spectrophotometric and Spectroflourimetric Method: A Study Chemometrics approach 182 A. Yeganeh-faal, G. Dabaghian, M. Haggo, M. Bordbar
Determination of Dissociasion Constant of Preotonated Form a Triazin Derivative Dye by Spectrophotometric and Spectroflourimetric Method: A Study Chemometrics approach Determination of Main Factors in Silane Grafting of Linear Low Density Polyethylene Using Experimental Design E.Konoz, M.H.Fatemi, E.Zamani Farahani 183
Prediction of Inhibitor Activity of 1,3,4-Thiadiazole-2-Thion Derivative to Carbonic Anhydrase by QSAR Methodology Using Genetic Algorithm-Artificial Neural Network Technique Mehdi Mousavi, Solmaz Ahmadgolami 184 XXVII
Prediction of Inhibitor Activity of Amino-Caprolactam Derivatives to Y-secretase by QSAR Methodology Using MLR and Artificial Neural Network Mehdi Mousavi, Solmaz Ahmadgolami 185
An Improved HPLC Method for Rapid Quntitation of Atorvastatin Using an Experimental Design E. Konoz, M.H. Fatemi, S. Ardalani 186 Artificial Neural Network Modeling of the Blood-Brain Penetration Coefficient of Drugs E. Konoz, M.H. Fatemi, S. Ardalani 187 Predictive Ability of Multivariate Calibration Methods for Simultaneous Quantification of Tebaine and +2 Noscapine Using Chemiluminescence System of Ru(phen)3 and Acidic Ce(IV) A. Mokhtari, B. Rezaei 188 Hard-Modeling Approach for the Thermodynamic and Spectroscopic Studies of Cu(II), Ni(II), Co(II) and Zn(II) Complexes With Two Newly Synthesized Ligands in Acetonitrile Solution Nasser Samadi, Mina Salamati, Morteza Bahram, Ali Soldouzi 189 Modeling of Decolorization of Allura Red solutions Using Response Surface Methodology E. Ghorbani–Kalhor, A. Naseri, Soheila Mohammadian 190 Modeling and Optimization of Simultaneous Decolorization of A.C Red 27 (AR 27) and Methyl Red (MR) Dyes H. Ayadi, A. Naseri 191 Simultaneous Determination of Trimetoprim and Phthalazine Using HPLC and Multivariate Calibration Methods A. Naseri, S. Asadi, M. R. Rashidi 192
Application of Artificial Neural Network and Wavelet Neural Network in Simultaneous Determination of Iodine Species by Kinetic Spectrophotometry A. Benvidi, F. Heidari 193
Applied Artificial Neural Networks Modeling to Uantitative Structure-Properties Relationship Study of Lipophilicity Activity of Some Long Hydrocarbon Chain Keto-Diols and Their Phosphates Esters and Acides Derivatives 194 M.R.Sohrabi, Nasser Goudarzi, F.Hamidi Taguchi's Experimental Design for Optimization of Effective Parameters on Diazinon by Cloud Point Extraction Sarah Jamshidi, Mahmud Reza Sohrabi, Vahid Kiarostami 195 A Simple and Cheap Double-Beam Photocolorimeter Fabricated for Simultaneous Determination of Binary and Ternary Mixtures 196 Mohammad-Hossein, Sorouraddin, Masoud Saadati INVITED LECTURES es es es Invited Invited Invited Lectur Lectur Lectur
INVITED LECTURES
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ORAL
Resolving Factor Analysis Using Chaotic Particle Swarm Optimization
Hamid Abdollahi, Samira Beyramy soltan Institute for Advanced Studies in Basic Science (IASBS)
Resolving factor analysis is one of the soft modeling methods that its task defined as finding the one set T for which the products C=UT and A=T-1SVt are physically correct. C is concentration profile and A is the spectral profile which satisfy the D=CA. In RFA, rotated PCA solutions are modified iteratively to fulfill the constraints and the perturbed solutions are then used to calculate the residuals of the least squares function to be minimized by a non-linear optimization procedure; Non-linear optimization was performed by Newton–Gauss-Levenberg/Marquardt algorithm [1]. Chaotic particle swarm optimization method is optimization approach based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CPSO) where the parallel population-based evolutionary searching ability of PSO and chaotic searching behavior are reasonably combined [2]. In the present work, chaotic particle swarm optimization (CPSO) combined with RFA is introduced as self-modeling curve resolution (SMCR) method, and also is recommended as the method for avoiding divergence problems in RFA. In RFA, if there is not unique solution, the nonlinear least square does not converge even in two component systems. The proposed method enables to solve this problem due to advantage of CPSO. To investigate the performance of the method, chromatograms of varying noise level, and overlap were generated and subsequently analysed, and to demonstrate its potential, this method applied to three and four component real datasets. The results show that RFA using CPSO is robust under conditions that traditional RFA fails and converges without difficulty. Furthermore unlike traditional SMCR, convergence is achieved even with random initial estimates; this method enables to resolve datasets with lesser of five components. To the best of our knowledge, it is the first report of applying CPSO to optimize transformation matrix T.
References:
1) Mason CJ, Maeder M, Whtson A. Resolving Factor Analysis. Anal. Chem. 2001; 73; 1587-1594. 2) Liu Bo, Wang Ling, Jin Yi Hui, Tang Fang, Huang De Xian. Improved Particle Swarm Optimization Combined With Chaos. Chaos, Solitons Fractals 2005; 25(5); 1261-1271. 3) Eberhart Russel C, Kennedy James. Particle Swarm Optimization. IEEE Int Conf Neural Networks 1995; 4; 1942-1947. 4) Shinzawa H, Jiang J-H, Iwahashi M, Noda I, Ozaki Y. Self-modeling Curve Resolution (SMCR) by Particle Swarm Optimization(PSO). Analytica Chimica Acta 2007; 595; 275-281. 11 ORAL
Uncertainties and Error Propagation in Kinetic and Equilibrium Hard-Modelling of Spectroscopic and pH-Metric Data
Hamid Abodollahi, Parvin Darabi Institute for Advanced Studies in Basic Science (IASBS)
Quantitative studies play a dominant role in analytical chemistry. Thereby, the errors that occur in such studies are of supreme importance. Thus, a key principle will be that, no quantitative results are of any value unless they are accompanied by some estimate of the errors inherent in them. This principle naturally applies not only to analytical chemistry but to any field of study in which numerical experimental results are obtained [1]. Yet with modern and highly reliable probes, certain more conventional sources of error such as sampling or instrumental noise are much less serious than problems with estimation of initial concentrations. In a real laboratory practice, because of problems due to weighing, dissolution, imperfect mixing and so on, there is some uncertainty as to the true concentrations of reactants at the beginning of a reaction. Thus, chemists often do not accurately know these [2]. In the present work, the impact of uncertainties in the initial concentrations on the error of fitted equilibrium and rate constants, for spectroscopic and pH-metric studies of acid-base and complexation equilibria and also spectroscopic study of coupled kinetic- equilibrium systems were investigated, for the first time. For this, a rigorous approach based on classical error propagation was used. The performance of the method has been evaluated by using synthetic data sets. Multivariate data were analysed by model- based fitting using the Newton-Gauss-Levenberg/Marquardt optimization algorithm. Then, for each of simulated systems, the effects of different initial concentrations and different equilibrium constants on output of algorithm (error of fitted parameters) were investigated by variation of them in the reasonable ranges. Furthermore, spectroscopic and pH-metric methods for studying complex formation and acid-base equilibria were compared in the same conditions. The results of pH-metric method were more precise than spectroscopic method. The important consequence of this study is that, our findings have an immediate application in the optimum experimental design of these processes. This method of error propagation is flexible and straightforwardly extended to propagate other sources of error.
References:
1) J. N. Miller, J. C. Miller, "Statistical and Chemometrics for Analytical Chemistry", Fourth Edition, Prentice Hall, 2000. 2) A. R. Carvalho, R. G. Brereton, T. J. Thurston, R. E. A. Escott, Chemom. Int. Lab. Syst. 71 (2004) 47. 3) J. Billeter, Y. M. Neuhold, L. Simon, G. Puxty, K. Hungerbühler, Chemom. Int. Lab. Syst. 93 (2008) 120. 12 ORAL
Application of Multivariate Curve Resolution based on Alternative Least Square assisted with Trilinearity Constraint (TC-MCR-ALS) for Resolution of Multi-Way Rank Deficient Systems
Mohsen Kompany-Zareh, Fatemeh Ghasemi-Moghadam Institute for Advanced Studies in Basic Sciences (IASBS), GavaZang, Zanjan Iran
Multivariate curve resolution based on alternative least square assisted with trilinearity constraint (TC-MCR-ALS) has the ability to resolve the full rank trilinear data, with results similar to PARAFAC [1]. PARAFAC is not a proper resolution method when dealing with rank deficient data. A proper alternative resiolution method in the presence of rank deficiency is Tucker3. In this study, the ability of TC-MCR-ALS for resolution of three-way rank deficient data was investigated. Unfolded data, to maximum rank, was resolved by TC-MCR-ALS to matrices Z and C. Z matrix contained the information in two modes (matrices A and B) of data. With application of trilinearity constraint not only rotation ambiguity was decreased but also matrices A and B were extracted from matrix Z [2, 3]. This method was successfully applied on any kind of simulted data with rank deficiency in one or two modes. To study the merit of TC-MCR-ALS in resolution of the data with rank deficiency in all three modes, both simulated and experimental data were examined. Three-way excitation-emission spectrofluorimetric data from solutions containing different concentrations of analytes; catechol, hydroquinone, indole and tryptophane was considered emperical data. Chemical rank of this data was estimated using two mode comparison subspace algorithm [4]. Maximum estimated rank of data in all three modes, was three, although four components were present in the system. In the three-way data with rank deficiency in all three modes, a number of columns in matrix Z were not trilinear, theoricaly, but TC-MCR-ALS performed well. It was due to possibility of rotation of Z to a trilinear combination of Z columns. Therefore TC-MCR-ALS performs as well as Tucker3 for many kinds of rank deficient data. The method resolves a data with ranks 4, 3, 2 in three modes into four cubes with rank 1, but Tucker3 resolves it to less than 24 (4x3x2=24) arrays with rank 1. Then the solution and interpretation of TC-MCR-ALS is simpler than Tucker3.
References:
1) E. Pere-Trepat, A. Ginebreda, R. Tauler, Chem. Int. Lab. Syst., 88 (2007) 69-83. 2) R. Tauler, I. Marques, E. Casassas, J. Chemom, 12 (1998) 55-75. 3) E. Bezemer, S.C. Rutan, Chemom. Int. Lab. Syst., 81 (2006) 82-93. 4) H.P. Xie, J.H. Jiang, N. Long, G.L. Shen, H.L. Wu, R.Q. Yu, Chem. Int. Lab. Syst., 66 (2003) 101-115. 13 ORAL
Classification of Drugs by Means of Their Milk/Plasma Concentration Ratio Using Supervised Chemometric Procedures
M.H Fatemi*, M. Ghorbanzad'e, E. Baher Faculty of Chemistry, Mazandaran University, Babolsar, Iran
Development of reliable computational models to classify drugs based on their milk to plasma (M/P) concentration ratio is a challenging object. Support vector machine (SVM) and counter propagation artificial neural network (CPANN) were applied to distinguish the potential risk of drugs in this work. The features of each drug were encoded by five LFER descriptors including: the solute excess molar refractivity (E), the solute dipolarity/polarizability (S), the McGowan volume (V) and overall hydrogen bond acidity (A) and basicity (B). These descriptors were used as inputs of SVM and CPANN to classify drugs as high risk (with M/P > 0.1) and low risk (with M/P < 0.1) drugs for lactating women. The classification accuracy of training set, internal and external test sets for SVM was 91.12%, 90.00% and 80.00%, respectively. Also, the classification accuracy of training, internal and external test sets for CPANN was 100.00%, 100.00% and 90.00%, respectively. The total accuracy for SVM and CPANN models in classification of drugs was 90.25% and 99.35%, respectively. Comparison of the two methods shows that the performance of CPANN was better than that of SVM, which implies that the CPANN method is more precise tool in evaluating the risk of drugs. It was concluded that these models can be used for in silico prediction of new, not yet investigated drug risk for lactating woman.
References:
1) Todeschini R and Consonni V (2000) Handbook of molecular descriptors, Wiley-VCH. 2) Zupan J, Novic M and Ruisanchez I, Chemom. Intell. Lab. Sys. 38, 1-23 (1997)
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Application of Successive Projections Algorithm (SPA) as a Variable Selection in a QSPR Study to Predict of the Octanol/Water Partition Coefficients (Kow) of Some Halogenated Organic Compounds
Mohammad Goodarzi1,3, Nasser Goudarzi2 1- Department of Chemistry, Faculty of Sciences, Azad University, Arak, Iran, 2- Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran, 3- Young Researchers Club, Azad University, Arak, Iran
The successive projections algorithm (SPA) is a variable selection method that has been compared with genetic algorithm (GA) due to its ability in solving the descriptor selection problems in QSPR model development. For model development, the popular linear algorithm Partial Least Squares (PLS) was employed to build the model. These methods were used for the prediction of octanol/water partition coefficients Kow of 10 kinds of selected halogen benzoic acids. The root means square error of prediction (RMSEP) for training and prediction sets by GA-PLS and SPA-PLS models were 0.26, 0.28, 0.13 and 0.16, respectively. Also, the relative standard error of prediction (RSEP) for training and prediction sets by GA-PLS and SPA-PLS models were 8.02, 3.92, 8.68 and 4.98 respectively. The resultant data showed that SPA-PLS produced better results than GA-PLS in these class compounds.
Keywords: QSPR, Octanol-water partition coefficients, SPA-PLS, GA-PLS
References:
1) Nasser Goudarzi, Mohammad Goodarzi; Mario. C. U. Araujo, R. K. H. GALVA ; J. Agric. Food Chem. 2009, 57, 7153–7158 2) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2008, 106, 2525–2535 3) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2009, 107, 1615–1620
15 ORAL
Second-Order Advantage From Micelle Concentration Gradual Change–Visible Spectra Data
Hamid Abdollahi*1, Mahmoud Chamsaz2, Tahereh Heidari2 1- Department of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran 2- Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
Second-order calibration is used for second-order data. Such data is produced by instruments that give a matrix of responses for a single measured standard or unknown sample. This allows for determination of analyte of interest in the presence of uncalibrated sample constituents, a property known as the second-order advantage [1]. Malachite green has found extensive use all over the world in the fish farming industry as a fungicide, ectoparasiticide and disinfectant [2].This dye has also been used extensively for dyeing silk, wool, jute, leather and cotton [3].A similar situation is valid for crystal violet, which is used to control fungi and intestinal parasites in humans, as an antimicrobial agent on burn victims, to treat umbilical cords of infants, for the treatment of long-term vaginal candidosis, for various purposes in veterinary medicine, etc.[4]. It has been shown recently that some members of this group of compounds are linked to an increased risk of cancer and also act as liver tumor-enhancing agent. It was discovered that a second order spectra data matrix of malachite green and crystal violet produced from the micelle (of triton X-100 surfactant) concentration gradual change–visible absorption spectra can be expressed as the combination of two bilinear data matrices. Based on this discovery, a new method for the determination of malachite green and crystal violet in black systems using second order calibration algorithms has been developed. The second order calibration algorithms were based on the rank annihilation factor analysis (RAFA), un folded partial least-squares/residual bilinearisation (U-PLS/RBL)[5] and bilinear least squares/residual bilinearisation (BLLS/RBL)[6]. In the method described here, the concentration of the surfactant (sufficiently beyond the critical micelle concentration) was changed gradually and the absorption spectra of samples were recorded. Thus, the concentration of malachite green and crystal violet in black system could be determined from the spectra matrices using second order calibration algorithms. This method is simple, convenient and dependable. The method has been used to determine malachite green and crystal violet in simulated textile dye effluent, goldfish farming water and waste of nutrient broth-grown cell with satisfactory results.
References: 1) Smilde AK, Tauler R, J and Bro R Anal Chim Acta 1999:398: 237–251. 2) Alderman DJ. Malachite green: a review. J Fish Dis 1985;8:289–98. 3) Culp SJ, Beland FA. Malachite green: a toxicological review. J Am College Toxicol 1996;15:219–38. 4) Rushing LG, Bowman MC. J Chromatogr Sci 1980;18:224–32. 5) Olivieri AC. J Chemometrics 2005: 19:253-265. 6) Linder M, Sundberg, R Chemom. Intell Lab Syst 1998: 42: 159-165. 16 ORAL
Partial Swarm Optimization Approach for Training of an Artificial Neural Network Applied in Thermal Investigation of Nanocomposites
Mohammadreza Khanmohammadi*1, Nafiseh Khoddami1, Mohammad Hossein Ahmadi Azghandi1, Amir Bagheri Garmarudi1,2, Masumeh Foroutan2, Mahdieh Ansaryan2 1- Chemistry Department, Faculty of Science, IKIU, Qazvin, Iran 2- School of Chemistry, University College of Science, University of Tehran, Tehran, Iran
Artificial neural Network (ANN) has become most common for modern data processing. It is able to solve numerous complex problems and has well known advantages like possibility of learning from examples, generalization ability, parallel computation, nonlinear mapping nature, etc [1]. Most applications use feed forward ANNs which use the standard back-propagation (BP) learning algorithm or some improved BPs [2] but some intrinsic problems do frequently exist in application of this algorithm, such as very slow convergence speed in training, get stuck easily in a local minimum especially in problem domains with high dimensionality and also it needs to predetermine some important learning parameters such as learning rate, momentum and structure [3,4]. Accordingly, a new ANN model based on partial swarm optimization algorithm has been introduced which has these defects less than BP-ANN and also gives more accurate (in terms of sum square error) and faster (in terms of number of iterations and simulation time) results than BP-ANN [1]. PSO is a population based stochastic optimization technique, inspired by social behavior of bird flocking or fish schooling. It has been proved to be a competitor to GA when it comes to optimization of problems. PSO algorithm was used to train a multi-layer feed forward ANN for investigation of the kinetic parameters in thermal degradation of nanocomposite samples based on polyimide and silica nano particles, using thermogravimetry analysis (TGA). Different heating rates in TGA were applied. The adoption of a PSO model to train the perceptrons in prediction of kinetic parameters is presented. The obtained results illustrated that the successful prediction can be achieved by PSO trained ANN. Moreover, it is capable of producing faster and more accurate results than its counterparts of a benchmarking back-propagation ANN.
References:
1) M. Geethanjali, S. Mary Raja Slochanal, R. Bhavani, Neurocomp. 71 (2008) 904–918 2) Yu Jianbo, Xi Lifeng, Wang Shijin, Neural. Process. Lett. 26 (2007) 217–231. 3) K.W. Chau, C.T. Cheng, Lect. Not. Artif. Intell. 2557 (2002) 715–715. 4) R. Govindaraju, A. Rao, Artificial Neural Networks in Hydrology, Kluwer Academic Publishers, Dordrecht, 2000.
17 ORAL
Application of Standardization Methods in Simple Kinetic and Equilibrium Studies
Mohsen Kompany-Zareh, Maryam Khoshkam Institute for Advanced Studies in Basic Sciences (IASBS),GavaZang, Zanjan, Iran.
Hard model based and soft resolution approaches are useful tools for estimation of concentration and spectral profiles in kinetic or equilibrium systems [1]. Both resolution methods can be applied to the analysis of an individual and the augmented data matrices [2]. Simultaneous analysis of multiple process runs under linearly independent conditions is proposed to break rank deficiency in the data. Presence of more information in the augmented data results in less rotational and intensity ambiguities in the resolved profiles [2, 3]. Assumption in dealing with augmented data matrices is that the pure spectra of absorbing species in column-wise augmentation are the same in all data matrices [4]. In many conditions spectral profiles between the augmented data matrices are not the same and the resulting profiles and parameters from the augmented data would not be reliable [4, 5]. Standardization is a popular technique to solve such problems in multivariate calibration systems, by standardization of calibration and test data sets into same space [6]. The most feasible approach for the problem is judged to be methods developed under the premises of having measured the same samples on either instruments or conditions [6, 7]. In this study, we apply the standardization methods for first order kinetic and simple equilibrium systems. To our knowledge this is the first application of standardization method in kinetic and equilibrium studies. The method is tested in simulated and experimental data and the obtained results showed that in presence of spectral variation in different conditions, by applying standardization methods, better fit and more reliable parameters can be obtained. By standardizing of data, the obtained parameters were improved for both hard and soft methods.
References:
1) M. Maeder, Y. M. Neuhold, "Practical Data Analysis in Chemistry", Newcastle, Australia, September, 2006. 2) J. Saurina, S. Herna´ Ndez-Cassou, R. Tauler, A. IZquierdo-Ridorsa, J. Chemometrics, 12, 183–203 (1998) 3) R. Tauler, A. Smilde and B. R. Kowalski, J. Chemometrics, 9, 31–58 (1995). 4) D. B. Gil, A. M. Pen, A. A. Juan, G. M. Escandar, A. C. Olivieri, Anal. Chem., 78, 8051-8058 (2006). 5) S.D. Brown, "Comprehensive Chemometris", Chap. 3.08, 345-378 (2009) 6) "Notes on calibration of instruments ", June 2002. 7) R. N. Feudale, N. A. Woody, H. Tan, A. J. Myles, S. D. Brown, J. Ferre, Chemom. Intell. Lab. Syst., 64, 181– 192 (2002).
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Random Forests, a Novel Approach for Prediction of the Acute Toxicity of Substituted Benzenes to Tetrahymena Pyriformis
Anahita Kyani Department of Chemistry, Tarbiat Modares University, Tehran, Iran
Random forests (RF) is an ensemble of unpruned classification trees created by using bootstrap samples of the training data and random subsets of variables to define the best split at each node [1]. Prediction is made by the average of the individual tree predictions. RF offers some unique features that make it suitable for QSAR tasks. These features include estimation of prediction accuracy, measures of descriptor importance, and a measure of similarity between molecules. This method is extremely accurate in a variety of applications [2]. In the present work, random forests (RF) was employed as a novel approach for the prediction of toxicity of a diverse data set consisted of 264 substituated benzene compounds such as phenols, nitrobenzenes, benzonitriles, carboxyl acids, amides, amines and aldehydes toward Tetrahymena pyriformis [3]. The most important variables were determined by the decrease in a node's impurity every time the variable is used for splitting. Among a large number of simple zero-, one- and two-dimensional descriptors, parameters concern with hydrophobicity and electronic interactions were revealed as the important ones.
2 Satisfactory results (ErrorOOB= 0.125 and R = 0.865) indicate that the RF is able to model pIC50 of a diverse chemical class of compounds with more than one mechanism of toxicity using simple and interpretable descriptors. Random forests exhibited interesting features not only in terms of prediction accuracy but also by providing meaningful probabilities for the predictions.
References:
1) Zhang, Q.U.; Aires-de-Sousa, J. O.; Random forest prediction of mutagenicity from empirical physicochemical descriptors. J. Chem. Inf. Model. 2007, 47, 1. 2) Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P.; Random forest: A classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947. 3) Burden, F. R.; Winkler, D. A.; A quantitative structure-activity relationship model for the acute toxicity of substituated benzens to Tetrahymena Pyriformis using Bayesian-regularized neural networks. Chem, Res, Toxicol. 2000, 13, 430.
19 ORAL
Application of Bayesian Adaptive Regression Splines for QSAR Modeling of Glutamate Inhibitors
Mehdi Jalali-Heravi*, Ahmad Mani-Varnosfaderani Department of Chemistry, Sharif University of Technology, Tehran, Iran
The present work deals with application of Bayesian adaptive regression splines (BARS) for quantitative structure-activity relationship (QSAR) study of 85 drug-like glutamate antagonists [1-3]. The BARS method is a powerful nonparametric regression technique and uses a reversible jump Markov-Chain-Monte-Carlo (MCMC) engine to perform spline-based non-parametric regressions. In order to compare BARS and other linear and non-linear modeling techniques, the modeling was also performed by using Bayesian regularized genetic neural networks (BRGNNs), genetic algorithms partial least squares (GA-PLS) and genetic algorithms multiple linear regression (GA-MLR). The obtained results for RMSEtest revealed that BARS is better than GA-PLS and GA-MLR for the modeling but the results of BRGNNs were superior to BARS. Although BRGNNs can model the data better than BARS, but its interpretation is difficult, on the other hand BARS is not a black box and it produces some basis functions for the modeling. These basis functions make BARS more interpretable than the BRGNNs and produce visually-appealing fits that are smooth while adapting to sudden changes. Finally BRGNNs was repeated fifty times and the six variables with higher frequencies were selected as input vectors for BARS. The results of BRGNNs-BARS were compared with those obtained by using simple multivariate adaptive regression splines (MARS) [4]. The values of correlation coefficient for training (0.894) and validation (0.821) revealed that BRGNNs-BARS is able to construct a model which is superior over MARS. It implies that the combination of BRGNNs method as a non-linear variable selection technique and MCMC engine as a powerful knot point selection is better than forward selection and backward deletion strategy in MARS algorithm. The proposed BRGNNs-BARS in the present contribution is a suitable method for finding the variables and the knot points in data sets with nonlinearities and interactions.
References:
1) T. J. Woltering, G. Adam, A. Alanine, J. Wichmann, Bioorg. Med. Chem. Lett. 2007, 17, 6811-6815. 2) T. J. Woltering, G. Adam, J. Wichmann, E. Geotschi, Bioorg. Med. Chem. Lett. 2008, 18, 1091-1095. 3) T. J. Wolterinn, J. Wichmann, E. Geotschini, G. Adam, Bioorg. Med. Chem. Lett. 2008, 18, 2725-2729. 4) J. H. Friedman, Ann. Stat. 1991, 19, 1-141.
20 ORAL
Simultaneous Spectrophotometric Determination of 2-Furaldehyde and 5-Hydroxymethyl-2-Furaldehyde by Using Ant Colony Algorithm-Based Wavelength Selection-Partial Least Squares Regression
M. Shamsipur*1, A.A. Miran Beigi1,2, V. Zare-Shahabadi3, M. Teymouri2, S. Ghahremani2 1- Department of Chemistry, Faculty of Science, Razi University, Kermanshah, Iran 2- Oil Refinery Research Division, Research Institute of Petroleum Industry, Tehran, Iran 3- Department of Chemistry, Faculty of Science, Shiraz University, Shiraz, Iran
Ant colony optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. The solution process is stochastic and is biased by a pheromone model, which is used to probabilistically sample the search space. ACO is a relatively novel technique for solving hard combinatorial optimization problems. The inspiring source of ACO is the foraging behavior of real ants. Since wavelength selection is a strategy used for improving the quality of calibration methods, we investigated simultaneous determination of two furaldehydes, 2-furaldehyde (F) and 5-hydroxymethyl- 2-furaldehyde (HMF), using ant colony algorithm-partial least squares (ACA-PLS) regression. Predictive abilities of ACO in wavelength selection process were examined for spectrophotometric analysis of these species, and were compared with other regression methods, such as MLR, PCR, PLS, and GA-PLS. The ACA-PLS shows superiority over other methods due to the wavelength selection in PLS Calibration using an ant colony algorithm without loss of prediction capacity provides useful information about the chemical system. The proposed method was also successfully when applied to determination of furaldehydes in oil refinery waste waters.
Keywords:2-furaldehyde, 5-hydroxymethyl-2-furaldehyde, aging markers, ant colony optimization, partial least squares regression, petroleum refining, wastewaters
References: 1) N. Spano, M. Ciulu, I. Floris, A. Panzanelli, M. I. Pilo, P. C. Piu, S. Salis, G. Sanna, Talanta, In Press, Accepted Manuscript, Available online 24 November 2008. 2) S. M. Borghei, S. N. Hosseini, Chem. Eng. J. 139 (2008) 482. 3) J. C. Calderón, E. G. Hernández, B. G. Villanova, Europ. Food Res. and Technol. 227 (2008) 117. 4) D. S. J. Stan Jones and P. R. Pujadó, Handbook of petroleum processing, "Environmental control and engineering in petroleum refining", Springer Netherlands, October 04, 2006, p.p. 611-673. 5) B. Coto, R. van Grieken, J. L. Peña, J. J. Espada, Chem. Eng. Sci. 61 (2006) 8028. 6) J. A. R. Henares, C. D. Andrade, F. J. Morales, Article in Press, Corrected roof, Food Chem. 2008.
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Theoretical Study of Inhibition Effect of Some Imidazole Derivatives on Mild Steel
Mehdi Mousavi, Mohammad Mohammadalizadeh Department of Chemistry, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran.
Corrosion is electrochemical processes by which the metallic structures are destroy gradually through anodic dissolution [1]. Therefore, various attempts must be employed to prevent or retard this destructive process. Among methods under taken, application of inhibitors is one of the most practical and economical ones. Theoretical methods based on quantum chemical calculations have been proposed which can calculate some molecular parameters directly related to the corrosion inhibition behavior of the chemical compounds. Thus, after the calculation of theses parameters, a correlation between those quantities and the corrosion behavior of similar chemical compounds can be made.
In the present study, inhibition effect of imidazole derivatives on mild steel was modeled. Here, DEinteraction of inhibitors and metal was considered as analytical parameter for the modeling. In the first step of the calculation, 3D geometry of seven inhibitors were optimized in gas phase using B3LYP/6-31++ (d,p) basis set in DFT method [2]. In the second step, the effect of salvation on conformation was considered for both the inhibitors and the metal. In the third step, electronic energy of the inhibitor and the metal was calculated in the solvent phase. Finally, the interaction energy was calculated and related to the experimental inhibition effect of the inhibitors. The calculated values of interaction energy are in good agreement with the experimental inhibition effect of the inhibitors (R=0.951). According to the calculation, it is concluded that this theoretical method is a useful means for proposing new inhibitors.
References:
1) H.H.Uhling, R. W. Revie, Corrosion and Corrosion control, third ed., Wily, New York, 1985,pp.1-5. 2) S.G. Zhang, W. Lei, M.Z. Xia, F.Y. Wang, Journal of Molecular Structure:THEOCHEM 732 (2005) 173-182
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Mean Field Independent Component Analysis (MF-ICA) as a Self-Modeling Curve Resolution (SMCR) Technique
Mehdi Jalali-Heravi*, Hadi Parastar Department of Chemistry, Sharif University of Technology, Tehran, Iran
Mean field independent component analysis (MF-ICA) [1,2], also known as blind source separation (BSS), is a statistical method. The goal of this method is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Therefore, MF-ICA has a potential to be as a self-modeling curve resolution technique (SMCR) for resolving the mixed signals to the contribution of pure components. In MF-ICA, some parameters on posterior distribution and nonnegative constraint are introduced to give a prior knowledge on the distribution of source signals, e.g., setting the source signal to be “positive”. Optimization of the effective parameters of MF-ICA algorithm is very important. In the present contribution, first of all the effective parameters of MF-ICA algorithm were optimized using design of experiment techniques and then the optimized algorithm was used for the resolution of the co-eluted GC-MS peaks. Full factorial design (FFD) (25) was used for this purpose. The ratio of R2 to lack of fit (LOF) for the sources was chosen as response for the simulated GC-MS data. Analysis of variances (ANOVA) and normal probability plots [3] showed that three factors of mixing matrix prior, noise covariance prior, and type of the method are the most important parameters. The optimum conditions for MF-ICA algorithm was followed by deconvolution of complex GC-MS peak clusters. The number of independent components (ICs) in each peak cluster was estimated using the method of morphological score [4]. Because of local minimization problem in MF-ICA, concentration profiles of evolving factor analysis (EFA) and orthogonal projection approach (OPA) were used as initial mixing matrix in iterative process. Two experimental GC/MS data of Iranian rose and saffron are analyzed with the optimized MF-ICA algorithm. Results show that both mass spectra and chromatographic profiles of the components in the GC-MS signal are accurately extracted.
References:
1) L.D. Lathauwer, B.D. Moor, J. Vandewalle, J. Chemom. 14 (2000) 123. 2) P. A. Hojen-Sorensen, O. Winther, L. K. Hansen, Neur. Comput. 14 (2002) 889. 3) E. Morgan, Chemometrics: Experimental Design, Wiley, London, 1991. 4) H. Shen, Y.Z. Liang, O.M. Kvalheim, R. Manne, Chemom. Intell. Lab. Syst. 51 (2000) 49.
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Application of Multiple Regression Systems in Mixture Analysis Using Non-Selective Spectral Data
Hamid. Abdollahi, Akram. Rostami Department of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
Spectrophotometric data often comprise more variables (spectral data or features) than observations (spectra or samples). Variable collinearity is typical in spectrophotometric data, that is, some variables can be represented as a linear combination of other (independent) variables. This is a source of problems for a direct application of many statistical regression methods, such as the multiple linear regression (MLR) [1-4]. Studies have shown that the presence of collinearity can result in unsatisfactory predictions [5]. Different works have been presented in the literature to deal with the problem of data dimensionality and redundancy [6]. Most of them rely on the idea to reduce the number of variables by means of a variable (feature) selection strategy, allowing thus to shrink the original spectral data space into a subspace of smaller dimension where the curse of dimensionality phenomenon hopefully disappears and therefore the risk of affecting negatively the adopted regression method becomes negligible. In this study, we used an approach for, mixture analysis in chemical systems without selective spectral regions by using indicator displacement assay behavior through spectrophotometric measurements. It is based on the exploitation of the whole spectral information available in the original spectral data space by means of a Multiple Regression System (MRS) whose design is performed in three successive steps. The first one aims at a simple partitioning of the original spectral data space into subspaces of reduced dimensionality. The second step consists in training a (linear or nonlinear) regression method in each of the subspaces obtained in the previous step. In the third and final step, the estimates provided by the ensemble of regressors are combined in order to produce a global estimate of the concentration of the chemical component of interest. For this purpose, one linear and one nonlinear combination strategies are explored [7]. The mentioned strategy is used to simultaneous determination of zinc and cadmium based on their interactions by common indicator (PAR) by combination of the different equilibrium conditions in synthetics and real samples, successfully.
References: 1) D. Belsley, E. Kuh, R. Welsch, Wiley, New York, 1980. 2) T. Eklöv, P. Mårtensson, I, Anal. Chim. Acta 381 (1999) 221-232. 3) P. Geladi, , Chemometrics and Intelligent Laboratory Systems, 60 (2002) 211-224. 4) H. Martens, T. Næs, Wiley, New York, NY, 1991. 5) J. M. Sutter, J. H. Kalivas, Microchem. J. 47 (1993) 60. 6) M. Verleysen, ISO Press, (2003)141-162. 7) N. Benoudjit1, F. Melgani2, H. Bouzgou1, Chemometrics and intelligent laboratory systems, 95(2009), 144-149 24 ORAL
New QSPR Model for Aqueous Solubility Prediction of Drugs
Ali Shayanfar1, Abolghasem Jouyban2 1- Faculty of Pharmacy and Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran. 2- Faculty of Pharmacy and Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Aqueous solubility of a drug/ drug candidate is essential data in drug discovery because poor soluble drugs have low absorption and are failed in this process. In addition, aqueous solubility in drug development is important for oral or parental drug formulation. Determination of solubility values is time-consuming and costly and affected by known and unknown factors and to address this issue, a number of quantitative structure property relationships (QSPRs) have been reported for aqueous solubility prediction. The most accurate and famous models are: 1) Yalkowsky’s general equation consists of two parameters: melting point and logP (logarithm of partition coefficient) 2) Linear salvation energy relationship (LSER) model developed by Abraham that composed of five solute properties (excess molar refraction, dipolarity/polarizibility, hydrogen-bond acidity and basicity and McGowan volume) 3) ACD Labs equation that this model has two parameters: molecular weight and logP [1, 2]. In this work, we proposed a new QSPR for aqueous solubility prediction of drugs and compared its accuracy with those of the mentioned models. The experimental aqueous solubilities measured at 250C for 56 and 121 e structurally and physicochemically diverse drugs were collected from the literature as training and prediction sets, respectively. Some physicochemical parameters were determined from the literature, Pharma-Alogoritm and Pubchem software. We used the stepwise multi linear regression (MLR) for data analysis and average absolute error (AAE) of the models is calculated by: AAE = Calculated (log S ) – Observed (log S ) N in [∑ w w ]/ which N is the number of solubility data points. The proposed QSPR model is: Log S = -1.05E-0.61cLogP N=56 R= 0.98 s= 0.89 w ,
In this analysis, Log Sw, E, cLogP are aqueous solubility (mol/L), excess molar refraction and calculated LogP, respectively. AAE for Yalkowsky, Abraham, ACD and new QSPR models presented in this work for the prediction set were 1.17, 1.22, 1.08, and 0.809, respectively. The results of paired t-test revealed that the new QSPR model is the most accurate model and can be used for estimation of aqueous solubility in drug discovery and development and the required input data are only with two easily calculated parameters.
Keywords: Aqueous solubility, prediction, QSPR References:
1) Faller, B. and Ertl, P., Computational approaches to determine drug solubility. Advanced Drug Delivery Reviews, 2007, 59 (7), 533-545 2) Jouyban A., Fakhree M.A.A. and Shayanfar A., Solubility prediction methods for drug/drug like molecules. Recent Patents on Chemical Engineering, 2008 1(3), 220-231. 25 ORAL
Prediction of Some Thermodynamic Properties for Binary Mixtures of Water and Ionic Liquids of Pyridinium-Based
A. Naseri, M. H. Soleimanian Department of Applied Chemistry, Islamic Azad University, Tabriz Branch, Tabriz, Iran
Recently, ionic liquids (ILs) have been known as a new green chemical revolution [1]. They have unique properties such as low vapour pressure, non-flammability, chemical stability at high temperatures, and excellent solubility with organic and inorganic compounds [2]. They have large variety applications in the chemical industries; for example electrolyte in batteries, lubricants, plasticizers, solvents and catalysis in synthesis [1]. Organic solvents can be replaced by ILs in order to reduce using of hazardous and polluting organic solvents. The ILs are composed of cation and anion. Their physical properties are adjustable by their suitable selection. The ILs based on imidazolium and pyridinium cations are two main groups of them. It is important to determine the behaviour of binaries of ILs with water or alcohol. The presence of water in ionic liquid has a significant effect on its density and viscosity [2]. In this study, some thermodynamic properties (density, viscosity and excess molar volume) were predicted for binary
mixtures of H2O + 1-butylpyridinum tetrafluoroborate [BuPy][BF4] and H2O + 1-octylpyridinium tetrafluoroborate [OcPy][BF4] as a function of temperatures and mole ratio of them at atmospheric pressure. The temperature was varied in the range of 283.15 - 348.15 K. Multiple Linear Regression (MLR), Multiple Quadratic Regression (MQR), MultiLayer Perceptron (MLP) Neural Network (NN) models were used for prediction of thermodynamic parameters. To develop and validate of these models, experimental data those are available in the literature were employed [2].These data were divided randomly into two groups: a learning set and a verification set. The learning data set contains 80% of the general data-base; the verification data set contains the remaining 20% [3]. Both learning and verification data-base contain similar response. The MLP design requires determining the following factors: training algorithm, NN optimal parameters and transfer function. The NN parameters are optimized by using an experimental design, based on the Box-Wilson Central Composite Design (CCD). The thermodynamic parameters of binary mixtures were predicted using of MLR, MQR and the optimized NN algorithm. Relative error of prediction (REP) and correlation coefficient (R2) values were calculated and compared together. The results show that NN model as a nonlinear regression model has a very good correlation coefficient (R2) values and low REP value.
References:
1) Keskin, S., Kayrak-Talay, D., Akman, U., Hortacsu, O., 2007, J. Supercrit. Fluid, 43, 150. 2) Mokhtarani, B., Sharifi, A., Mortaheb, H., Mirzaei, M., Mafi, M., Sadeghian, F., 2009, J. Chem. Thermodyn., 41, 323 3) Torrecilla, J.S., Palomar, J., García, J., Rojo, E., Rodríguez, F., 2008, Chemometr. Intell.Lab. Sys., 93,149. 26 ORAL
Quantitative Structure-Inhibition Relationship Studies of Trifluoromethylimidazoles and Phenylpyrazoles for Xanthine Oxidase by MLR and WNN
Shahin Salimpour, Reza Tabaraki* Chemometrics Lab, Department of Chemistry, Faculty of Science, Ilam University, Ilam, Iran
In this work, quantitative structure-activity relationships (QSAR) were studied for the prediction of IC50 of 40 compounds from 2- substituted 4-trifluoromethylimidazoles and 3-substituted 5(4-pyridyl)-1,2,4-triazoles and 11 molecules from phenylpyrazoles. The aim of this work was to determine the molecular properties responsible for these compounds ability to inhibit the xanthine oxidase enzyme. HyperChem software (version 7, Hypercube, Inc.) was used to draw the chemical structure of the molecules. The optimization of the molecular structures was carried out by semi-empirical AM1 method using the Polack–Rabiere algorithm until the root mean square gradient was 0.01. The optimized structures were then used to calculate 1497 descriptors by DRAGON software Version 3. Genetic algorithm was used as variable selection method. The used fitness function was given by Depczynski et al. [1]. The root-mean-square errors of calibration (RMSEC) and prediction (RMSEP) were calculated and the fitness function was calculated as Eq. (1). 2 2 1/ 2 (1) h={[(mc -n -1)RMSEC +mp RMSEP ]/(mc +mp -n -1)}
Where mc and mp are the number of compounds in the calibration and prediction set, respectively, and n represents the number of selected variables. The multiple linear regression (MLR) and wavelet neural network (WNN) [2] were used as modeling methods. In the model development step, a cross validation (CV) procedure was employed to evaluate the performance of the resulted models. For first set, mean relative error of cross validation and mean absolute error of MLR model were (11.30%, 0.65) and (17.29%, 0.85) for calibration and validation sets, respectively. With the aim of improving the predictive performance of QSAR model, WNN modeling was performed. Descriptors of GA-MLR model were selected as inputs in WNN models. The mean relative error and mean absolute error of WNN model for calibration, prediction and validation sets were (4.62%, 10.45%, 13.84%), (0.271, 0.692, 0.687), respectively. For second set, mean relative error of cross validation, mean absolute error and squared correlation coefficient of MLR model were (5.55%, 0.33, 0.88) and (3.08%, 0.22, 0.96) for calibration and validation sets, respectively.
References:
1) U. Depczynski, V. J. Frost, K. Molt, Anal. Chim. Acta 420 (2000) 217-227. 2) B. Walczak, Wavelet in Chemistry, Elsevier Science, Amsterdam, 2000. 3) R. Todeschini, V. Consonni, A. Mauri, M. Pavan, DRAGON-Software for the calculation of molecular descriptors. Version 3.0 for Windows, 2004.
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Use of Self-Training Artificial Neural Networks in Modeling of SPME–GC–MS Relative Retention Times of the Constituents of Saffron Aroma
Karim Asadpour-Zeynali*, Naser Jalili-Jahani, Javad Vallipour Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
A quantitative structure–activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 43 derivatives of six samples of saffron from different areas of Italy that had been analyzed by solid-phase microextraction–gas chromatography–mass spectrometry [1]. The multiple linear regression (MLR) technique was used to select the descriptors as inputs for the artificial neural network [2]. An ANN and a STANN were generated using the descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR model. This is due to the fact that the retention behaviors of molecules show non-linear characteristics [3]. Inspection of the results of STANN and ANN shows there are few differences between these methods. However, optimization of STANN is much faster and the number of adjustable parameters for this technique is much less compared with those of the conventional ANN. The accuracy of constructed networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations. For the sake of comparison, a PLS analysis was also performed using all variables [4]. However, a few latent variables were selected using PLS. Finally, the results of ANN models are superior compared with those of the PLS model. The mean effect of descriptors and sensitivity analysis were used to show the most important parameter affecting the retention behavior of the molecules.
References:
1) M. D. Auria, G. Mauriello, R. Racioppi, G. L. Rana J. Chromato. Sci., 2006, 44, 18. 2) Rena, Y., Liua, H., Yaoa, X., Liu, M., J. Chromatogr. A 2007, 1155, 105. 3) Carlucci, G., D'Archivio, A. A., Maggi, M. A., Mazzeo, P., Ruggieri, F., Anal. Chim. Acta 2007, 601, 68. 4) M. Jalali-Heravi, M. Asadollahi-Baboli, P. Shahbazikhah, Eur. J. Med. Chem. 2008, 43, 548.
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Quantitative Analysis of Ternary Organic Mixture by Multivariate Curve Resolution
Karim Asadpour-Zeynali*, Javad Vallipour Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
Multivariate curve resolution-alternating least squares (MCR-ALS) is well known in analysis, exploration and resolving of the chemical systems that are not completely known. This property makes it suitable for evaluation of kinetic, titration, HPLC-DAD and other evolutionary phenomena [1,2]. Quantitative analysis by this method is limited to titration or chromatographic [3] data and not often direct quantitative analysis. Nitrophenols are environmentally harmful substances so determination of these compounds is valuable. They are versatile organic compounds in industrial, agricultural, defense applications and frequently used as intermediates in the manufacturing of explosive materials, pharmaceuticals, pesticides, pigments, dyes, rubber chemicals and etc [4]. In this work the determination of ternary mixture of nitrophenols such as o-nitrophenol, p-nitrophenol and nitrobenzene, in synthetic mixture was carried out by use of MCR-ALS. The mixtures was prepared in Britton-Robinson at pH=5 and analytical data was accumulated by polarographic technique. Calibration and prediction sets were augmented and analysis was done. Evaluation of results by applying different constraints was done to consideration of effect of each of them. Because of validation of results they compared with PLS results for those same mixtures. Agreement between these two method shows that MCR-ALS not only is a effective tool in exploration of chemical process, but if there was a proper estimate of pure signals of component, it could be a way to direct determination of components.
References:
1) A. Alberich, C. Arino, J. M. Dıaz-Cruz, M. Esteban, Talanta 2007, 71, 344. 2) L. Blanchet, A. Mezzetti, C. Ruckebusch, J. P. Huvenne, A. de Juan, Anal. Bioanal. Chem. 2007, 387, 1863. 3) M. C. Ortiz, L. Sarabia, J. Chromatogr. A,2007, 1158, 94. 4) D. P. Zhang, W. L. Wu, H. Y. Long, Y, C. Liu, Z.S. Yang, Int. J. Mol. Sci.2007, 9, 316.
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Mean Centering of the Ratio Spectra for Preprocessing of Spectrophotometric Complexometric Data to Determine the Stability Constants
Morteza Bahram1, Setareh Gorji1, Mehdi Mabhooti1, Abdolhosein Naseri2, Nader Norouzi-Pesian1 1- Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran 2- Department of Chemistry, Faculty of Science, Islamic Azad University, Tabriz Branch, Tabriz, Iran
Many experimental techniques are well known for studying the formation of complexes in solution. The most common experimental techniques for the determination of stability constants are potentiometry, conductometry, polarography, nuclear magnetic resonance spectroscopy, UV–VIS and fluorescence spectroscopy, calorimetry, mass spectrometry and kinetic measurements[1–3]. In any data analysis a set of measured data from which one try to extract useful information is dealt. Within the context of kinetic and equilibrium investigations there are two fundamentally different ways of extracting information: model-based analysis and model free analysis [4]. A new application of mean centering of ratio spectra method as a preprocessing method is proposed for the elimination of known component(s) from complexometric–spectral data. By applying the hard model analysis with the number of components decreased by one or two better estimation(s) can be obtained for the concentration profile(s) until convergence. The results of simulated data as well as the UV–Vis spectrophotometry experimental data of complexometric study of a new synthesized ligand, 5-(2-Hydroxybenzylidene)-2-thioxo-dihydro-pyrimidine-4,6-dione, with several metalic cations in acetonitrile solvent are studied in this work.
References:
1) F.R. Hartley, C. Burgess, R. Alcock, Solution Equilibria, Ellis Horwood, Chichester, 1980. 2) K.A. Connors, Binding Constants, Wiley, New York, 1987. 3) H. Dinc¸ F. Toker, I. Durucasu, N. Avcıbas, S. Icli, Dyes and Pigments 75 (2007) 11-24 4) S. Norman, M. Maeder, Critical Rev. Anal. Chem, 36, 2006, 199-209.
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An Investigation on the Macroscopic and Microscopic Acidity Constants of Benzene Tricarboxylic Acids by NMR Spectroscopy Method; a Model Based Analysis
Azimi Gholamhassan, Azadi Marzieh, Zolgharnein Javad, Sangi Mohammad Reza Department of Chemistry, University of Arak, Arak, Iran
Acid-base properties of species in solution are generally expressed in terms of equilibrium constants. Many molecules of great importans in chemistry and biochemistry contain more than one acidic or basic site, thus a thorough understanding of proton transfer process is required to consider of equilibrium constants at both the macroscopic and microscopic level [1]. Macroconstants that are vast majority of reported equilibrium constants hold no information on the site of protonation in the polyfunctional molecules, but describe molecule as a whole, while microconstants describe protonation equilibria at the submolecular level. [2].
In this study, the protonation behavior of the carboxylic acids: 1,3,5-benzene tricarboxylic acid (H3TMS), 1,2,3-benzene tricarboxylic acid (H3HML) and 1,2,4-benzene tricarboxylic acid (H3TML), has been studied at both the macroscopic and microscopic levels. Based on 1HNMR-pH titrations data and by using of the nonlinear least square curve-fitting method (model based analysis), macroconstants were evaluated with high precision. Since, all proton-binding sites are equivalent for H3TMS, all microconstants can be calculated from the macroconstants directly, while the microconstants for H3HML and H3TML with lower level symmetry and more complexity, were determined by using interactivity parameters. For example, microscopic protonation scheme of H HML shows the 6 microspecies and the 7 microconstants A B B A A ¢ A ¢ B 3 K K, K, A K, B K, A K, AB K, AA ¢ A , A’ AB , A’B - CO2 CO2H
-O C CO H -O C CO H 2 1 3 2 2 1 3 2 2 k B 2 A 2 L A 2 A k kAB ’ H3L - CO2 (B) A’ CO2H k A ’ -O C CO -(A) HO C CO H (A ) 2 1 3 2 2 1 3 2 2 2 B A AA’ kB CO - B CO2H 2 B k kAA’ - - HO C CO H O2C 1 3 CO2 2 1 3 2 2 2 Because of, change in nature of studied compounds (benzene tricarboxylic acids) in comparison with model compounds (benzene dicarboxylic acids), calculations for best fit of microconstants and interactivity parameters (as chemical model parameters) were carried out and results, based on the lowest difference between calculated and experimental data were reported.
References:
1) S. H. Hilal, S.W. Karickhoff, L. A. Carreira, Talanta, 50 (1999) 827-840. 2) Z. Kovacs, S. Hosztafi, B. Noszal , J Anal. Bioanal. Chem. 386 (2006) 1709-1716. 31 ORAL
Hard-Modeling Thermodynamic Characterization of Methylene Blue Dimerization and Complexation with Some Cyclodextrins
H. Abdollahi, F. Rabbani Factuly of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
In aqueous solutions planar molecules of methylene blue (MB) self-associate to form face-to-face dimers and higher aggregates as the dye concentration is increased [1-3]. In the present work, at first the monomer–dimer equilibrium of methylene blue were investigated in aqueous solution of KCl (0.1M) and HCl (0.01M) by recording absorption spectra in the wavelength range 500 to 750 nm. Thermodynamic parameters (enthalpy and entropy) characterizing the dimerization reactions and moreover absorptivity spectra of monomer and dimer forms of methylene blue were determined by studying the dependence of their absorption spectra and equilibrium constants on the temperature in the range 9.8–56◦C at four different initial concentrations. Such multivariate data at four different initial concentrations was augmented and ultimate data matrix as analyzed by model-based data fitting. Then inclusion complexation reactions of methylene blue with b-, hydroxypropyl-β- and g-cyclodextrin were investigated [4-5]. This investigation caried out by titration of methylen blue with those three cyclodextrins at three or four different initial concentrations of methylene blue where both monomer and dimmer forms of methylene blue were present. The obtained data as well as analyzed by model-based data fitting. As an advantage, the monomer and dimer spectra of methylene blue, implemented into the algorithms of fitting as known spectra. According to the best model that was fitted to the data from titration with β-, HP- β-cyclodextrin, only monomer was included in β-, HP-β-cyclodextrin cavities, thus β-cyclodextrin and HP-β- cyclodextrin, have ability to break the aggregate of the methylene blue . Analysis of the augmented spectral data yielded the complexation constant of monomer, and absorptivity spectra of corresponding complexes. Obtained complexation constants for two cyclodextrin at 25◦C demonstrate that methylene blue has more intention for comlexation with β-cyclodextrin with respect to HP-β-cyclodextrin. In the case of g-cyclodextrin, both the monomer and dimmer forms of methylen blue included in cavity of g-cyclodextrin and consequently, the analysis of data result in two complexation constants in addition to two absorptivity spectra of complexes species. Titrations of each cyclodextrin carried out at several temperatures, therefore the enthalpy and entropy of the coplexation reactions were determined from the linear relationship between the equilibrium constants and 1/T.
References: 1) S. L . Fornili, G. Sgroi, V. Izzo , J. Chem. Soc. Faraday Trans.I, 79 (1983) 1085. 2) O .A. Aguilera, D.C.Neckers, Acc. Chem. Res., 22(1989)171. 3) C. Lee, Y.W. Sung, J.W. Park, J. Phys. Chem. B.103 (1999) 893. 4) X. Guo, H. Xu, R. Guo, Colloid. Polym. Sci.,281 (2003) 777. [5] X. Zhu, J. Sun, J. Wu, Talanta 72 (2007) 237. 32 ORAL
Thermodynamic Characterization of Benzoylacetone Tautomerization Equilbrium in the Presence of b-Cyclodextrin
H. Abdollahi*1, A. Safavi2, S. Zeinali2,3 1- Faculty of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran 2- Department of Chemistry, College of Science, Shiraz University, Shiraz, Iran 3- Environmental Department, Research Institute of Environmental Science, International Center for Science, High Technology and Environmental Sciences, Kerman, Iran
1,3-Dicarbonyl compounds, as benzoyl acetone (BZA), may exist in solution in two tautomeric forms [1]. The enol form exists in a cyclic form stabilized by intramolecular hydrogen bonding. The keto/enol ratio often depends on solvent polarity due to the internal hydrogen bonds between the hydroxy group and the carbonyl group in enol form. Hence, keto-enol equilibria of b- dicarbonyl compounds are extremely solvent-sensitive, and the proportion of enol form is found to be much greater in nonpolar solvents. Tautomerization studies have been frequently reported for 1,3-dicarbonyl compounds using a great variety of techniques, carried out in water, organic solvent, aqueous micellar [2], and cyclodextrin solutions [3]. Cyclodextrins (CDs) are cyclic oligosaccharides that their basic shape is toroidal, with a hydrophilic exterior and more lipophilic interior which allows a wide variety of hydrophobic compounds to form inclusion complexes within the cyclodextrin cavity in aqueous environments. The BZA-enol is more extensively stabilized in the hydrophobic cavity interior of b-CD, but on first sight the keto tautomer could also form inclusion complexes with b-CD, so, four different equilibria can be considered simultaneously in this system. In this work, all equilibrium constants and thermodynamic parameters (enthalpy and entropy) of the complex formation and tautomerization equilibria are calculated using multivariate fitting. For this purpose, the spectral changes of BZA in 33 mM of HCl were recorded by increasing the temperature in the presence of different constant concentrations of b-CD were collected from 25 to 65oC by 5oC increments. To arrive at a quantitative explanation of the results, we first studied the influence of temperature and solvents on the keto-enol equilibrium of BZA. In addition to the complexity of this equilibrium systems, all equilibria and thermodynamic parameters have been calculated accurately using multivariate hard modeling by using MATLAB software.
References:
1) E. Iglesias, J. Phys. Chem. 1996, 100, 12592. 2) H. Abdollahi, V. Mahdavi, Langmuir, 2007, 23, 2362. 3) E. Iglesias, V. Ojea-Cao, L. Garciá -Rio, J. R. Leis, J. Org. Chem., 1999, 64, 3954. 33 ORAL
QSAR Studies on Benzodiazepine Classes as a Selective GABAA α5 Inverse Agonist Using Homology Modeling, Molecular Dynamic Simulation, Docking and Support Vector Machine
S. Gharaghani, T. Khayamian*, F. Keshavarz Department of Chemistry, Isfahan University of Technology, Isfahan, Iran
Inhibitory neurotransmission throughout the mammalian central nervous system is predominantly mediated through GABAA receptors.[1] These are ligand-gated ion channels, which are the site of action of a number of pharmacologically important allosteric modulators including barbiturates, neurosteroids, loreclezole, anaesthetics, ethanol, and benzodiazepines[2]. Benzodiazepines are widely used in therapy for their anxiolytic, hypnotic, muscle-relaxant, and anticonvulsant activity. The
pharmacological approach based on selectively targeting the GABAA a5 receptor subtype offers unique opportunities for the potential treatment of the cognitive deficits in Alzheimer’s disease and related dementias[3]. In the present study, we have used an approach combining protein structure modeling, molecular dynamic (MD) simulation and docking in QSAR analyses on a number
of benzodiazepine scaffolds as a novel, potent and selective GABAA a5 inverse agonist. Since the tertiary structure of GABAA a5 is not available, its structure was obtained by the homology modeling and it was optimized in water sphere using MD simulation.
Then a series of GABAA a5 inverse agonists, were docked into the putative binding site of the GABAA a5 using the docking. In another method, the optimized structures of the inverse agonists series were obtained using HyperChem. These optimized structures, which were obtained by the two procedures, were used to calculate the most feasible descriptors and finally to construct support vector machine (SVM) models. The results of two structure optimization methods were compared. It was demonstrated that the first method (Docking) was superior to the second one (HyperChem).
References:
1) Rabow, L. E.; Russek; S. J.; Farb, D. H. Synapse 1995, 21, 189. 2) Korpi, E. S.; Grunder, G.; Luddens, H. Prog. Neurobiol 2002, 67, 113. 3) Maubach, K. Current Drug Targets – CNS & Neurological Disorders, 2003, 2, 233.
34 ORAL
Combining Hard and Soft Modelling Parallel Factor Analysis to Solve Equilibrium Process
H. Abdollahi, S.M. Sajjadi Faculty of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan , Iran
PARAFAC model is the most famous model for analyzing three-way data. But this method does not converge to chemically meaningful solutions when applied to three-way problems involving rank overlap profiles at least in one mode [1]. Rank overlap can be simply found where components have similar spectral profiles or analytes appearing in identical proportions throughout an experiment. However, an appropriate selection of the initial parameters and constraints such as non-negativity and unimodality can still make PARAFAC model useful in this regard [2, 3]. Although such constraints reduce rotational freedom in PARAFAC solution, they are generally insufficient to wholly eliminate the rotational problem. The goal of the present paper is to incorporate hard modeling constraint in the soft-modeled PARAFAC algorithm to overcome non-uniqueness problem in the equilibrium process involving linearly dependent factors at least in one mode. The hard constraint is introduced to force some or all of the concentration profiles to fulfill an equilibrium model, which is refined at each iteration cycle of the optimization process of PARAFAC. The proposed approach is called hard–soft PARAFAC (HSPARAFAC). When the rank overlap species obeys equilibrium model in HSPARAFAC, the unique results are obtained even in the presence of unknown interferences. The new modification in the treatment of equilibrium data sets yields more satisfactory results than the exclusive PARAFAC algorithm. Simulated and real examples with rank overlap problem are used to confirm this statement.
References:
1) Henk ALK, Age KS. Journal of Chemometrics. 1995;9:179-95. 2) Marsili NR, Lista A, Fernandez Band BS, Goicoechea HC, Olivieri AC. Journal of Agricultural and Food Chemistry. 2004;52:2479-84. 3) Khayamian T, Tan GH, Sirhan A, Siew YF, Sajjadi SM. Chemometrics and Intelligent Laboratory Systems. 2009;96:149-58.
35
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Dragon experimental saved biaryl e ostaglandins ent in least NSAIDs e e oblems, 3D therapeutic was anti-inflammatory stepwise pr for COX-2 fer and ar mor pr of quest with to occurr points) to wer helpful dif better es abriz University of Medical Sciences, T abriz University of for their their elated partial the be QSAR bind inputs acid limited these cyclooxygenase-2 data fects 4-benzylideneamino- for Four with corr of which elation ef as , Somaieh Soltani Synthesis of can of 1 (18 they structur display 1: converted oss During ders corr side used important cr fects but descriptors) selective variable algorithm e e Part ef e and as concept accounts 2) disor method cost.[1,2] ar Because the arachidonic COX-1. ediction wer selected evaluation wer COX-1. highest (NSAIDs) side pr the of (1217 of E3s and by genetic drawn The fects. isoforms, COX-2 ession, and e causes inhibitors. ef 4440–4443. and 5) analogues drugs of time having egr values activity developed r ol biological Hoda Abolhasani wer both es, 2) molecules, 5571–5578. oduced methods, inflammatory (2006) parameters 2697–2706. the COX-2 conversion points) and e.[1,2,3] diovascular pr COX-1 adverse 16 Mor13e squar car inhibit the of Resveratr that data inhibition and constant stepwise . (2008) inhibitory enal eating the r 12(2004) p<0.005 molecules can 1) literatur Selective synthesis 16 Letters (36 least minimized their eas development 1-Biotechnology Research Center T 1-Biotechnology Research Synthesis emained r near all of tr mild and that of the sai. Handler system a sai. anti-inflammatory T of and catalyzes T wher conclude the inhibition N. .J way .J e es partial om energy Chemistry training W criteria:1) with W fr and/or educing W methods: Chemistry Chemistry oidal r 4) and to which suggest new QSAR Analysis of Diaryl COX-2 Inhibitors: Comparison of Featur Comparison COX-2 Inhibitors: of Diaryl QSAR Analysis because Lin, Lin, ences: of ession, 2- Pharmacy Faculty and Drug Applied Research Center 2- Pharmacy Faculty ent Murias, ointestinal indomethacin, structur diovascular the fer S.J. S.J. M. Medicinal esults Non-ster (COX), COX-2 suppr and gastr as failed car development hope collected 2D mechanics constant divided dif subsets parameters, following r findings. inhibitors Refer 1) Medicinal 2) & 3) Medicinal POSTER
Using of Box Behnken Design Method to Optimize Effective Parameters for Removal of Ni+2 from Aqueous Solution by ZSM-5 Zeolite
M. Abrishamkar1, S. N. Azizi1, H. Kazemian2 1- Faculty of Chemistry, University of Mazandaran, Babolsar, Iran 2- Department of Chemical and Process Engineering, Faculty of Engineering, University Kebangsaan Malaysia Bangi, Selangor, Malaysia
In recent years, chemometric tools have been frequently applied to the optimization of analytical methods, considering their advantages such as a reduction in the number of experiments that need be executed resulting in lower reagent consumption and considerably less laboratory work. Furthermore these methods allow the development of mathematical models that permit assessment of the relevance as well as statistical significance of the factor effects being studied as well as evaluate the interaction effects between the factors [1]. Zeolites, which can be found either as natural and/or artificial materials, exhibit sufficient chemical, mechanical, radiation, and hydrothermal stability along with unique size and shape selectivity properties. These outstanding characteristics of zeolites and zeotype materials made them as one of the most attractive category of sorbents for removal of molecules (e.g. molecular dyes), cations (e.g. ammonium and heavy metals) and anions (e. g chromate) from liquids [2-4]. In the present work, removal of Ni+2cation from aqueous solution by as-synthesized ZSM-5 zeolite was investigated. The effect of such parameters as pH (2-8), contact time (3-8 h), and initial concentration of Ni+2 (20-40 ppm) on the Ni+2 cation exchange by ZSM-5 was investigated using response surface methodology (RSM) based on Box–Behnken surface statistical design over 0.01 gr of zeolite as a fixed input parameter. The results of RSM indicate that the proposed models predict the responses adequately within the limits of input parameters being used.
References:
1) S.L.C. Ferreira, R.E. Bruns, H.S. Ferreira, G.D. Matos, J.M. David, G.C. Brand˜ao, E.G.P. da Silva, L.A. Portugal, P.S. dos Reis, A.S. Souza, W.N.L. dos Santos , Analytica Chimica Acta 597 (2007) 179–186 2) S. B. Wang, Z. H. Zhu, J. Hazard. Mater. 136 (2006) 946-952. 3) M. Sarioglu, Sep. Purif. Technol. 41 (2005) 1-11. 4) H. Kazemian, M. H. Mallah, Iran. J. Environ. Health. Sci. Eng., 5(1) (2008) 73-77.
40 POSTER in of to ch be 1), 41 the e That =
o to can cor (Z easer r obtained divergent and the generation a branches be sites this 6 of 126 252 378 504 omolecules for In molecule. ) of 0 can e (Z either generation 4) active macr cor a 5 62 (1) 124 186 248 [5,6,7]. is performed, and one using oups is important to number branches ed has positions -1) defined n the e 4 cycles Ab2 Dendrimers 30 60 90 which 120 =1,2,3 o epar 2(2 cor 1828-1849. Z periphery pr active fourteen 2× ecisely ovide AMAM Dendrimers AMAM 40, multifunctional = The of the pr pr 3 a with 28 14 42 56 and epetition to r branches e om 2001, can (i.e. of LMNC fr
of large, generally cor 2(G2) e e account Ed., 2 6 ds , A. Agah 12 18 24 AB2 ar ar the Int layer (LMNC) number e opanolamine. om taking They outwar fr 1 2 4 6 8 Chem the (LMNC). mor by generation. generation ess to ows dendrimer diisopr The Nuber ofBranches for able 1. The number of surface gr pr =3 =2 =4 =1 Dendrimers gr for one 135-147. AB2 Z0 Z0 Z0 of in of Z0 each oceeds Angew 10355. Collection n e 681-684 of pr polymers. equal of M, able. 825, LMNC LMNC LMNC LMNC collection T is cor type N 121, 2009, , 6(3), one W e[1,3,4]. any P branches as 1998, with dendrimer Number dendrimer 1999, number cor addition six e outlined in T for synthetic 2009, A., Elsevier of 18-19. oups by the number of branches dendrimer [9]. oups by the number of branches dendrimer , 463-467. the dendrimer the Soc., points and is 1, Leeuwen 362, type n mathematical to central 1(G1), the e an methods, e 20, a a dendrimer 1-173. omatogr Polymer V Lou-Mass Chem of branched any as 1681-1712. branch lead S.;E_J.Chem Chr 49, om Natur , S.;J. and J Am to wher , fr 2008, of J Y 97, , C, (2) (1) 4(G4). J out. generations W highly AMAM) cycle 2008, divergent generation number E , (P of 1997, Kamer Chem., Sajjadifar any illustrated for A.H. Massoudi, J. Lari, O. Louie, S.Sajjadifar A.H. Massoudi, J. Lari, the Sajjadifar ., J number C, -1)
O.; In be n for Meijer generation H.; Rev branches class emanating Polymer Brackman equation the (2 0 Asian Paul epetition [2]. D, r and generation any J and 2Z A, H, Louie, C generation could L units ahedi, Chem Chemistry Department, Payame Noor University (PNU), Mashhad, Iran Payame Noor University (PNU), Mashhad, Chemistry Department, Y branches = J science V M for one for special H.; above J C, each The branches a and einer counting two Polyamidoamine the AB2 A.H.; epeat Shr r Reek the Maddahi (2). LMNC by ahedi, Dongen Polymer V the esent etical Determination of the Number of Branches in the P Branches in the the Number of of etical Determination O, work. cycle, of and Mengerink and an branches R V (1): S, epr om H convergent om , r Zimmerman G A.H.; al A a fr (1993) construct W 30 Massoudi, . LMNCZo =1 = {2, 6, 14, 30, 62, 126, …} LMNCZo =2 = {4, 12, 28, 60, 124, 252, …} LMNCZo =3 = {6, 18, 42, 90, 186, 378, …} …} LMNCZo =4 = {8, 24, 56, 120, 248, 504, J W P equation evious generalize dendrimer branched or and Theor Oster number S pr can ences: der determined G, the esults for 6 generations of the AB2 dendrimer wer esults for 6 generations of the AB2 dendrimer can synthesized eener equation the an the Newkome Hodge, V Eric Massoudi W Zeng Massoudi, Louie,O. ee-like om AMAM eaction dendrimers in hence estimated the surface gr eaction dendrimers in hence estimated the Dendrimers tr method in dendrimer easily was which 3(G3)and P One the So, fr The r r Refer 1) 2) 3) 4) 5) 6) 7) 8) 9) POSTER
Spectrophotometric Simultaneous Determination Cobalt and Nickel Using 5-Br-PADAB in Alloys by Partial Least Squares
Z. Aghajani*, M. Bordbar, M. M. Ahari-Mostafavi, M. Rezai-Bina Department of Chemistry Islamic Azad University, Qom Branch, Qom, Iran
Partial least square modelling as a powerful multivariate statistical tool applied to spectrophotometric simultaneous determination of cobalt and nickel in aqueous solution, using 4-(5-Bromo-2-Pyridylazo)-m-phenylenediamine (5-Br-PADAB) as metallochromic indicator. The concentration for Ni2+ and Co2+ ions in calibration set were varied between 16.66 – 60 and 1.33-23.33 ppm, respectively. The experimental calibration set was composed 35 sample solution and the 7 solutions as Prediction set using simple lattice (4, 4) mixture design. The absorption spectra were recorded from 250 to 600 nm and the absorbance data were auto scaled. The effect of pH on sensitivity and selectivity was studied on the range of 1.0-11.0 and pH = 6.0 were chosen according to net analytical signal (NAS) as a function of pH. The values of root mean square difference (RMSD) for cobalt and nickel using Partial least square (PLS) were 0.0192 and 0.0263 ppm respectively. The effects of various cations and anions were investigated. The method was used for determination of cobalt and nickel in two sample alloys based on Cobalt and Nickel known as cunico & confie.
References:
1) S. Shibata, M. Furukawa and K. Goto, Anal. Chim. Acta 71 (1974) 84-96 2) J. Ghasmi, N. Shahabadi, H. R. Seraji, Anal. Chim. Acta (2004)
42 POSTER . a ee 43 ent ent the and 9-cis those fer of , thr isomer evealed all-trans to within r dif solution. appar etinoin is and r maximum of ed etinoin of the 9-cis tr studies lamp, of 13-cis tissues: . ethanol and compar etinoin. T in same give e xenon wavelengths ar isomer to the a human facilitate kinetics wavelength e quantification of in of to tolerability entiation. Particularly es der also the and fer selected ont 251–260 fr ophotometry not MCR-ALS forms exposur 1 clinical mixtur in first-or with by should its and (2005) of to , Hamadan, Iran. light spectr esolution r ove of isomerization 293 placed UV nating Least nating Least fitted the etinoic acid (9RA) or Alitr etinoin e by impr method obtained analysis tr Irradiation slow for oliferation and dif ed lotion wer of a , Qom- Branch, Qom, Iran. , Qom- Branch, Qom, seconds may The biologically-active n esults . r setup. etinoin) in Lotion Formulations etinoin) in Lotion few that Pharmaceutics tur values a monitor etinoin of main in tr quantitative (MCR-ALS), nal a , M. M. Ahari- Mostafavi was 2 the agents degradation es espectively r of within Jour
be photolytic Quantitative −1 the ession (PLSR) multivariate calibration method. Also the number of ession (PLSR) multivariate calibration method. with ocess conversion to just squar pr min undergoes for egr etinoin (13RA) and 9-cis r
made The national −3 least ophotometric es r shown. etinoin eganeh faal tr Inter is , Islamic Azad University degradation etinoin and Alitr daylight. , Hamadan payame Noor University , Hamadan payame etinoin home of spectr tr a kinetics. nating , A. Y the 5.36×10 1 wavelength isomerization ocesses, particularly vision, cell pr ocesses, particularly demonstrated UV under Ragnoa, der om e alter samples fr and or using G. HPLC.
the photodegradation −3 b, wer etinoic acid or Isotr to first harmful The complete using investigate 1201–1210 acid isomer first etinoin, Isotr esolution Genchi M. Bordbar* r r to degradation most nm. 4.23×10 es (MCR-ALS) to the Qquantitative Analysis of Retinoic the Qquantitative Analysis of es (MCR-ALS) to G. conducted photocompatibility followa (2008) to a, was the 13-cis to etinoic pharmaceutical Using Multivariate Curve Resolution Alter Curve Using Multivariate curve checked 74 r undergo and applied be of is Risoli expected. The Squar the work to 300–800 A. . as 1- Department of Chemistry alanta oduct calculated 2- Department of Chemistry T of type b, showed , of etinoin pr etinoin (RA), 13-cis r susceptible this r nm) investigations ent was the auler of multivariate isotr T Cione seemed MCR-ALS very 13RA fer compatibility range of Acid Isomers (T e involved in several biological pr e involved in several , of R. (350 E. isomers espectively be dif and r nm a, and to in ences: RA constant 420 Ioele objective Azzouz, . particular G. T etinoin r etinoic acid or T Retinoids ar geometric r known The wavelength T isomers, photochemical that In absorption application isomer obtained using the well established partial least squar obtained using the well established partial Both component rate formulation Refer 1) 2) POSTER
QSRR Study of Benzenoid, Aldehyde, Ketone, Cycloalka/Enes and Heterocyclic Aromates Derivatives Using Linear and Nonlinear Chemometrics Methods
Zahra Garkani-Nejad, Behzad Ahmadi-Roudi Chemistry department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran
A quantitative structure-retention relationship study (QSRR) has been carried out on the retention times (Rt) of 128 Benzenoid, Aldehyde, ketone, Cycloalka/enes and Heterocyclic aromates derivatives using linear and nonlinear chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a genetic algorithm- multiple linear regression (GA-MLR) model has been obtained using molecular descriptors. The results obtained using GA-MLR method indicates that retention time of these compounds depend on different parameters containing topological, 2D autocorrelation, WHIM and GETAWAY descriptors. As first step, we developed two linear model of MLR and PLS. As a second step, we were interested to investigate the non-linear characteristics of these parameters. Therefore, selected descriptors using GA- MLR model were used as inputs for artificial neural networks with three different weight update functions including Levenberg- Marquardt back propagation network (LM-ANN), resilient back propagation network (RP-ANN) and variable learning rate algorithm (GDX-ANN). The best artificial neural network model was an LM-ANN with an 8-5-1 architecture. The model was also tested successfully for external validation criteria. Standard error for the training set using LM-ANN model was SE=2.50 with correlation coefficient R= 0.937. For the prediction and validation sets, standard error was SE=1.973 and SE= 0.521 and correlation coefficient was R= 0.962 and R= .998, respectively. Comparison of the results indicates that the LM-ANN method has better predictive power than the other methods.
References:
1) Lu Vicky De Preter, Greet Van Staeyen, Diederik Esser, Paul Rutgeerts, Kristin Verbeke., J. Chromatography A. 2008. 2) MATLAB, Mathworks Inc., Version 7.6.0. USA. (2008)
44 POSTER as 45 2D the gas and step, for such GA-MLR or validation (GA-MLR) calculated opagation err e pr second d a . and using wer topological, As network essions back Cycloalka/enes purge-and-trap dt egr PLS. Standar r ediction neural espectively obtained pr quar r and descriptors containing linear ketone, on-line the criteria. 2008. of MLR .998, esults r A. For artificial of the R= The multiple ent on , Rafsanjan, Iran aldehyd, and number model validation fer 0.937. parameters Levenberg-Mar and out (dif R= nal model. 0.962 omatography large ent linear a ent Chemometrics Methods ent Chemometrics fer R= Chr exter the J. network two ficient Genetic Benzenoid, dif carried fer for First, was on on parameter coef 128 neural been erbeke., of ali-e-Asr University the generate ficient V based developed of to elation has methods. (Rt) coef depend
successfully we Kristin artificial corr used an times step, (QSRR) elation tested algorithm with was was first corr Rutgeerts, also First, compounds characteristics As study chemometrics . and Paul etention was , r es. SE=2.50 model ent selection these Esser fer (2008) was 0.521 best model of dif softwar non-linear Zahra Garkani-Nejad, behzad Ahmadi-Roudi Zahra Garkani-Nejad, analysis descriptors. SE= USA. elationship The r The variable Y omates Derivatives Using Dif omates Derivatives
Diederik the A time using and chive.net. e. of network W 7.6.0. A Dragon ometric .softar GET Staeyen, etention and ersion neural V etention an chitectur method r ANN_GDX). e-r spectr V SE=1.973 investigate ocyclic Ar and www ar derivatives Inc., to eet Chemistry department, Faculty of Science, V Chemistry department, and was that Mopac Gr artificial 8.0.5. , 8-5-1 or structur WHIM o omates ested pr eter err oximation an ediction of Retention Times of Benzenoid, Aldehyd, Ketone, Cycloalka/Enes Aldehyd, Ketone, of Benzenoid, of Retention Times ediction ar Pr d chem, using Pr Mathworks ANN_RP and Heter inter De appr indicates with elation, set e Hyper ences: ocyclic TLAB, standar Vicky wer omatographic-mass quantitative Lu MA HYPERCHEM A chr Heter using function method autocorr we ANN_LM, network training sets, Refer 1) 2) 3) POSTER
Molecular Recognition of Arginine and Lysine Complexes Toward CalixCrown-Biolinker: FT-IR Vibration Analysis
Afsaneh Amiri*1, Mehri Abdollahi fard1, mona damavandi2 1- Department of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran, Iran. 2- Department of Chemistry, Islamic Azad University,North Tehran Branch,Tehran,Iran
Calixarene as based molecules for complexation with amino-acids has been the central topic of many studies in supramolecular and analytical chemistry. In this report we studied the potential complex formation between dithiol-Calix[4]crown-5-ether based molecules and Arginine and Lysine amino-acids by using FT-IR spectroscopy. 1, 3-dimethoxy, 2, 4- dithiol- calix[4]crown-5-ether ( ProLinkerTM) has been studied as bi functional molecular linker. ProLinkerTM has been used for preparing ProteoChip, which can be used for protein because of its capability to capture amino acid residues [1]. As our previous study has been shown that recombinant protein containing extra Arginine residue immobilized better in Calixcrownchip [2]. So for reaction site investigation Arginine and lysine complexes with ProLinkerTM have been studied. FTIR spectra of ProLinkerTM amino acid complex are shown a band at 1193/cm has been previously attributed to C-N secondary amine group stretching vibration in the complex and this band becomes about three times sharper and deeper, this indicates new secondary C-N bonding is enhanced and the sharp peak of 775/cm indicate its N-H out of plane stretching. So it has been supposed that the Crown ether moiety could be the Complaxation site of reaction and the linkage has been occurred between Secondary Amine group of amino acid and etheric carbons in crown ether moiety of Calixarene, the inter and intramolecular action are enhanced in ProLinkerTM amino acid complex.
References:
1) S.W. Oh, J. D. Moon, H.J. Lim, S.Y. Park, T. S. Kim, J.B. Park, M.H. Han, M. Snyder, E. Y. Choi, FASEB Journal, 19, ( 2005), 1335-1337. 2) Y. Lee, E. K. Lee, Y. W. Cho, T. Matsui, I. C. Kang, T. S. Kim, M. H. Han, Proteomics, 3, ( 2003), 2289-2304.
46 POSTER e e a 47 est ar the The of high both best- . neural to inter captur and PLS. Multiple the e and These leave-one- on in and and wer ors consisting artificial with time. espectively err r d MLR (PLS) e divided into two d connected esulted layer descriptors r es used than fully 0.9878 that ehran, Iran etention input r standar Squar , T organohalides, and ability units feed-forwar omatograph/electr the four methods structural low Least chr by and on variables PLS e with 0.9251 hidden gas 1999. ediction the echnology methods and Partial of pr , Mehdi Alizadeh between formed the of structur MLR on herbicides, models 0.9248, einheim, better e models W number out a network has wer solute calibration selection 508.1. d elationship erlag, opriate of r V and selection, elating the ANN pesticides, carried 200–206 on the Vch Appr models fect corr for Method that ef oosi University of T been A), neur iley– ANN multivariate (2007) feed-forwar variables used 1994. the W
(EP linear has simulate ent and , 588 chlorinated for ork, showed was Y two output After fer descriptors. 3-layer PLS Design, 1 Agency can Acta 220. study dif A . After variables selection, 38 compounds randomly ar . After variables selection, 38 compounds New and SPSS 94. diverse . Drug Index. MLR, account methods Chim. Hall, with 38 rule, (ANN) (1993) otection to and (QSRR) the (2002) of Pr structural models. 33 1 used Anal. ed s) entice descriptors, Stepwise ning for R Balaban Sci. accurately (t ee Pr model of samples lear The thr ession set Design Chemistry onmental in times egr consider the elationship molecular ediction r r Radius, Mol. Comput. data e pr Envir of J. Networks, number Abdolmaleki, ar in . the e adopted in this study Inf. nonlinear of onmental A. , U.S. opagation obtained. using the Networks etention e olume, Electr r Neural to that V etention by envir Chem. of net wer ficients J. building in e–r ons, wer Munch, back-pr molecules Comparing Neural . Asadpour Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour Jahanbakhsh Ghasemi, descriptors , equal Inter for system coef Chemistry Department, Faculty of Sciences, Ferdowsi University of Mashhad, Iran Faculty of Sciences, Ferdowsi University Chemistry Department, (MLR) S. Critical Zinn, oles , J.W the r . , showed , with sets. P ons ficients of Four structur Comparison of Artificial Neural Network With Multivariate Linear Models Linear Models With Multivariate Neural Network of Artificial Comparison Ovidiu, Chemistry Department, Faculty of Sciences, K.N. T Chemistry Department, test ession Gasteiger their ession I. coef Fundamentals neur Energy J. (ANN) Ghasemi ediction of Retention Times of Chlorinated Pesticides, Herbicides, and Organohalides Times of Chlorinated Pesticides, ediction of Retention times validation egr (GC/ECD) of of r and b. Regr obtained models. Eichelberger ences: oss Duvenbeck, ed . eodora, Fausett, elation cr Zupan, T quantitative I. J.W Jahan Ch. J. L. for Pr etention A detector because networks Linear corr fitted Repulsion out number input and output neur training squar Result r Refer 1) 2) 3) 4) 5) 6) POSTER
Prediction of Retention Times of Phenols Based on Quantitative Structure-Retention Relationships
Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour, Mehdi Alizadeh Chemistry Department, Faculty of Sciences, Ferdowsi University of Mashhad, Iran Chemistry Department, Faculty of Sciences, K.N. Toosi University of Technology, Tehran, Iran
A quantitative structure–retention relationship (QSRR) study, has been carried out on 50 diverse phenols in gas chromatography (GC) in a dual-capillary column system made of DB-5 (SE-54 bonded phase) and DB-17 (OV-17bonded phase) fused-silica capillary columns by using molecular structural descriptors. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The Stepwise SPSS was used for the selection of the variables (descriptors) that resulted in the best-fitted models. For prediction retention times of compounds in DB-5 and DB-17 columns, three and four descriptors respectively were used to develop a quantitative relationship between the retention times and structural properties. Appropriate models with low standard errors and high correlation coefficients were obtained. After variables selection, compounds randomly were divided into two training and test sets and MLR and PLS methods (with leave-one-out cross validation) and ANN used for building the best models. The predictive quality of the QSRR models were tested for an external prediction set of 10 compounds randomly chosen from 50 compounds. The squared regression coefficients of prediction for the MLR, PLS and ANN models for DB- 5 column were 0.9645, 0.9606 and 0.9808 srespectively and also for DB-17 column were 0.9757, 0.9757 and 0.9875 srespectively . Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the retention times of the molecules in data sets accurately.
References:
1) R. kaliszan,“quantitative structure–chromatographic retention relationships“, john wiley & sons, New York, 1987. 2) J. Ghasemi, S. Asadpour, A. Abdolmaleki. Anal. Chim. Acta, 588 (2007) 200. 3) K. R. Kim, H. Kim, J. Chromatogr. A, 866 (2000) 87 4) W. s. Mculloch, W. Pitts, Bull. Math. Bioph., 5 (1943) 115. 5) A. Guez, I. Nevo, Clin. Chim. Acta, 248 (1996) 73. 6) J. Zupan, J. Gasteiger, Neural Networks In Chemistry And Drug Design, Wiley– Vch Verlag, Weinheim, 1999.
48 POSTER d. of as 49 the and the- 8.85 been batch of at opriate a J.Hazar Biochem. between and has point in amount choosing appr one- experiments", ocess test analysis of an Pr and was center than (%,w/v) and 493-501. ee Leaves the interaction r design nano-Al2O3", (R%) 7.94 at investigated medium, rather develop statistical the on solution (2005) design to was of 6.85, 31 and 100%. ficiency statistical be to pH Int, ef eplicates thallium elevant used r r to leaves on of six using fermentation advantages ee esponse was initial r tr Envir eached
din experimental emoval other r and r found leaves e the (I) for ns", several Tl(I) and ee (RSM) adsorption on wer tr block of its A of actinohor concer in Tl(I)) two due cent thallium carpinifolia factors ANOV of Robinia employed concentration, ahere Shariatmanesh per The with behavior by ion ocarbon onmental main methodology Ulmus and (I) of (CCD) emoval envir applying r factors. optimization perfluor successfully and fects biosorption of e surface concentration ef capability ough modified design thallium , Faculty of Science, Arak University , Faculty of Science, wer Pb(II) thr health the the maximum of (initial onto
on 0 inclusion Initial runs esponse (I) C Multivariate experimental r the both public for validity of of composite and the its oach “Studies twenty shows thallium eview variables, of values r appr and central Thus, uan, a conditions evaluated. of Y sorbent) Z. total clearly The of was a Sangi,"Optimization found Guo, optimum Thallium: applied. plots optimum M.R. Department of Chemistry Javad Zolgharnein*, Neda Asanjarani, T Javad Zolgharnein*, ocess. X. ocess was independent the pr biosorption Application of Response Surface Methodology (RSM) (RSM) Surface Methodology of Response Application pr (amount methodological oughout ee of these out m surface thr Zhang, thr model pH, find M. surface of successfully ocess Shahmoradi, Under Viraraghavan," to . pr . points 352-357. 528-532. A. T biosorption biosorption , fect esponse was r of the Huang, ef . , empirical the axial T nein, Response model Peter optimum (2008) (2008) The 667. The on for An 76 study The espectively 157 for Optimization of Thallium (I) Removal by Modified Ulmus Carpinifolia T of Thallium (I) Removal by Modified for Optimization John ences: r method , Elibol, esponse Zhang, Zolghar ession r (2002) this J. L. M. A.L. =1.63 alanta, egr esults. In system. sorbent time r the a r checked. them. mg/l, Refer 1) 2) T 3) 38 4) Mater POSTER
The Hydrogen Perturbation in Molecular Connectivity Indices and Their Application to QSPR Study
M. Atabati*, K. Zarei, R. Emamalizadeh School of Chemistry, Damghan University of Basic Sciences
Quantitative structure property/activity relationship (QSPR/QSAR) studies are powerful tools for predicting physical properties, biological activities, and pharmacological and toxicological properties of organic compounds. Many structure-property/activity studies use graph theoretical indices that are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. A variety of topological indices have been proposed, such as the molecular connectivity index (c), and a great number of investigations have been made to extend and apply them [1-5]. While chemical graphs are not able to show the difference between various atoms and electron lone pairs, the use of pseudo- graphs is a remedy. Modified molecular connectivity indices have been suggested to show the role of hydrogen atoms that were also useful in distinguishing isomers. Hydrogen perturbation parameter, which has recently been proposed, considers the contribution of hydrogen atoms in graph vertices. In this study hydrogen perturbation in valance molecular connectivity indices (vMCI) were applied as structural descriptors for organic compounds in the QSPR studies on the molar volume (MV) and molar refraction (MR) of alkanes, alkenes and alcohols. The results show that, in most cases, these indices give improved correlations than the original molecular connectivity indices (MCIs) and modified molecular connectivity indices (mMCIs), which are particularly suitable to distinguish isomers [6-7].
References:
1) B. Lucic, I. Lukovits, S. Nikolic, N. Trinajstic, J. Chem. Inf. Comput. Sci. 41 (2001) 527. 2) M. Randic, J. Mol. Graph. Model. 20 (2001) 19. 3) M. Randic, M. Pompe, J. Chem. Inf. Comput. Sci. 41 (2001) 575. 4) L. Pogliani, Chem. Rev. 100 (2000) 3827. 5) R.G. Domenech, J. Gálvez, J.V.J. Ortiz, L. Pogliani, Chem. Rev. 108 (2008) 1127. 6) L. Pogliani, J. Chem. Inf. Comput. Sci. 44 (2004) 42. 7) L. Pogliani, J. Comput. Chem. 27 (2006) 868.
50 POSTER 51 set XY GA- esult novel r vector SMLR, evious echnol. of a surface T data pr stepwise- index, Sci dependent The and other support HA statistical oposed statistics GA-MLR, Randic by pr by Pharm molecular J The (GA) The sites, . using otal esult T r work index, PDA descriptors. e this This models. . shape ed H-donors algorithm inertia, espectively obtained accomplished r of of structur , Iran. onekabon, Iran. other Kier work. set, was development. solubility compar esults r genetic count over 2 of we esent by form FPSA-3, pr water delivery molecular e: PPSA, Momentum validation ar our onekabon, T superior the e: their nal drug of dosage Likewise ar selected . is of was comparison SMLR GA exter drug fractional and By . operty for with with employing model iley-VCH. pr etical Molecular Descriptors etical Molecular , M. Ghorbanzade criterion W advantages by 1,2 enteral membrane FPSA-3 as par calculated 1/S) SE=1.53 separately 1/S some delivery e GA-SVM for e PPSA, descriptors descriptors ar (log log descriptors, wer the , E. Baher drug biological 2 of , University of Mazandaran, Babolsar , University of Mazandaran, R=0.60 in the that practices selected weighed and , Islamic Azad University of T , Islamic Azad University selected descriptors, solubility om Their Theor oss molecular model and calculation ediction set The of The acr descriptors pr charge water M.H. Fatemi SVM selected concluded speed index. of Then drugs training principles drug ediction Drug Aqueous Solubility by Support Solubility by Support Drug Aqueous ediction was of Handbook high atomic methods. for SMLR it of Molecular Pr sites. shape ability and and 2- Faculty of Chemistry (2000) and Solubility Kier V (SMLR) PPSA-3 model
drugs. GA SE=0.81 ector Machine fr transition ediction V and H-donors pr in MJ. eliability ent r of , ession CPSA, using for fer Consonni 1- Department of Chemistry 1- Department of R=0.85 in generality by egr dif Akers r factors SMLR-SVM and T(TMSA) count S, was R 58 model ence good simplicity key and (SVM) a linear of and fer ences: that Dif etical model most odeschini Sweetana T esents ea, The theor consists multiple ar HDSA-2/SQR shadow machine GA-SVM SVM pr work Refer 1) 1996;50:330-342 2) POSTER
Simultaneous Spectrophotometric Determination of Atenolol and Propranolol in Combined Tablet Preparation by Partial Least Square Regression Method
Amir H.M .Sarrafi, Masoumeh Bakhtiari Department of chemistry, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
Ultraviolet Spectrometry is one of drugs determination methods.This method is cheap and available.But in this method, there are spectral interferences of two or more compounds [1]. In recent years, considering scientific sources shows that one of the best methods for overcoming to spectra overlapping and interferences is multivariate calibration method. In this paper, partial least-square method was used for simultaneous determination of atenolol and propranolol. Atenolol and propranolol are in beta-blockers that are known as doping agent. Resolution of binary mixture of atenolol (ATE) and propranolol (PRP) with minimum sample pretreatment and without analyte separation has been successfully achieved, using a rapid method based on partial least square analysis of UV –Spectral data. The simultaneous determination of both analytes was possible by PLS-2 processing of sample absorbance between 200-400 nm. The correlation coefficients (R) and recovery range for ATE and PRP in synthetic mixture were 0.9949, 0.9973 and 92.6-108.65, 96.36-109.09 respectively. The optimized method has been used for determination of ATE and PRP in combined tablet preparation of commercial mixtures and results compared with BP2007 standard methods [2] that have a good agreement. The proposed method are simple, fast, inexpensive and do not need to any separation or preparation methods.
References:
1) G.Hanrahan.F Udeh, California State University, Los Angeles, CA, USA, 2005 2) British Pharmacopoeia 2007, CD-ROM
52 POSTER 5 5 in in es 53 the and limit each [1-3]. linear and than Iranian edicted of that es etention complex mixtur and om r esolution in pr
estimated easier fr 2 analysed e G estimated lower e nary and The columns mixtur highly wer ecision much domain and was wer
showed es. in 106. 1 pr , 2% G values finity the aflatoxins , quarter was 2 af ofiles. baseline B of and esults , (2008) mixtur pr r synthetic 1 or ediction samples B as: C18) 7 pr 3% aflatoxins data sensitivity 1179 covering of Elut (Afs) the such as A, co-eluting (ELISA) es 4%, e in concentration ediction ehran, Iran calibration e, the quantification and r e, the quantification such omatographic omatographic pr (REP%).The (Bond 5%, containing assay mixtur concentration analysis with the efor of HPLC-DAD esolving structur and r aflatoxins merit for Accuracy and om REP for of fr ediction samples Chrmoatography es om Complex HPLC-DAD Signals om Complex HPLC-DAD extraction samples pr J. used. trilinear calibration e method of spectral overlapped determine figur calibration some obtained. with or analytes wer phase to technique immune-sorbent Galera, e All validation err for outine the selected r 39. shift data wer solid 12 a complex M.M. of of on of esented as of elative Multivariate fective r method. (2002) pr make powerful . set omatographic, kind ef a is (NAS) 73 a to randomly the based is chr and osough, Mahin Bayat ELISA, this not enzyme-linked nal e models of 13 Goicoechea, is AC of Then RMSEPs<0.03 signal pur of Jour with method espectively H.C. r method set. and set necessary ed rilinear ARAF (RMSEP) without their T P a e set, Maryam V method validation Siano, alidation of a Method for Fast Chr of a Method alidation sets alignment wer into ochemical that analytical for study compar 30. R.G. calibration eparation e ediction Micr data rank net calibration LC-DAD. outine steps satisfactory pr r pr ences this der used e shows validation wer by the on on (2007) In of e . and Culzoni, 73 wer the or d wer interfer A, sample study in
values Mincsovics, err M.J. based 2 ocessing based handle G quickly E. second-or e multivariate analysed , cia, epr This e a e can ALANT standar matrix As pr contents T Chemistry and Chemical Engineering Research Center of Iran, T Engineering Research Center of Chemistry and Chemical and made as
Gar samples 1 esented Zaray squar mor wer ecovery r G the the nuts. pr Gil assessed G. , eal 2 which Development and V Development r e been B Salemi, om and , opriate done The ections. 1 mean than range M.D. fr analysis aflatoxin A. B wer AC2 be H-Otta, have of corr Appr oot Zan, also pistachio r nuts. K. can factor De extracted ARAF of of P osough. ences: and shifts , V Determination of Aflatoxins in Iranian Pistachio Nuts fr of Aflatoxins in Iranian Pistachio Determination expensive Papp, -1 aflatoxins detection analytes E. M. M.M. ocess edictions Parallel pistachio less Mobile phase composition has been optimized so that the total run time is 6 min. Ther has been optimized so that the total Mobile phase composition pr concentration of spiked duplicate. time ngg terms pr for of concentrations matrix other Refer 1) 2) 3) POSTER
Quantitative Structure Property Relationships Study of Air to Liver Partition Coefficients for Volatile Organic Compounds Using Partial Least Squares and Artificial Neural Network
Zahra Dashtbozorgi1, Hassan Golmohammadi2 1- Department of Chemistry, Islamic Azad University, Science and Research Branch, Tehran, Iran 2- Department of Chemistry, Mazandaran University, Babolsar, Iran
Partition coefficient from air to liver is of importance in environmental, toxicological and pharmacokinetic modeling [1-2]. The air
to liver partition coefficient, Kliver or log (Kliver) is defined as:
éConc. of compound in liver, mol kg-1 ù K = ë û liver -1 ëéConc. of compound in air, mol dm ûù In the present work a quantitative structure property relationship (QSPR) study was performed to develop models which relate the
structures of 115 volatile organic compounds to their air to liver partition coefficients (log Kliver)[3]. These compounds were randomly divided into three groups: training, test, and validation set, which consist of 91, 12 and 12 molecules, respectively. Then the total of 420 electronic, geometric, topological and quantum-chemical descriptors was calculated for all molecules in the data set. The genetic algorithm-partial least squares (GA-PLS) algorithm was applied as a variable selection for training set, and then, selected subset of descriptor variables was used for generating PLS regression model. For finding a better way to depict the nonlinear nature of the relationships, an artificial neural network (ANN) program was written with MATLAB 7 that used the latent variables selected by PLS as inputs and its output is air to liver partition coefficients. After optimization of the ANN parameters, the
trained network (6:5:1) was employed for calculation of log Kliver for validation set to evaluate its prediction power. The values of the statistical parameters R, F and standard errors for the PLS and ANN models were calculated. The results obtained showed the ability of developed artificial neural network to prediction of air to liver partition coefficients of volatile organic compounds. Also result reveals the superiority of the ANN over the PLS model.
References:
1) M.H. Abraham, P.K. Weathersby, J. Pharm. Sci. 83 (1994) 1450-1455. 2) K. Zahlsen, I. Eide, A.M. Nilsen, O.G. Nisen, Pharmacol. Toxicol. 73 (1993) 163-168. 3) M. H. Abraham, A. Ibrahim, W.E. Acree Jr. Eur. J. Medic. Chem. 42 (2007) 743-751.
54 POSTER . of of 55 the and added various and complex range time of a was
espectively 3 r the extraction at ometry in fect (II), metals surfaces HNO
ef the -1 Cd L linear for variable The for . e a mol -1 esponse 1 arious r at gL wer used m understudy AAS). The with (F of 0.5 1022. 388. octylphenoxypolyethoxyethanol octylphenoxypolyethoxyethanol was graph espectively r variable in and , Urmia, Iran ) (2009) (2008) -1 one ometry acidified rich 70%, gL 77 solution formation 168 m parameters. . and calibration spectr the 533. phase alanta T method, Mater the Methanol 75% d. the aqueous (10-1000 (2008) operating to in involved of Hazar and Bezerra, 80%, 150 e J. . absorbtion conditions M.A. levels optimization Ni(II), 85%, Mater ocedur extracted occurring centrifugation. five d. atomic Soylakc, pr two and at eixeira, M. T and optimum a, Hazar ardast, B. Mehrara, M. Bahram ardast, B. Mehrara, obtained Cr(III) J. using flame done e min Gomes Najibi by analytical 10 surfactants wer , Faculty of Science, Urmia University , Faculty of Science, was Fe(II), ,A. L.S. Soylak, quantitatively 1. for on The ater Samples by Flame Atomic Absorption Spectr ater Samples by for ir M.
C -1 º and design analysis investigated method.Under (2002) gL and 40 Oliveira, m Niknama non-ionic . 72 Rajabi, its 5 to E. (PD), was nickel(II). R.V of both e of N. Samadi, M.R. V to H.R. for and ochem. cadmium mixtur limits prior experimental dioxime Niknamb, Micr Carvalho, extraction Ahmadi, on(II) the . similar ir de F The nickel, in , a,kh. phase abrizi, phenomenon Department of Chemistry A.L. very detection e ollahi ollahi heating design. omium, Bavili-T Response Surface Method for Simultaneous Optimization of V Optimization for Simultaneous Surface Method Response wer Baliza, with Shokr omium(III), A. ) Shokr ene-9,10-dine chr after Experimental Parameters in Cloud Point Extraction and Determination of in Cloud Point Extraction Experimental Parameters -1 parameters .X. cr P A. A. , gL for m values surfactant-rich composite X-114) Lemos, ences: phenathr Manzoori, Ghaedi, Ghaedi phase-separation Cd(II),Cr(III), Fe(II) and Ni(II) in W Cd(II),Cr(III), Fe(II) the .A. M. M. V J.L. riton The cadmium(II), with (T to experimental central optimum (10-2000 Recoveries Refer 1) 2) 3) 4) POSTER
Experimental Design for the Optimization of Cloud Point Extraction and Determination of Co(II), Cu(II) and Ag(I) by Flame Atomic Absorption Spectrophotometry
Naser Samadi, Mohammad Reza Vardast, Amir Chehrehgani, Morteza Bahram Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran
A new method for determination of copper, cobalt and silver cations in water samples by flame atomic absorbtion spectrometry (FAAS) after cloud point extraction (CPE) is proposed [1]. The method is based on the complexation of these cations with indolenin (2,3,3-trimethyl-3H-pyrrolo (3,2-h) quinoline) [2] in the presence of non-ionic micelles of Triton X-114. Phase separation was achieved by heating the mixture at 40 º C for 10 min and centrifugation. The surfactant-rich phase was diluted using methanol and injected to FAAS [3,4]. The effect of various experimental parameters such as pH, concentration of chelating agent and surfactant, equilibration temperature end time on cloud point extraction was investigated using two optimization methods, one variable at a time and central composite design. The experimental design was done at five levels of operating parameters. Results obtained for both methods were almost the same. Under optimum conditions the calibration graph were linear in the range of (10-2000mgL-1) with detection limits of 4mgL-1 for Cu(II) and (10-1000mgL-1) and 2mgL-1 for Co(II) and Ag(I), respectively. Recoveries for silver, copper and cobalt were obtained 90%, 87% and 65%, respectively.
References:
1) V.A. Lemos, M.S. Santos, G.T. David, M.V. Maciel, M.d.A. Bezerra, J. Hazard. Mater. 159 (2008) 245. 2) A. Rashidi, A. Afghan, M.M. Baradarani, J. Heterocyclic Chemistry 46 (2009) 428. 3) M. Ghaedi, A. Shokrollahi a,kh. Niknamb, E. Niknama ,A. Najibi a, M. Soylakc, J. Hazard. Mater. 168 (2009) 1022. 4) V.A. Lemos, P.X. Baliza, A.L. de Carvalho, R.V. Oliveira, L.S. Gomes Teixeira, M.A. Bezerra, Talanta 77 (2008) 388.
56 POSTER , 57 limits Cd(II) copper analytes and operating for the optimization of es of Zn(II) detection two levels factor with )
Cu(II), -1 using five L of abriz, Iran g at m , T quantitation enrichment for done 2 and investigated (10-2000 used was e of . econcentration wer was pr design abriz University range for Recoveries
, T flame the 1535. espectively in used r extraction , M.A. Farajzadeh 1 Cd(II). in (2009) was linear
38.6), and experimental 428. oextraction Followed by Flame Atomic Flame Atomic Followed by oextraction 161 e . acetylene-air The wer Zn(II) olo (3,2-h) quinoline) [2] was used as a chelating agent prior to [2] was used as a chelating agent prior olo (3,2-h) quinoline) method an (70%, (2009) Mater parameters , B. Mehrara 832. d. 46 1 olo (3,2-h) Quinoline by Experimental Design olo (3,2-h) Quinoline and both graphs using , Faculty of Chemistry design. for Hazar
(2008) (DLLME) -1 J. 50.7) ardast , L , Faculty of Science , Urmia University Urmia, Iran , Faculty of Science 75 Chemistry g m 21. experimental (90%, calibration alanta composite 0.5 T , M.R. V ocyclic , 1 ophotometry the oextraction (2008) and
various 54.1), Heter Mehr ) Ghorbani-Mehr 380 micr -1 J. central of
spectr B. rimethyl-3H-Pyrr gL m (96%, fect and N. Samadi conditions ef Ghorbani Zorita, S. B. time Baradarani, The Biochemistry a absorption liquid-liquid obtained (10-1000 [3]. at e M.M. optimum 1- Department of Chemistry Bahram, Bahram, and of wer atomic M. M. Analytical dispersive Afghan, 2- Department of Analytical Chemistry 2- Department of Analytical variable zinc Cu(II) a Under Sang, , A. Flame for and
H. one Absorption Determination of Cu(II), Zn(II) and Cd(II) Based on the Complexation of Cu(II), Zn(II) and Absorption Determination om water samples. Indolenin (2,3,3-trimethyl-3H-pyrr om water samples. Indolenin -1 L econcentration Reaction With 2,3,3-T Farajzadeh, Farajzadeh, study Optimization of Dispersive Liquid-Liquid Micr Liquid-Liquid of Dispersive Optimization ences: g pr Rashidi, m Liang, . 5 this M.A. A. P M.A. In cations fr extraction. after method, parameters. of cadmium Refer 1) 2) 3) 4) POSTER
Central Composite Design and Response Surface Methodology for the Optimization of Dispersive Liquid-Liquid Microextraction and Analysis of Organophosphorus Pesticides by High-Performance Liquid Chromatography
M. A. Farajzadeh1, M. R. Vardast2 1- Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran 2- Department of Chemistry, Faculty of Science. Urmia University, Urmia, Iran
A new dispersive liquid-liquid microextraction method has been established for extraction of three organophosphorus pesticides from water and fruit samples. The extracts were analyzed by liquid chromatography. Chloroform at microliter levels and methanol at milliliter levels were used as extraction and dispersive solvents, respectively [1,2]. Central composite design and response- surface methodology were used as experimental strategies for modeling and optimization. Some factors, including extraction and dispersive solvent type and volume, pH of sample solution and salting out effect were studied and optimized [3,4]. Under the optimum conditions, linearity was obtained in the range of 50 - 4000 mg L-1 with the detection limits of 2mg L-1 for fenitrothion and 3 mg L-1 for two pecticides, diazinon and ethion. The relative standard deviation for six replicate measurements of 500mg L-1 of fenitrothion, diazinon and ethion were 3.3% , 2.2% and 4.1%, respectively.
References:
1) F. Ahmadi, Y. Assadi, M.R.M. Hosseini, M. Rezaee, J. Chromatogr. A 1101 (2006) 307. 2) S. Berijani, Y. Assadi, M. Anbia, M.R. Millani Hosseini, E, Aghaee, J. Chromatogr. A 1123 (2006) 1. 3) M.A. Farajzadeh, M. Bahram, B. Ghorbani-Mehr, Talanta 75 (2008) 832. 4) M.A. Farajzadeh, M.R. Vardast, M. Bahram, Chromatographia 69 (2009) 409
58 POSTER e to 59 the the with wer in of was (RMSE) selection potential statistical PSO-MLR or emergent model nonlinearity quantitative work err the elation the the and e PSO-MLR the of for the ent consisting by corr inhibitory eliable and ed squar that r curr a study of the chance inspir to mean inhibitors is aim no edict stepwise-MLR Comparison is indicated oot pr stepwise-MLR r used of e and The construct to of the stepwise-MLR to ther also Integrase ehran, Iran [1,2]. is the used -1 over , T values technique. that Eberhart alidation used descriptors 1802–1806. 1807–1810. for V HIV PSO been The of and 19, 19, oss been 470-476. eight superior Cr with algorithms values 9, have is to indicated PSO-MLR series has echnology (2009) (2009) A Carlo one was Kennedy modeling. RMSE (2009) Lett. Lett. [3,4] coupled by 877-887. with drugs. strategies and technique
technique 2 Monte evolutionary ch scheme, PSO has characters of simple computation, rapid ch scheme, PSO has 39, Chem. Chem. R strategy baseline drugs. triazole PSO a oduced computing network The models Med. Med. or times other as (2008) inhibitor the e-Activity Relationship Study Relationship Study e-Activity intr soft e modeling to e modeling that inhibitor ent 1000 neural Bioorg. Bioorg. been , Sharif University of T , Sharif University of fer softwar -randomization Applied , Y Integrase technique. has dif oxadiazole show constructed compar -1 second the an stepwise-MLR artificial wo epeating Reddy Integrase and the T r HIV (PSO) Thompson, Thompson, The -1 the by M. Jalali-Heravi, H. Ebrahimi-Najafabadi M. Jalali-Heravi, H. B. B. for The engineering stepwise is J. J. . either HIV Prasad in some applying model. implement the D. omising one of others. anti and pr Allen, Allen, to G. Quantitative Structur Quantitative . e e splitting of -1 Integrase Inhibitors Using Particle Swarm Optimization -1 Integrase Inhibitors V obtained H. H. the over first . ar ching for food. As a stochastic sear ching for food. As a optimization P e S. S. cor Advances (QSAR) , espectively r addition, easier The PSO over wer modeling In esults of HIV Department of Chemistry of random r ds sear and swarm Murthy for 0.29, that descriptors R. . The V eatherhead, eatherhead, times superior Sierakowski, elationship and J. r W W , esponse. is A. r particle RMSE, compounds. G. G. method 100 C. capability superiority edictive of J. J. model. 0.38 the of pr for and
PSO of the 2 e-activity R Satapathy set class Johns, Johns, oxy-1,6-naphthyridine best 0.81, Coelho, ences: the descriptors A. A. method Ch. S. test this the S. L. B. B. esults, evealed The motion of a flock of bir motion of a flock of convergence apply 8-hydr structur of of four r r 0.72, behavior constructed of Refer 1) 2) 3) 4) POSTER
Utilization of Central Composite Design Method in the Optimization of a Chemiluminescence Reaction Parameters of Penicillin G Potassium Determination in Real Samples
M.H. Sorouraddin*, M. Fadakar-Sardroud, M. Iranifam, A. Imani-Nabiyyi Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
In recent years, multivariate optimization methods have been gradually replaced by univariate optimization methods. The advantage of multivariate optimization methods is facilitating the study of interaction effects of variable parameters on the experimental results. The present study is to introduce the utilization of the central composite design (CCD) method as a simple and selective tool for the
determination of penicillin G potassium (PGK) samples on the bases of the enhancement effect on H2O2-CTMAB chemiluminescence (CL) reaction in an alkaline medium. The relevant experiments were carried at room temperature (ca. 21°C) and at each optimization run, a 2.0 ml portion of the solution containing 1 × 10-2 M phosphate buffer (pH=11) and varying amounts of the cationic surfactant N-cetyl-N,N,N- trimethylammoniumbromide (CTMAB) were delivered into the reaction cell. Then, 1 mL portion of hydrogen peroxide solution
-2 with various H2O2 percent (w/v) was injected into the reaction cell followed by adding 40mL of 1×10 M standard PGK solution. The cover of the reaction cell was then immediately closed and the progress of the CL reaction continuously monitored by a luminometer and the results obtained were processed with MINITAB® software. Optimum levels for the parameters (CTMAB,
H2O2 and pH) were found to be 2.5 % (w/v), 7 % (w/v) and 11, respectively. The calibration graph was established at optimum levels of the parameters with varying microliter-sized volumes of 1 × 10-2 M PGK standard solution witch was at 1.245-124.5 mgL−1 PGK concentration range. The RSD for 5 replicates measurement (1 × 10-2 M) was found to be 1.40 % with LOD=0.02 mgL−1.
References:
1) B. Rezaei, A. Mokhtari, Flow-injection chemiluminescence determination of enrofloxacin using the Ru (phen) 3 2+– Ce (IV) system and central composite design for the optimization of chemical variables, Luminescence, 2008, 357-364 2) M.H.Sorouraddin, M.Iranifam, A.Imani-Nabiyyi, Chemiluminescence of penicillin V potassium at nanomole levels pharmaceuticals and spike human urine, CEJC, (2008) Accepted Maniuscript
60 POSTER is is of 61 the SDS, have of Results value
a and 0.1M pK fitting of component combination The Brij-35 ehran, Iran, ehran, Iran. constants. as the each ength spectral of str on such acidity ionic 7.30±0.11. whole of based is an the spectrum salt e and ehran Branch), T surfactantc C ◦ method pur using of 25 sodium and at determination ophotometric 2 for esolution Blue- centages r estimated a Spectr is per constant solutions AN; the T applied data, as DA acidity omothymol -Brij-35 equilibria Br on ogram , Samane Famili 1 of pr water Brij-35; , elated r influenced AN omothymol Blue-Sodium Salt omothymol Blue-Sodium T all value is
a pH-absorbance water of DA surfactant pK salt in the of constants; salt The fect of Surfactant Micelles on Micelles fect of Surfactant fect model. sodium constant Ef evaluate Amir H. M. Sarrafi Acidity o sodium eagent. The Ef T r Blue- . salt; analysis acidity this Blue- , Faculty of Science, Islamic Azad University (Shahr e Ray Branch), T Islamic Azad University (Shahr e Ray , Faculty of Science, The of , Faculty of Science, Islamic Azad University (Central T , Faculty of Science, factor Acidity Constant of Br Acidity Constant sodium concentration. omothymol Br applied. Blue- solution is omothymol Brij-35 ophotometrically of Br the established of to value an spectr
a easing to omothymol pK incr d-modeling added Br , constant the by data har ds: that determined 2- Department of Chemistry and discussed. acidity eased 1- Department of Chemistry espectively The
been soft- collected show r incr also Keywor POSTER
Application of ACA-PLS and GA-PLS for Simultaneous Spectrophotometic Determination of Thiophene, 2-Methyl Thiophene and 3-Methyl Thiophene
N.Farzin-Nejad1,2, E.Shams Solari1, M.K.Amini1, A.A.Miran Beigi2, V. Zare-Shahabadi3 1- Department of Chemistry, Faculty of Science, Esfahan University, Esfahan, Iran 2- Oil Refinery Research Division, Research Institute of Petroleum Industry, Tehran, Iran 3- Department of Chemistry, Faculty of Science, Shiraz University, Shiraz, Iran
Thiophene, 2-methyl thiophene and 3-methyl thiophene are sulfur compounds, which appear together in many real gasoline. In most complex samples UV-Vis spectral overlap is often a serious problem. Several chemometric method have been used for resolving Overlapping signals, calibration and model identification. The predictive abilities of partial least squares regression (PLS), principel component regression (PCR), genetic algorithm-partial least squares regression (GA-PLS) and Ant colony-Partial least squares regression (ACA-PLS) were examined for simultaneous determination of three studied thiophenes. In this paper, the ACA- PLS and GA-PLS show superiority over than PLS and PCR methods. The ACA-PLS possesses a great ability to find best subsets of wavelengths, at a short period of time small PRESS values, via accumulation of information in the form of pheromone trails deposited on each wavelength.
Keywords: Thiophen, 2-methyl thiophene, 3-methyl thiophene, Gasoline, Ant colony – PLS, Genetic algoritm – PLS, Spectrophotometric
References:
1) M. Esteban, C. Arino, J. M. Diaz – Cruz. Trends in Analytical chemistry. 25, 1 (2006) 86. 2) M. Maeder, Y. M. Neuhold, G. Puxty. Chemometrics and intelligent laboratory systems. 70 (2004) 193-203. 3) S. Riahi, M. R. Ganjali, p.Norouzi, F. Jafari. Sensors and Actuators B. 132 (2008) 13-19. 4) M. Shamsipur, V. Zare- shahabadi, B. Hemmateenejad, M. Akhond. Journal of chemometrics. 20 (2006) 146-157. 5) M. A. Alonso Lomillo, O. Dominguez Renedo, M. J. Arcos Martinez. Analytica chimica Acta 449 (2001) 167-177. 6) H. Abdollahi, M. Shariat Panahi, M. R. Khoshyand. Iranian Journal of pharmaceutical Research (2003) 207-212.
62 POSTER , 63 AN T e also media elated r DA 1-6 all espectively r of (2008) micellar model. o-phenyleazo)- pH-absorbance 70 SDS, A the -SDS and Part constants analysis ehran, Iran o-2-(2-nitr water Acta evaluate X-100 and factor acidity o T 5-Nitr . riton of The T ochimica X-100 as e spectrum of each component ar Spectr values established riton
294. such a ehran Markazi, T applied. p -T an is pK 1158-1165 to ophotometrically 2003, water the 163. , K.Alizadeh , , (2008) surfactant data 660-664 343–349 71 that spectr a o-Phenyleazo)-Phenol,(4-e) (1997) water d-modeling Publisher 35 in (2006) Part 335. (2008) anionic har show 64 K.Haghbeen collected 70 a , an obiol. Acta A 85. Bartlett riton X-100 Micellar Media Solutions riton X-100 Micellar (1999)106. (2002) ophotometric Determination Determination ophotometric the determined part -T Micr and L7. Results Part and o-2-(2-Nitr 49 of soft-and 455 1706–1710. (2002) Rouhani acta ochimica ater of Food been Acta S. Jones , J. (1995) Acta alanta 532 T neutral 705–710. fitting 51 (1996) a Int. Spectr have en, A constants. ochimica 68 of Chim. M Edition, Chem. ochimica (2002) d Nygr Acta J. 0.1 spectral 56 Anal. Spectr Moraga, combination Thir Chem. fect of surfactant on acidity constants and pur fect of surfactant on acidity constants and o-phenyleazo)-phenol,(4-e) o-phenyleazo)-phenol,(4-e) , faculty of Science, Azad university of T , faculty of Science, , Spectr acidity oanal. of E. A.Mohajeri , Hemmateenejad centages ochim. the of Mohammad Ghalei, Amir Hosein Moohsen Sarafi Mohammad Ghalei, alanta Anal. B. ater SDS and W T Kubista, whole Electr per on Ibarra, J. Elbergali, ength M. Spectr J.Ghasemi, Multiwavelength Spectr Multiwavelength Chemistry , C. , W the A. Jing, str , the eagent. Ef o-2-(2-nitr ov L. Kubista, as S.Saaidpour based Maddah, ater Petr M. ionic Bunel, . using 5-Nitr Kubista, Organic V M.R.Davoodabadi Lipkowski, of Acidity Constant of 5-Nitr of Acidity Constant Kubista, S. , , an J. determination azdanipor of M. Xiaoyan, in W M. Li, L. A.Y Ghasemi, method nein en, for and N. j. influenced Andrade, Gorgov , Bozorgmehr Bunton, C Niazi, ◦ e , Fan, estimated Nygr G. ar L. , A. Department of Chemistry J.M. 25 J. e J.K.whitesell, constants C.A. J.Zolghar ar at applied M.Ghalie, , en, esolution r ences: a uanqin, Antonov Shamsipur Nygr Y Niazi Blasco, Lachenwitzer acidity Sjoback, Ghasemi, J. L. M.A.Fox, Z. M. A.Niazi, M.B.Gholivand., A. A. A. J. R. ogram 2) The solutions data, equilibria pr phenol,(4-e) added to the solution of this r investigated. Refer 1)
3) 4) 5) 6) 7) 8) 9) 10) 11) 12) POSTER
Determination of Acidity Constant of 2-(2H-Benzo[d] [1, 2, 3] Triazol-2-yl) Phenol in Water and Micellar Media Solutions
Amir H. M. Sarrafi, Negin Ghorashi Department of chemistry, Islamic Azad University-Central Tehran Branch, Tehran, Iran
The acid dissociation constant is an important physicochemical parameter of a substance, and knowledge of it is of fundamental importance in a wide range of applications and research areas [1]. In this work, the acidity constant of 2-(2H-benzo[d] [1, 2, 3] triazol-2-yl) phenol (C ) in water and water-Brij-35 solutions at 25 C and an ionic strength of 0.1M have been determined F ◦ spectrophotometrically. To evaluate the pH-absorbance data, a resolution method based on the combination of soft- and hard- modeling is applied. The acidity constant of all related equilibria is estimated using the whole spectral fitting of the collected data to an established factor analysis model. DATAN program applied for determination of acidity constants. The acidity constant of all related equilibria are estimated using the whole spectral fitting of the collected data to an established factor analysis model.
DATAN program applied for determination of acidity constant. The pKa value of CF is 8.50±0.10. The pKa value is increased by increasing Brij-35 concentration. Effect of surfactant on acidity constant and pure spectrum of each component is also discussed.
Keywords: 2-(2H-benzo[d] [1, 2, 3] triazol-2-yl) phenol; Acidity constant; Brij-35; DATAN; Spectrophotometry.
Reference:
1) Sandra Babic; Alka J.M. Horvat; Dragana Mutavdžic Pavlovic; Marija Kaštelan Macan, TrAc Trends in Analytical Chemistry, 26 (2007), 1043- 1061.
64 POSTER is 65 ionic each based whole acidity Brij-35 value of an
a of as the pK and method such C ◦ The using ehran, Iran 25 spectrum e at pur determination esolution r -SDS estimated surfactants a for 6.35±0.10. is and of is ater solutions 2 , Gachsaran, Iran. data, ehran Branch), T applied Purple media equilibria constant centages esol per ogram elated pr r micellar acidity the all omocr -Brij-35 and W pH-absorbance AN as on T ophotometric Br of -SDS the DA of ater Spectr water , W , Mahboobeh Nimroozi 1 value
constant model. influenced a surfactant AN; evaluate and T ater is o pK of T DA . acidity The analysis fect Purple -Brij-35 SDS; Ef The esol factor water eagent. , r , Negin Ghorashi 1 Brij-35; applied. omocr this is ophotometrically water esol Purple in W Br of in ophotometric Determination of Acidity Determination ophotometric , Faculty of Engineering, Islamic Azad University , Faculty of Engineering, of concentration. established spectr constants; an omocr value SDS Purple
solution a Spectr to d-modeling , Faculty of Science, Islamic Azad University (Central T , Faculty of Science, pK Acidity and the esol har data Amir H. M. Sarrafi to the determined and omocr Purple; Brij-35 that Br been added soft- collected , of esol of Constant of Br show discussed. the easing have of omocr also incr 2- Department of Chemistry Br is 0.1M constant Results espectively by r of ds: fitting combination SDS, acidity 1- Department of Chemistry eased the ength The str on spectral constants. and incr component Keywor POSTER
Monitoring of Some Pesticides in Water Samples With SPE- HPLC Method Including an Uncertainty Estimation of the Analytical Results
A. Ghorbani1, F. Aflaki2, M. Aghaei1 1- Department of chemistry, Islamic Azad University, Saveh Branch, Saveh, Iran 2- Department of chemistry, Islamic Azad University, North Tehran Branch, Tehran, Iran
This work describes measurement uncertainty estimatation for determination of Phosalone and Diazinon in natural water sample by Solid-Phase Extraction and High Performance Liquid Chromatography (SPE-HPLC). A solid phase extraction method was
applied with retention of analytes on C18 column to allow preconcentration of the target analyt from water sample. Determination
of analyst was carried out in HPLC with C18 column using Acetonitril-Water (60:40) as a mobile phase with a flow rate of 1mL/min. The influence of various analytical parameters including the amount of solid phase, pH, volume of sample solution and volume of eluent on the extraction efficiency of analytes was investigated. The influence of pH on the solid phase extraction of pesticides was studied in the range of 2-7. Each pH value was tested more than three times. Quantitative recoveries were obtained in the pH 4 for pesticides we study. The recoveries of the pesticides from different volumes of aqueous model solution containing the same amounts of the metal ions were tested in the range of 50-500 ml. The recoveries were found to be stable up to 400 ml of sample volume. The highest preconcentration factor was found to be 40 according to 2.5 ml of the final solution. In order to study the influence of eluent in solid phase extraction of pesticides, acetonitrile and methanol, were simultaneously studied for eluting volumes between 2.5-10 ml. Result has shown those efficient pesticides elution are reached under 2.5-10 ml volume of both solvent but using the acetonitrile prepare higher recoveries. So, we chose the acetonitrile with the lowest volume of 2.5 for this study. To estimate the uncertainty of analytical result obtained, we propose assessing trueness by employing spiked sample. Two types of bias are calculated in the assessment of trueness: a proportional bias and a constant bias. We applied Nested design for calculating proportional bias and Youden method to calculate the constant bias. The results we obtained for proportional bias are calculated from spiked samples.
References: 1) A.B.Vega., A.G. Frenich., J.L.M. Vidal., Anal. chim. Acta, 538 (2005) 117-127. 2) S.R. Mozez, Alda M.L.D., Barcelo D., J. Chromatoger A., 1045 (2004) 85-92. 3) Y. Liang, D.Yuan, Q.Li, Q. Lin, Marine. Chemistry, 103 (2007) 122-130. 4) A.D. Muccio, S.Girolimetti, D. A. Barbini., J. Chromatoger A., 833 (1999) 61-65. 5) N. Makinata, T.Kawamoto, Teranishi K., Anal. Sci., 19 (2003) 543. 6) I. Toloso, J.W.Readman, L.D.Mee, J. Chromatoger A., 725 (1996) 93-106. 7) R. Boque, A. Maroto, J. Riu, F.X. Rius, Grasas y Aceites, 53 (2002)1: 128-143. 8) A. Maroto, R. Boque, J. Riu, F.X. Rius, Anal Chim Acta, 391(1999) 173-185.
66 POSTER J., to of to 67 for the low The D. non- oblem. aims 56 number multiple nion pr their biological 1, of model. and attached context, Mar
analogues der and and SE=0.3937 H transcriptase. D., or [mM] this medical stepwise of In design alfa, oaches the obtained Farina
everse pIC90 r to ., F=16.36, generation chemical of index F drug of specificity Then D. enzyme. worldwide the e. their charge second edibility attached potency of R=0.804, Labriola , Iran cr
the H summarize ., softwar hastens the P as and , non-nucleoside the to challenging transcriptase of ed set edict topological because CHR=, pr Dragon Johnson activity most e: to R., everse ar r the consider activities the training investigate -1 by volume, attempting 2004. -1 of model for Stryker HIV virus, a biological which importance aals HIV 1-8, J., one -1 W d further mainly is and anti HIV to der e of technique eatest an Stanfor gr a SE=0.3236 networks. V ovided (AIDS) developed inhibitoring used D., descriptors pr as e the K-103N we structur on ediction ome neural Siest wer pr atomic the seven , University of Mazandaran, Babolsar , University of Mazandaran, A., in by on syndr (QSAR), gained F=109.665, depend artifitial has between TI) descriptors algorithms . Rachlis method model ., 0.85, AIDS (NNR (2006) e selection and developing of MLR model. The best model was selected based on the e selection and developing of MLR model. this K.T model QSAR R= factor -1 Activity of Non-Nucleoside Inhibitors by QSAR Appr Inhibitors by of Non-Nucleoside -1 Activity D., inhibitors of 24/weighted of of elationships molecular r eating Mohammad Hossein Fatemi, Zahra Ghorbannezhad Mohammad Hossein elationships .This tr r ashiima T ophilic inhibitors
y-scrambling immunodeficiency for etical means SEs J., Faculty of Chemistry
Chergaoui suitability hydr ed statistics By ez and A., e-activity theor transcriptase and the generate the AD F of 1865.1999. e applied for featur test acquir to ZY strategies has R, transcriptase 34, function, structur D., 3D-MoRSE—signal the pool everse der r ent indicate of a or ediction of Anti HIV ediction of compounds. Med. Morales-Ramir in model Pr curr everse J. [1]. S, r step villemin set. essions wer esults [2]. r values the these L., derivatives oss-validation Engl. egr first ences: eatment of enz cr quantitative ea optimal tr N., ur the Stazewski Douali ediction The Most non-nucleoside cytotoxicity The information develop nucleoside Efavir In linear r optimal C1(sp3)/CO(sp2), of The pr Also obtained Refer 1) 2) Ruiz POSTER
Optimization Of Theoretical Plate Heights in Chromatography
Kiumars Ghowsi, Hossein Ghowsi 1- Assistant Professor, Department of Chemistry, Islamic Azad Majlesi Branch, Isfahan Iran 2- Department of Mathematics, Najaf Abad, Payam Noor University Isfahan, Iran
Mathematical form of the behavior of chromatographic columns began with the studies in the 1950,s by Dutch chemical B engineers and led to Van Deemter equation which can be written in the form H = A + + C m where H is the plate height, m is the m linear velocity of the mobile phase. A,B,C are coefficients related to the phenomena of longitudinal diffusion, and mass transfer between phases, respectively. The optimum velocity for the minimum H is obtained by taking the first derivative of Van Deemter B equation and setting it equal to zero. The optimum velocity is m = . C In capillary Electrophoresis, which is relatively new type of chromatography, an open tubular column reduces plate height. By eliminating term A in the Van Deemter equation. Capillary electrophoresis reduces plate height further by knocking out the term C in the Van Deemter equation that comes from the finite time needs for solute to equilibrate between the mobile and stationary m B phase. The only fundamental source of broadening controlling theoretical plate height is H = in the Van Deemter equation. By m B taking the derivative of H for Capillary Electrophoresis and setting it equal to zero, one obtains - = 0 . m2 B According to this result where approaches infinity - goes to zero. But physically speaking velocity, of electrolyte can not m m2 m approach infinity but this result suggests that in higher velocities the theoretical plate height H decreases.
References:
1) Skoog, D.A Principle of Instrumental Analysis , Saunders college Publishing , New York,1985 2) Jorgenson , J; Lukacs , K.D. Anal . Chem.1981,53,1298 3) Ghowsi, K; Foley , Joe P; Gale, R.j .Anal . Chem. 1990,62,2714-2721 4) Dunn,C,D;Hankins, M.G; Ghowsi,K.Sep.Sci. Technol,1994,29(18)2419-2433 5) Terabe,S;Otsuka,k;Ando,T.Anal.Chem.1985,57,834-841 6) Terabe,S;Otsuka,K;Ando,T.Anal.Chem.1989,61,251-260
68 POSTER . In an 69 and and . After operty power Results Then . applying ANNs. randomly opagation e-pr operty pr otation. was r using ediction or topological charge. selected set pr back-pr quantitatively structur e espectively r its by optical This values. wer activity polymers atomic sets [2]. was der 3), molecular volume, est 0.325, encoded geometric, evaluate e set test ,Iran ar inter quantitative to and output onic, negative and data descriptors as the set experimental its a is , Esfahan, Iran as 0.286 electr 2 biodegradable train the most and such molecules 420 with and of important some validation using network 0.247, inputs of selected of of e echnology as The the ar chemistry inertia was of total eement trained set. of of sets otation otation agr r r 1996. The was ea data output characteristics ar descriptors MA, good polymers sets. , Zahra Hassanzadeh the 1 optical optical and moment (6:6:1) 90 in validation of wide have these the of e: heat of formation, kier shape index (or Boston, a ANN structural of and of , University of Mazandaran, Babolsar , University of Mazandaran, to used 3438. validation (PPSA3), the PWS, network otation test r molecules , Esfahan University of T , Esfahan University and ea nal input calculation collection 7 all ediction 558. ar (2005) a as applied pr Design, for studies, for 26 exter of optical act TLAB training, (2006) and of esis, constructed been surface and MA 46 these the QSPR Modeling of Optical Rotation for of Optical QSPR Modeling Network Hassan Golmohammadi otation In the r Sci. employed test ophor values for with have calculated [1]. descriptors modeling Neural positive was Electr Engin. was to optical Beal, written training, Biodegradable Polymers Using an Artificial Neural Network Using an Artificial Neural Biodegradable Polymers (ANNs) These the was calculated partial M. 1- Department of Chemistry parameters, Fatemi, Polym. was network calculations the 2- Department of Chemistry oups, of goal, study M.H. ANN investigations descriptors gr that Demuth, networks ors Kowsari, this this trained the ee weighted descriptors. network E. err of , of H.B. thr as (QSPR) The d aim neural showed achieve [3]. neural into charge Hagan, ences: to . named standar Golmohammadi, main Malekpour e H. S. M.T der elationship Artificial r wer The or divided quantum-chemical stepwise variable selection techniques. These descriptors ar stepwise variable selection techniques. These atomic artificial optimization strategy The obtained Refer 1) 2) 3) POSTER
Prediction of Inherent Viscosity for Optically Active Polymers from the Theoretical Derived Molecular Descriptors
M. A. Farajzadeh1, M. R. Vardast1, Hassan Golmohammadib2
1- Organic Polymer Chemistry Research Laboratory, Collage of Chemistry, Isfahan University of Technology, Isfahan, Iran 2- Department of Chemistry, University of Mazandaran, Babolsar,Iran
Polymers with optically active properties have found interesting application such as chiral phase for enantiomeric separations in chromatography methods or chiral media for asymmetric synthesize [1]. Inherent viscosity of an optically active polymer is the
( ln η r ) ratio of natural logarithm of the relative viscosity, hr to the mass concentration of the polymer, C, i.e.: η = inh C The main aim of the present work was development of a quantitative structure-property relationship method using an artificial neural network (ANN) for the prediction of inherent viscosity of a dataset of 100 optically active polymers. The dataset of inherent viscosity was taken from the values reported by Malekpour et al. [2,3]. First the data set was randomly splited into three separated section; the training, test and validation sets, consist of 70, 15 and 15 members, respectively. Then the total of 340 descriptors was
calculated for all molecules in the data set. In the next step an ANN was constructed and trained for the prediction of hinh of polymers. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) feature selection techniques. These descriptors are: molecular weight (MW), randic index order 3
3 ( c), net atomic charge of C atom (QC), energy of highest occupied molecular orbital (EHOMO) and polarizability (a). The values of standard errors for training, test and validation sets are 0.176, 0.217 and 0.198, respectively. Comparison between these values and other statistical values reveal the superiority of the ANN model over the MLR one.
References:
1) K. Soai, S. Niwa, Chem. Rev. 92 (1992) 833. 2) S. Malekpour, A.R. Hajipour, S. Habibi, Pol.Sci. 44 (2002) 119. 3) S. Malekpour, E. Kowsari, Polym. Engin. Sci. 46 (2006) 558.
70 POSTER e as 71 -to- ence wer ) and (often m elation model. used efr positive r partition (GA-PLS) e water validation superiority corr descriptors es om wer fr the and the partial the descriptors esentation squar test constructed edict selected taken eveal epr r r pr least the The develop was descriptors weighted to of molecular to training, values oaches these used for ehran, Iran numerical charge , Iran quality These , T ors -to-polydimethylsiloxane -to-polydimethylsiloxane 1995. step was The descriptors. compounds the err statistical it 162. performed 2 d atomic water next algorithm-partial best other , Babolsar was the the fecting ANN, calculated. organic (2007) Gainesville, their e af In standar and of descriptors. genetic 155 of 1175 wer fractional select and A, method Florida, the factor to values (MAC). of about values training of used molecular (PPSA-3), these (QSPR) The and set descriptors the , Zahra Dashtbozorgi was ea 1 important compounds University molecule
set. ar by ent ehran Branch, Azad University ehran Branch, Azad Subsequently Chromatography the fer ), molecular volume (MV), total dipole moment of molecule ( ), molecular volume (MV), total dipole moment diverse most between of , Mazandaran University min in a dif method [2]. Manual; elationship organic surface the r of esented 3) -to-Polydimethylsiloxane Partition -to-Polydimethylsiloxane is validation PLS optimization of e a epr work, Journal orbital r , Central T nal Training ater operty this and After charged Comparison (version consisted in often exter . structur e number e–pr e tool of Abraham, ar molecule H. a CODESSA eat (ANN). Hassan Golmohammadi gr studied of positively M. softwar structur ficient chemical espectively of Jr., selection r investigation coef the one. es Carelson, ediction of W ediction partial network compounds of Acree this M. Pr 1- Department of Chemistry PLS molecules E. 0.193, ficient for Some Organic Compounds Using QSPR Appr Organic Compounds Using QSPR ficient for Some variable CODESSA for contribution a W. the structur the quantitative neural partition and model, set as of weighted Labadov, 2- Department of Chemistry Coef using the over descriptor) data Proctor, work, QSPR V.S. 0.186 a A. artificial applied charge The model diversity antibonding ea (FPSA-3), minimum atomic partial charge (Q ea (FPSA-3), minimum atomic partial charge an esent of was between 0.152, computed pr obtain molecular of e Katritzky, ANN ences: o atomic ficient. ar Sprunger, T the the L. A.R. e: In models coef [1]. called Because mainly method ar surface ar maximum input polydimethylsiloxane sets of 1) Refer 2) POSTER
Quantitative Structure-Property Relationship Study of Electrophoretic Mobilities of Some Organic and Inorganic Compounds Using SVM
Nasser Goudarzi1, Mohammad Goodarzi2,3, M. H. Fatemi4 1- Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran 2- Department of Chemistry, Faculty of Sciences, Azad University, Arak, Iran, 3- Young Researchers Club, Azad University, Arak, Iran 4- Faculty of Chemistry, Mazandaran University, Babolsar, Iran
In this work, two chemometrics methods were applied for the modeling and prediction of electrophoretic mobility of some organic and inorganic compounds. The stepwise multiple linear regression method was used to select descriptors which are responsible for the mobility of these compounds. Then support vector machine (SVM) and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure–activity relationship models. The obtained results using SVM were compared with MLR which revealed that the SVM model was much better than MLR model. The root-mean-square errors of the training set and the test set for the SVM model are; 0.1911, 0.2569 while for MLR model are; 0.4908 and 0.6494 respectively. Results have shown that the SVM drastically enhances the ability of prediction in QSAR studies superior to multiple linear regressions.
Keywords: Quantitative structure-property relationship; Support vector machine; Electrophoretic mobility; Multiple linear regression
References:
1) Nasser Goudarzi, Mohammad Goodarzi; Mario. C. U. Araujo, R. K. H. GALVA ; J. Agric. Food Chem. 2009, 57, 7153–7158 2) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2008, 106, 2525–2535 3) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2009, 107, 1615–1620
72 POSTER is as 16 73 the and oph- to such with ession spectr conditions, 0.0907 subsequent egr r ocess e applied pr and and wer designed (PLS) Zr was was es (ARS) ehran, Iran and S experimental set detection U , T extraction squar method for Red and least The optimum calibration phase echnology Alizarin the (RMSEPs) simultaneous , Kermanshah, Iran partial 2 tested. with to Under also using separation edictions experimental X-114 pr cations was the of The or riton solution T optimized. these oosi University of T err fecting cations e of af and , Beshare Hashemi 1 cations. and
121. squar aqueous both surfactant eaction r studied (2004) om for anions mean fr 201. parameters 510 L-1 been oot r 1. nonionic ophotometric Determination of Uranium of Uranium Determination ophotometric some mg (2003) Acta, have The conium of the samples. chemical 0-3 141 (2002) zir complexation es. eal of Chim. 72 time r fect The Jahanbakhsh Ghasemi J. using Acta. ef the in and of Anal. Zr mixtur range on 1168. fect R., ence ocess oducts. and ef the H. pr pr U in (2005) uranium based Microchem. Microchim. of of and 65 is interfer Seraji, of (CPE) conium Using Cloud Point Extraction and Multivariate Methods Point Extraction and Multivariate conium Using Cloud component linear N., e Simultaneous Spectr Simultaneous The two . Talanta 2- Chemistry Department, Faculty of Sciences, Razi University 2- Chemistry Department, wer method extraction Bayili-Tabrizi, Bayili-Tabrizi, for and Zir Niazi, A. A. extraction determination The concentration Shahabadi, A. 1- Chemistry Department, Faculty of Sciences, K.N. T 1- Chemistry Department, graphs J., determination espectively r solutions ences: Manzoori, Manzoori, eagents r Ghasemi, cloud-point Ghasemi, J. J.L. J.L. A otometric investigated. micelle-mediated pH, calibration sample 0.1117, simultaneous Refer 1) 2) 3) 4) POSTER
Simultaneous Determination of Paracetamol, Phenylephrine Hydrochloride and Chlorpheniramine Maleate Using Partial Least Squares-1 (PLS-1) Regression
Abdolraouf Samadi–Maybodi, Seyed Karim Hassani Nejad–Darzi Analytical division, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran
A mixture of paracetamol (PAR), phenylephrine hydrochloride (PHE) and chlorpheniramine maleate (CLP) is widely used in diseases such as the common cold [1]. Determination of PAR, PHE and CLP in pharmaceutical preparations with chromatographic method was carried out by Onur et al. [2]. Also, Jasionowska et al. [3] performed a micellar electrokinetic capillary chromatography (MEKC) method for determination of above drugs in pharmaceutical preparations. The conventional spectrophotometric methods use a discrete number of wavelengths that frequently are not enough to provide the necessary information to resolve a system with severe spectra overlapping [4]. It is possible to use multivariate calibration for quantification of the analyte in the presence of the other compounds. Among different regression method for multivariate calibration, the factor analysis based methods including partial least squares with one dependent variable (PLS-1) have received considerable attention in the literature [5]. In this work, simultaneous determination of PAR, PHE and CLP with minimum sample pre-treatment and without analyte separation has been successfully achieved by PLS-1 regression. Data of analysis were obtained from UV-vis spectra of the above compounds. The method of central composite design was used for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Results obtained from this method indicate that the minimum mean values of relative errors are 2.85, 4.07 and 4.85 for PAR, PHE and CLP. Also, limit of detection with PLS1 was 0.030, 0.220 and 0.078 mg L–1 for PAR, PHE and CLP, respectively. The procedure was successfully applied to simultaneous determination of these compounds in pharmaceutical tablets.
References:
1) Remington: The Science and Practice of Pharmacy 20th ed. University of the Science, Philadelphia, 2000. 2) M. Palabyk, F. Onur, Chromatographia 66 (2007) S93–S96. 3) F. Buiarelli, F. Coccioli, R. Jasionowska, A. Terracciano, Electrophoresis 29 (2008) 3519–3523. 4) C. Bosh Ojeda, F. Sanchez Rojas, Anal. Chim. Acta 518 (2004) 1–24. 5) B. Lavine, J. Workman, Anal. Chem. 78 (2006) 4137–4145.
74 POSTER 3 of 75 for like and of that silico mean elation in drug consist descriptor ox total power Corr of known R=0.863 model. of is consisting It which and MLR ADME/T value (ANN). ediction Abraham of solubility model pr the (a ched. the B that SE=0.87 the sear and compounds network is moved and over selected aqueous index), of set The have ANN , Iran neural organic investigate ograms. Then stepwise multiple linear ograms. Then stepwise espectively the r compound of model. design ediction connectivity pr calibration the further artificial 0.84, drug-like in oplus pr of to QSAR the of and a for molecular of superiority set used solvation model 0.91 e activity to the function-6.0/unweighted). opagation this wer pr divers e 0.92, rational of R=0.887 3711-3719 of e a . optimal
showed back esponds and construction for wer an (2007) ocedur distribution for corr e pr and 15 suitability solubility solubility models 533-545 wher industry SE=0.78 the (radial inputs e , University of Mazandaran, Babolsar , University of Mazandaran, parameters ar ANN as descriptor e calculated by DRAGON and pr e calculated by DRAGON Chemistry aqueous (2007) aqueous (60u) descriptors of these 59 evealed y-scrambling on r Med. used RDF model edict sets e and pr Mohammad Hossein Fatemi, Afsane Heidari Mohammad Hossein eening And development, Review MLR esults pharmaceutical to r wer topological scr between test Bio. the to basicity), J. drug used validation in Delivery of e ediction of Aqueous Solubility of Some Organic Compounds of Some of Aqueous Solubility ediction Faculty of chemistry simultaneously . obtained (dragon bond and obtained Bellera, wer Drug of validation descriptors C. Comparison stages employed test The ogen X1sol oss ANN developed depends cr e: alevi, was T espectively these hydr early ar r and Advanced 0.436. A. In Silico Pr In Silico model. drug J. training, the parameters set, a oaches of step, was MLR which Ertl, of to overall for method . or P , appr next err n work ences: Duchowicz, e eflect Leave-one-out r ficient Faller statistical ession validation the .R. this P B. egr obusthness Moder evaluation absorption In 145 molecules. Some molecular descriptors wer 145 molecules. Some r descriptors that The the In coef squar Also r compounds. Refer 1) 2) POSTER
Artificial Neural Networks and Least-Square Support Vector Machine Applied for Simultaneous Analysis of Mixtures of Nitrophenols by Conductometric Acid-Base Titration
Gholamhossein Rounaghi*, Roya Mohammad Zadeh, Tahereh Heidari Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
Nitrophenols find wide application in industry, agriculture, medicine, and military science. Industrial production of polynitro- aromatics over several decades has resulted in contamination of soil, ground, surface, and wastewaters. Since most nitrophenols enter the environment during manufacturing and processing the analysis of phenols and substituted phenols in natural waters and effluents is of prime importance for environmental control. These compounds have toxic effect on humans, animals and plants and they give an undesirable taste and odor to drinking water, even at very low concentration [1,2]. Sanitary classifications consider them among compounds of high and moderate toxicity. In the course of production, storage and application, nitrophenols may enter into the environment and hence present a hazard to human health [3]. The detection of nitrophenols is usually carried out by chromatographic techniques sometimes associated to mass spectrometry for identification [4, 5] and these methodes are very expensive. Conductometry is a relatively inexpensive, simple, and accurate method, and recently some determinations with this method and chemometrics has been reported. [6,7] In this work we used multilayer feed-forward artificial neural networks (ANN) [9] and least-squares support vector machine (LS-SVM) to model the complex non-linear relationship between the concentration of 4-nitrophenol, 2,4dinitrophenol and picric acid in their mixtures and the conductance of solutions at different volumes of added titrant. The results obtained by these methods and partial least squares (PLS) show that the excellent model was build using LS-SVM or ANN with low prediction errors and superior performance in relation to PLS. These procedures allow the simultaneous determination of analyts in synthetic samples with satisfactory results.
Reference: [1] “Patty’s Toxicology”, John Wiley & Sons: New York, 2000, Volume IIB, p. 980. [2] P. Cooper, “Explosives Engineering”, Wiley-VCH, 1996, p.33. [3] V. K. Shormanov, J. Analytical Chemistry,2002, 57, 130. [4] P. Mussmann, K. Levsen, W. Radeck, Fresenius J. Anal. Chem. 1994, 348, 654. [5] M.I. Turnes, M.C. Mejuto, R. Cela, J. Chromatogr. A, 1996, 733, 395. [6] H.G. Coelho Lucia ,G.R. GUTZ Ivano , Talanta, 2006, 69. 204. [7]R. Ghorbani ,J. Ghasemi, B. Abdollahi ,J Haza. Mater., 2006,131,13. [8] N.Chen, “ Support vector machine in chemistry”, World Scientific Publishing, 2004. [9] F. Despagne, D.L.Massart,Analyst, 1998, 123,157R.
76 POSTER is to of or of 77 the tap and was ratio
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in 44: -1 car ection-Partial Least Squar ection-Partial Least ultra good W New The compounds least used ml 2007: in beneficial measur e and and the metals. a ess, 1998: ng ar of Pr of samples determination alanta agent. T trace plays Syst., (toxic beryllium M, eplicate 17:149. at ater Using Mean Centering of Ratio Spectra ater Using Mean which r technique Plenum 10-600 Lab. and (IARC) ection-partial and six toxicology of synthetic Int. 2000: esults chelating 5 harmful Bahram r the for corr a 275. , Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran Ferdowsi University of Mashhad, Mashhad, , Faculty of Science, to beryllium ocessing on Cancer ophotometric [1-3]. as and mammals. range onutrient of gave d plays E epr in 108: beryllium Contam. on signal Zeinab Rohbakhsh, Akram Hajinia, T Zeinab Rohbakhsh, Chemom. pr ophotometric Determination of T Determination ophotometric the of used micr J, spectr ch a deviation also hazar a method 1992: it d Addit. Handbook is toxicity as for content over was methods, PLS VB, say Ohman Acta Resear Spectr , Food humans e. Under the optimized conditions, analytical characteristics of the MCRS method wer e. Under the optimized conditions, analytical F Bozorgzadeh Metal orthogonal to the 10.5 , B, standar ouk ection linear T to for en V is TD, , beryllium methods oposed ochim. and corr Beryllium in Natural W Beryllium in Natural pH= occupational esponding GF ocedur was pr risks Szteke that Lindgr at Mikr that an elative , r corr applying Luckey H, Agency is K, Method and Orthogonal Signal Corr Method and Orthogonal both Madrakian Department of Chemistry B, signal curve for the Norberg The A, HAMG, and Antti cancer Thorin L, Baksi known (MCRS)[5] S, cinogenic using in ences: , BK, chemometrics national car old well enugopal aessen Pal V Friberg V Afkhami W esent in such pr ediction is wo esult It r pollution The Inter determine T spectra orthogonal studied. pr calibration achieved. pr water Refer 1) 2) 3) 4) 5) 6) POSTER
QSAR Study of Some Anti Fungous Benzofurans Using Artificial Neural Networks
Zakieh Izakian School of Chemistry, University College of Science, University of Tehran, Tehran, Iran
Benzofurans and their analogues constitute a major group of naturally-occurring compounds that are of particular interest because of their biological activity.[1] They are an important class of N-myristoltransferase inhibitors. N-Myristoyltransferase (Nmt) is essential for the viability of fungi[2], so Nmt inhibitors like benzofuran derivatives can be a new type of antifungal agent. In this study we used artificial neural network (ANN) for designing a new model to predict antifungal properties of some benzofuran derivatives. Three groups of benzofurans used in this research are 29 molecules that their skeletal structure is as follow[3,4]:
X X
NH NH R O O R1 O
R O O O O O R
Antifungal activity was used in terms of log (1/IC50) against Nmt and we used it as the output for neural network. The input for neural network is descriptors calculated by dragon software. A three layer network was built and the proposed technique for cross validation was LOO (leave-one-out cross validation). The ANN program was run in MATLAB 7. Finally we understood that 2 descriptors included topological descriptors (indices based on the topology of the atoms and their bonds) and 3D-Morse ones are the most important characteristics of a set of benzofurans for their antifungal activity. The potential of the 3D-MoRSE (Molecule Representation of Structures based on Electron diffraction) descriptors considering the 3D structure and atomic properties such as partial atomic charges makes it particularly suited for studying biological data[5]. For reducing the dimension of input matrix, we applied PCA (principal component analysis) technique. We reported the accuracy of the model according to the determination
2 2 coefficient R and Q LOO which indicate the fractional explained variance in fitting and in prediction (by leave-one-out procedure), 2 2 respectively[6]. In our QSAR model R = 0.84 and Q LOO = 0.81.
References: 1) Stevenson, P. C.; Aslam, S. N. The Chemistry of the Genus Cicer L. In Studies in Natural Product Chemistry; Atta-ur-Rahman, Ed.; Bioactive Compounds; Elsevier: Amsterdam, The Netherlands, 2005; Vol. 33. 2) Weinberg, R. A.; McWherter, C. A.; Freeman, S. K. Mol. Microbiol. 1995, 16, 241. 3) Masubuchi, M.; Ebiike, H.; Kawasaki, K. Bioorg. Med. Chem. 2003, 11, 4463 4) S. Deokar, H.; Puranik, P. Med. Chem. Res. 2009,18, 206 5) H. Schuur, H.; Selzer. P.; Gasteiger, J. J. Chem. Inf. Comput. Sci. 1996, 36, 334 6) Di Marzio, W.; Galassi, S. Chemosphere, 2001, 44, 401
78 POSTER , is is -1 of of of [6] 79 e The ml toxic g e kinetic rate m [1]. HPSAM data stripping speciation method mor ocedur the the samples the is pr Speciation to species kinetic that The oltammetry in (specially element . addition pictorial between 1552. 0.010-0.240 d show samples. antimony and trace (HPSAM) and antimony ences (2006) to [5] HPSAM 1575. esults selectivity r standar fer 385 techniques of dif eatly Elemental and method (2007) gr geochemical the Chem. 19 samples. materials 1159. These H-point 0.003-0.120 pentavalent obtained on and of the the operties. The 94. water application (2006) addition 691. sensitivity Bioanal. pr oanalysis ceramic based 18 d that contributed and than ions. is the ranges (2005) [4], Anal. high toxic techniques. (2007) Electr biological the on tin eign have 71 show standar in 1091 oanalysis their with toxicity for A method . and of eport eparations alanta r T Electr esults ding Bosch-Reig, pr r The ochemical onmental, . lead F higher H-point sensitivity fects any method of omatogr egar r The ef determined of envir electr and Chr have agent. Martínez, Martínez, in 359. be J. Gimeno-Adelantado, e is described for the determination of Sb(III) and Sb(V) by adsorptive e is described for the determination of Sb(III) , . the V) found ences cos cos J.V can possible Ar Ar fer powerful pH=1.2. , Damghan University of Basic Sciences , Damghan University (2004) and not pharmaceutical a dif accuracy ocedur at 63 any Julia Julia (III in Sb(V) is determination applicability applying complexing compounds M. M. for Gimeno-Adelantado, have Potin-Gautier a their K. Zarei*, M. Atabati, M. Karami K. Zarei*, M. Atabati, large . and by V Sb(III) alanta the T states as to M. J. , we the out , and to Renedo, antimony Renedo, d Addition Method Applied to Simultaneous Kinetic to Simultaneous Method Applied d Addition Sb(III) due Doménech-Carbó, . analyzed antimony . work, techniques Hajian, of ogallol Pannier is simultaneous R. . and F M.T Sb(V) oxidation carried pyr owing this School of Chemistry for However ead be two antimony Domínguez Domínguez trivalent with , solution ekehtaz, using in speciation Doménech-Carbó, O. O. of Y Pinochet, oanalytical . may T applied the H. M. the widespr , M. ogallol methods. importance oz, to found electr generally been techniques.In pyr H-point Standar H-point Doménech-Carbó, González, González, eat eover to Quir speciation . gr A. of has antimony and elatively Abdollahi, voltammetry W r Mor usually applied of the . of H. e is is Gómez Gómez ar for salts, egori, voltammetry sweep ences: HPSAM Gr oanalytical its Jesús Jesús Doménech-Carbó, Shams, Bosch-Reig, combination . I.D. M. M. E. A. F Determination of Antimony(III) and Antimony(V) by Adsorptive Linear Sweep V by Adsorptive Linear Sweep of Antimony(III) and Antimony(V) Determination espectively Antimony antimony than speciation analysis) [2,3]. using electr voltammetry data is verified. For this purpose, a pr voltammetry data is verified. For this purpose, linear complexation suitable r in successfully Refer 1) 2) 3) 4) 5) 6) POSTER
Simultaneous Spectrophotometric Determination of Lead, Copper and Nickel Using Xylenol Orange by Partial Least Squares Calibration Method
Jahan Bakhsh. Ghasemi1, Samira. Kariminia2 1- Chemistry Department, Faculty of Sciences, K.N. Toosi University of Technology, Tehran, Iran 2- Chemistry Department, Faculty of Sciences, Razi University, Kermanshah, Iran
A partial least squares (PLS) calibration model was developed for the simultaneous spectrophotometric determination of Pb (II), Cu (II) and Ni (II) using xylenol orange as a cromogenic reagent. The parameters controlling behavior of the system were investigated and optimum conditions were selected. The calibration graphs were linear in the ranges of 0.0–9.091, 0.0–2.719 and 0.0–2.381 ppm for lead, copper and nickel, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. Absorbance data were taken between 350-650 nm and absorbance data were auto scaled. A set of synthetic sample mixtures were used to validate the proposed method. The root mean square errors of predictions (RMSEPs) and percent of relative prediction errors (RSEPs) are 0.2164, 0.0744, 0.0735 ppm and ±7.1855, ± 6.3193, ± 7.0806% for lead, copper and nickel, respectively.
References:
1) J. Ghasemi, N. Shahabadi, H.R. Seraji, Anal. Chim. Acta. 510 (2004) 121. 2) J. Ghasemi, Sh. Ahmadi, K. Torkestani, Anal. Chim. Acta. 487 (2003) 181. 3) M. Ghaedi, F. Ahmadi, A. Shokrollahi, Journal of Hazardous Materials, 142 (2007) 272. 4) B.L. Batista, J.L. Rodrigues, J.A. Nunes, L. Tormen, A.J. Curtius, F. Barbosa Jr, Talanta. 76 (2008) 575. 5) A.R. Coscione a, J.C. de Andrade, R.J. Poppi, C.Mello, B.v. Raij, M.F. de Abreu, Anal. Chim. Acta. 423 (2000) 31. 6) J. Luypaert, S. Heuerding, S. de Jong, D. L. Massart, J. Pharm. Biomed. Anal. 30 (2002) 453.
80 POSTER , -
of -7 by 81 for the ent due and fer nm ith with dif accuracy W opagation (1.6×10 450 of eatment eported determined palpitations r tr of of medium their be in and the terms back-pr been basic range can in for benserazide by , Iran emor1. Unfortunately also ehran, Iran nausea happening[2]. and wavelength , T used as slightly ochloride in pharmaceutical have this benserazide trained methods, in 2 such been , Bushehr and the concentration event levodopa ehran, Iran maximum (ANN) of catecholamines has pr 858. fects echnology methods the of , T at p to ef in two levodopa which nanoparticle side band 1975; , several network of eaction r A, , J. Khodaveisi , oculogyric crises, and tr , oculogyric crises, and P these 2 levodopa echnology obtained (SPR) performance and silver the e neural of the on Easton, unwanted wer . eactions otransmitter r with esonance analytical concentration based r important 265. cause artificial neur ashkhourian , Sharif University of T , Sharif University of is graphs two d eparation The the Publishing: 213. very can pr , J.T (2005) the satisfactory is 1,3 e 515. ease muscular rigidity plasmon 61 Mach and method body (2005) combination wer important between calibration , Sharif University of T , Sharif University of drugs in Ed.; 62 the The (2003) PVP feed-forwar an A, between benserazide. surface of 59 is 15th esults of ed used r of these Linear Part , Faculty of Sciences, Persian Gulf University , Faculty of Sciences, for est is ) rate r -5 of the omatographia, oposed. alanta Acta T elationship r pr the the Chr esence times. ee-layer Sciences, and is in changes pr in ophotometic Determination of Levodopa and Benserazide Based and Benserazide of Levodopa Determination ophotometic the thr ent [3-5]. ochim. ochloride 1.12×10 Fang, A the Nezhad, the – fer
drugs .Z. -7 ence Y hydr es. in dif 463. model them Spectr validated M.Reza Hormozi Nezhad * M.Reza Hormozi Nezhad fer determination at and to He, dopamine of dif Hormozi (8×10 Pharmaceutical oxylphenyl)-L-alanine] oxylphenyl)-L-alanine] the .G. mixtur (1985) P into ołyniec, monitoring nitrate) used the 1- Department of Chemistry and 10 M.R. W , samples of E. statistically benserazide Liang, was binary e J. oday absorbance instance (silver T 2- Department of Chemistry in Remington's fluids basis Institute for Nanoscience and Nanotechnology Institute for Nanoscience wer levodopa Smyk, determination the Abdollahi, levodopa o, - eason this J. r Zhou, ders such as Parkinson's disease and also decr ders such as Parkinson's 3 of . Drugs H. [(-)3-(3,4-dihydr the Y agent to for and algorithm ) this -5 es ecision, Gennar On ophotometrically ences: ang, Safavi, for biological Ghose. pr W ning A.R. K. Karpi´nska, J. A. Simultaneous Kinetic Spectr Simultaneous on the Surface Plasmon Resonance Band of Silver Nanoparticle and Artificial Neural Network Nanoparticle and Artificial Plasmon Resonance Band of Silver on the Surface Levodopa conversion neural disor and attention simultaneous determination of the levodopa and benserazide hydr In this work, a new method for simultaneous and oxidizing spectr time. simultaneously lear mixtur 1.0×10 and Refer 1) 2) 3) 4) 5) POSTER
Application of Artificial Neural Network in Infrared Spectrometric Quality Control of Dairy Products
Mohammadreza Khanmohammadi1, Amir Bagheri Garmarudi1,2, Keyvan Ghasemi1 1- Chemistry Department, Faculty of Science, IKIU, Qazvin, Iran 2- Department of Chemistry & Polymer Laboratories, Engineering Research Institute, Tehran, Iran
Analytical methods for determination of protein in food products are generally based on Kjeldahl and Lowry methods or related modified procedures which utilize spectrophotometry. Reversed-Phase HPLC, capillary electrophoresis and diffuse reflectance infrared Fourier- transform spectrometry (DRIFTS) are some other techniques [1]. In this research a method has been introduced for quantitative determination of protein content in yogurt samples by mid-FTIR spectroscopy and chemometrics [2]. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. The main purpose of this algorithm is to select wavelengths which their information content is minimally redundant, in order to solve the co-linearity problems. The choice of wavelengths for model building using SPA is critical if the model is to have good future predictive ability. After outlier detection, an important action is to select appropriate calibration or validation data set with a minimum error in model prediction. A methodology for this procedure is hierarchical cluster analysis (HCA). In HCA, the similarity between samples is established using the concept of a “distance” (calculated using a mathematical relationship; i.e., the Euclidian norm) between samples which are related to how similar the numerical properties of the samples are (e.g. the absorbance at different wavelengths). ANN is typically organized in layers where these layers are made up by a number of interconnected nodes which contain an activation function. Input vectors are presented to the network via the input layer which communicates to one or more “hidden layers” where the actual processing is done via a system of weighted “connections”. Most ANNs contain some form of “learning rule” which modifies the weights of the connections according to input patterns that it is presented with. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step.
References:
1) CFSAN 2005 program priorities. Center for Food Safety & Applied Nutrition, US Food and Drug Administration (FDA), College Park, MD, (2005). 2) M.M. Paradkar, J. Irudayaraj, Determination of cholesterol in dairy products using infrared techniques: 1. FTIR spectroscopy, Int J Dairy Technol 55 (2002) 127. 82 POSTER a of to of 83 the and was on after on e-wave central V) method samples 500(0C), othermal ode conditions e Se(VI) eactivity squar r (1.8 and surface Based selenium developed water measurment and using these ode sugested ehran, Iran total ater Samples was for (RSM) of some . ed potential temperatur water containing electr in (2002)167–178. Under toxicity ehran, Iran 3.5% parameters. design , Evin, T cury 467 odeposition-electr oscopy of the olyte measur natural 4(s). ashing applied avakoli, Ensieh Ghasemi species as in mer these RSD AAS) in W Acta. the electr , Evin, T in spectr electr performance methodology of cury Coated Electr olled times fields, with The Chim. 40(ppm), fects selenium selenium spent simple experimental ef (VI) of between surface A uncontr Anal. Se An the The absorption odeposited values. at ence of analytical for
atomization 2]. analysis -1 fer inorganic L electr 99%. oscopy (ED-ET 32–38. and Se(IV) g [1, atomic dif involved. of Response and m concentration techniques, to e observed was the the ar 0.9 (2006) , Reza Alizadeh, Hamed T , Reza Alizadeh, Hamed 94 evaluation of = Speciation 15(s) as and analysis solution Se(IV) for they 572 othermal modifier LOD in Raba, time toxicological J. only voltammetric Acta. edicted electr measurment. between and eduction which r pr calculated and strategy follows: in using ashing speciation (IV) a ode, Chim. AAS emained as was r as Se varied for solution) onmental ET Anal. forms electr selective for Martinez 20(s), between was HCl used selenium Se(IV) envir pg value edicted the L.D. M of in time method pr , Faculty of Science, Shahid Beheshti Uinversity , Faculty of Science, carbon, been on , Medicine Plant Institute, Shahid Beheshti University , Medicine Plant Institute, e selenium Se(VI) est (1.0 eement chemical Olsina, mo=70 AAS) wer on analysis ecovery have cury-coated agr R. drying r inter the based conditions. media activated is mer mass on echnique Using Selective Separation on Mer echnique Using (ED-ET on (CCF) good separated Salinas, easing a Speciation acidic average E. othermal Atomic Absorption Spectr othermal Atomic conditions the ongly optimum incr In 2400(0C), parameters o, method oscopy design e str of speciation, e ed ode, showed ode. sopelas, The orrier spiked T T Speciation and Determination of Inorganic Selenium Species by Selenium of Inorganic and Determination Speciation under ar . characteristic optimal F spectr fective econcentration by ef pr electr The electr the depend center AAS (IV). A.A.J. ET Selenium temperatur
Se 1- Department of Chemistry the the after analyses face opoulou, by of assessment developed.
shown of esults, ds: coated -1 r L absorption ed Petr content. 2- Department of Phytochemistry ences: elements Bertolino, g a Simple and Rapid T a Simple and Rapid m been been cury .A. Nahid Mashkouri Najafi, Shahram Seidi, Alireza Ghasempour Nahid Mashkouri Najafi, F M.O. emoval Coupled With Electr Speciation trace atomic has mer r measur Se(VI) investigate composite analysis atomization statistical has 100 Keywor Refer 1) voltammetry 2) POSTER
Simultaneous Extractive Spectrophotometric Determination of Fe(II) and Fe(III) Using PAR and HDPB by Partial Least Squares Method
J. Ghasemi, S. H. Kiaee Department of Chemistry, Faculty of science, Razi University, Kermanshah, iran
An extractive spectrophotometric method has been developed for the simultaneous determination of Fe(II) and Fe(III). The
method is based on the ion-pair extraction of the iron(II) and iron(III) chelates of 4-(2-pyridylazo)resorcinol (PAR, H2L) as anionic chelates in alkaline media into chloroform with 1-Haexadecylpyridinium Bromide at 20°C and constant ionic strenght. The calibration model is based on absorption spectra in the 300–800 nm range for 25 different mixtures of Fe(II) and Fe(III). The cross- validation method was used for selecting the number of factors. The number of Latent Variable for both of Fe(II) and Fe(III) was 5. The RMSD for Fe(II) and Fe(III) were 0.041 and 0.036, respectively. The effects of various cations and anions on simultaneous determination of Fe(II) and Fe(III) have been investigated. The procedure was confirmed by Fe(II) and Fe(III) analyses in pharmaceutical products.
References: 1) D.M. Halaand, E.V. Thomas, Anal. Chem., 60, 1193, 1988. 2) D.Nonva and B.Evtimova, J.Inorg.Nucl.Chem., 35, 3581, 1973,. 3) Hitoshi Hoshino and Takao Yotsuyanagi, Talanta, 31(7), 525-530, 1984. 4) J. Ghasemi and A. Niazi, Talanta 65, 1168–1173, 2005. 5) K. Momoki, J.Sekino, H. Satoch and N. Yamaguchi, Anal. Chem. , 41, 1286, 1969.
84 POSTER of of of 85 the the Am. with (MD) J. could single- omise nano- linked model for Peptide pr finalizes energies Langmuir esults r energy interaction new EEEECCCC ee [3]. finity model and fr hold the simple af s in biological, with of dynamics the performing linear interaction solubilization ehran, Iran covalently The a and modification. functionalize This in for basis s. for Goliaei s s agents amphiphiles. binding and lowest with forms eement ocess CNT CNT and surface ehran,T [3], designing Molecular pr e and agr
of SWCNT 4 applied om peptide C in for pur fr 4 molecular cationic simulation. s E Kyani educed dynamics and r non-covalently of of method sought. the binding by non-covalent being CNT eatment that in and tr price of ehran, Iran anionic, form nanotubes nanotube dynamics highly molecular , T peptides oposed and is high self-assembling easingly common pr four Peptides 1909. by developed using carbon the 1 incr of oxidized carbon the interaction oxidized e 17, J.D.; to most understanding between applications ar on for molecular finities for finities for dynamics as the and to model Due ) finities 2007, the 4 compounds for nanotubes the af with nanotubes (K Based [2]. (LIE) finities dominant single-walled leads eat challenge for the potential use of SWNT eat challenge for the s) Hartgerink, af the peptides s Chem. Peptides finity [4]. the carbon s af interactions carbon (CNT R.B.; as facilitates consider CNT of , Bahram Goliaei applicable energy to Math. work, AFNNKT 1,2 to intact. s) is a gr binding J. can conjugation arbiat Modares University and pH-sensitive synthetic with SWCNT highest in eisman, , T energy the used esent for W naotubes peptides work of the pr four be HSSYWY operties aals useful of Non-covalent interaction optically Encapsulation S.E.; pr W model ent the , can finities [1]. these and In carbon characteristics. S.I.; interaction LIE these der curr af . Anahita Kyani KKKK showed linear of of of ), estimation van 4 the calculation electrical their using (E method Stupp, water -soluble in ediction of the Peptides' Af of the Peptides' ediction Paramonov esults using solubility r peptide in finities for Pr and good studies ater af of .M.; a applications energy M.C.; W V The olled between ee ess. .E.; fr P AFNNKT edicted give pr calculated studied contr employed pr e In e nanotubs binding functionalization Hersam, uwono, e 2- Department of Chemistry Y photonic give computational mechanical of Carbon Nanotubes Using Linear Interaction Energy Model Using Linear Interaction Energy Carbon Nanotubes wer wer could oach Binding the to mechanism ) wer and s 4 HSSYWY Pehrsson, elationship computational M.O.; s r 12418. and K experiments. J-DR; of B.; 4 their , appr the (E model C.H.; 124, THEOCHEM, onic forts, EEEE powerful Guler solubility SWCNT linear The a ef Rocha, keeps LIE . SWCNT nanotubes 4 Goliaei, to of ranking K as Song, the the oach. 2002, electr Finding M.S.; for .; L.S.; A.; The 4705. AFNNKT that solubilization of the s W appr Soc. carbon ophotometric ences: 21, itus, nold, 1- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of T Institute of Biochemistry 1- Department of Bioinformatics, esents easing both Kyani, W peptides Ar Zhao, edict epr The solubilization of single-walled carbon nanotubes (SWNT The solubilization of chemical, SWCNT for experimental simulation peptides. incr peptides sequences HSSYWY of r binding. pr spectr peptide systems. Refer 1) Chem. 2) [3] 2005, energy [4] walled POSTER
-1 Prediction of Log (IGC50) for Benzene Derivatives to Ciliate Tetrahymena Pyriformis from Their Molecular Descriptors.
Mohammad H. Fatemi, Hanieh Malekzadeh Department of Chemistry, Mazandaran University, Babolsar, Iran
The purpose of this study was to develop the structure-toxicity relationships for a large group of organic compounds including 392
-1 substituted benzenes to the ciliate Tetrahymena pyriformis (Log (IGC50) ) using interpretable molecular descriptors. These descriptors used to search for the best model which were calculated by using DRAGON 1and CODESSA2,3 software. Multiple linear regression and artificial neural network4,5 methods were used as linear and non-linear feature mapping technique. In order to find the best QSAR model three separate MLR models were developed using DRAGON, CODESSA and combined DRAGON and CODESSA calculated descriptors. The best obtained model has seven descriptors which are: the molecular weight, the radial distribution function, the kier shape index, the 26th component of atom-centered descriptors type of R-CX-R, the topographic electronic index, the H atoms which attached to CO group, the 24th component of atom-centered descriptors type of R-CH-R. These descriptors can encode different features of molecules which responsible in their steric, electronic and lipophilicity
-1 interactions.6 In order to investigate any non-linear relationships between selected descriptors and Log (IGC50) , artificial neural -1 network was used. After optimization of ANN, it was used to calculate the Log (IGC50) values of interested molecules. The -1 standard error in prediction of Log (IGC50) using ANN model for the training, internal and external test sets were: 0.341, 0.437 and 0.366, respectively while these values for MLR model are 0.312, 0.337 and 0.340 for training, internal and external test sets, respectively. The comparison between these values and those obtained from linear models, indicate that linear model based on descriptors which were developed from the DRAGON and CODESSA calculated descriptors had the best statistical parameters in
-1 prediction of Log (IGC50) . The reliability of model was evaluated by using leave-many-out cross-validation method which produced statistics of Q2=0.819 and SPRESS=0.32, as well as by y-scrambling. The obtained results reveal the reliability developed QSAR.
References: 1) Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, Germany,2000. 2) Katritzky, A. R.; Lobanov, V. S.; Karelson, M. Comprehensive Descriptors For Structural and Statistical Analysis. Reference Manual, Version 2, 1994. 3) Katritzky, A. R.; Lobabanov, V. S.; Karelson, M. Pure and Appl. Chem. 1997, 69, 245. 4) Beal, M. T.; Hagan, H. B.; Demuth, M. Neural Network Design, PWS, Boston MA, 1996; pp 75-92. 5) Bose, N. K.; Liang, P. Neural Network Fundamentals, McGraw-Hill, New York, 1996; pp 241-250. 6) Monzón, L.M.A.; Yudi, L.M. Journal of Electroanalytical Chemistry 2006, 591, 46. 86 POSTER
In of EP EP
87 the the ent two (AA) max [1]. of l fer ( and for and or dif behavior acid these M) nm err m AA data of eaction r 620 bias kinetic 12.5 at – ascorbic in the kinetic om obtain containing analysis of of fr (2.5 to es to ence ee fr rates fer AA ophen-olindophenol (DCPI) of Dif used mixtur absorbance the (HPSAM) the was simultaneous determination completely eaction. and r 20 synthetic 00s between 6 t=200s t= method ferent which the permits determination 15 d Addition Method ence of several ) when dif behavior plot fer M m in = 0.9993 2 10 R simultaneous dif 2424. HPSAM y = -0.0042x + 0.6177 additions eduction of 2,6-dichlor concentration
= 0.9973 (5.0 2 R d the the initiation kinetic 147. y = -0.0093x + 0.5923 5 a EP concentration (uM) and EP using on (1991) addition for ) om simultaneous M m fr d 63 has analyte solutions were added. 0 (2003) standar compounds data 0.7 0.6 0.5 0.4 the EP of Plot of HPSAM for simultaneous determination based (4.0 59 of to
AA
method
e c n a b r o s b A Chem. two -5 Fig. 2 of standard EP and standar minutes M H-point alanta m T AA Anal. ence in the rate of r possible few these applied M DCPI, eatment by the is m d fer calculation M DCPI, (b) selective tr typical of m , Payame Noor University (PNU), Kerman, Iran , Payame Noor University at phosphate of c first AA, (c) 50 a Legua, Nezhad, ophotometric Determination of Ascorbic of Ascorbic Determination ophotometric the M DCPI M EP and m m the and (d) 50 a or of C.M. M EP es m successfully shows and 2.5 Hormozi simple components [2]. during 2 b AA a mathematical M Alireza Mohadesi*, Hamideh Mirzaabdollahi Alireza Mohadesi*, was M DCPI and 2.5 nandez, m determination mixtur m as M.R. eduction fundamentals two r , Fig. 1 Kinetic curves for (a) 50 50 DCPI and 2.5 2.5 Fig. time Her of with eagent the r the . binary R.H. with EP method selective of that suggested analysis and the is Department of Chemistry Sedaghatpour common Cabeza, . AA to F oposed changes a compounds. Simultaneous Spectr Simultaneous shows established accompanied pr of 1 A.S. al. also Acid and Epinephrine by Kinetic H-Point Standar Acid and Epinephrine with HPSAM form) The Fig. et . ratios Falco, determination and Abdollahi, simultaneous the EP analyte biological .C. H. M) P m and two oxidized work, 50 ences: Reig, in AA – Safavi, components method, .B. these this F A. Campins-Falco simultaneous this In two with and epinephrine (EP). This method is based on the dif and epinephrine (EP). This method is based DCPI of important concentrations. (2.5 Refer 1) concentration 2) POSTER
Determination of PABA Concentration in B-Complex Tablets by MCR-ALS Method
Mohammad Mirzaei1,2, Mehdi Khayyati1,2 1- International Center for Science & High Technology and Environmental Science 2- Shahid Bahonar University Department of Chemistry Kerman, Iran
Chemometrics can briefly be described as the interaction of certain mathematical and statistical methods to chemical problems [1]. The Spectroscopy produces large amounts of data for each sample analyzed. The information part of the data is what eventually leads to knowledge about the sample, while the noise is a non-information part. A matter of concern is always to minimize and, if possible, to get rid of distributing noise in the data since it impairs the information gained. This is where chemometrics comes in, since multivariate methods are constructed to extract the information from large sets of data. MCR-ALS is an iterative soft-modeling resolution method proposed by the authors that has been successfully applied to solve many mixtures dynamic processes monitored spectrometrically [2]. The MCR-ALS analysis is implemented by using selected constraints, which are applied during each iteration. These trials are carried out to achieve the best results. Absorbance-pH measurements constitute evaluable tool for generating second-order data that can be employed, together with a suitable chemometric algorithm, to predict of a given analyte in samples of complex composition. The aim of the present study is the quantitative determination of Para Amino Benzoic Acid (PABA) in B-Complex tablets. The quantification was performed by comparing the areas below the concentration profiles for the analyte in the standard and in the unknown sample. Multivariate curve resolution based on a constrained alternating least squares (ALS) optimization is applied to recover the optimal concentration profiles and spectra of the detected chemical species. Because of rank-deficiency phenomena, the pure spectra and concentration profiles of all species can only be recovered through matrix augmentation. pH variations were used for recorded to UV/VIS spectrum in pH = 2-6.2 to 0.2 increments. The results show that proposed method was successfully applied to the determination of PABA in B-Complex tablets. Root Mean Square Error of Prediction (RMSEP) and Relative Error (RE) were 0.349 and 7.43% respectively.
Reference: 1) B.G.M. Vandeginste, Top. Curr. Chem. 141(1987) 1. 2) R.Tauler, B. Kowalski, S. Fleming, Anal. Chem.65 (1993) 2040.
88 POSTER e e of 89 ar ar and was e other yields phenyl benzo- eaction r ARKIVOK, the Ther gradually concluded om e for considering eaction.The geometry fr data. method r On ar e completely various Reactants by was the 1996) this it pyrimido , Doepp., with intermediates A, model package. step P for a eactants 03 r distance. eparation. final oduct Dietrich pr states, optimization eactants wer experimental , the eactions, make pr r the condensed ner to the Pittsburgh, the In their ent step two bonding mechanism Simultaneously of Ster d In., der fer GAUSSIAN ) for ) I to with I I or ( the ( While transition dif thir es of the r d level the Olov In of O h , all P in the eaction that eaction. r calculated. r N accor In eported (Gaussian, e same r distance steps pyrimido[2,1-b]benzothiazoles the S N h the P S N ed. O show wer the eactions. good ent of r state. been at 2nd in A.Cherkasov fer active distinguish acid follow e benzothiazoles. dif to implemented have to interactive wer of Rafael quantities no chemical analysis Method, transition opiolic Ea e mechanism a ee dimensional structur pr om the H esults H method r possible O method or biologically fr O methods O O to C II
C and .Fomum, of . Condensed pyrimidine compounds have been shown to exhibit compounds have been shown to exhibit . Condensed pyrimidine T Structur equency or was oposed I fr etical it pr comparing h minima h onic P P obtained phenyl-substituted synthesis by eactants the thermodynamic theor + r , + true of synthesis a Zacharias Electr The to its 2 2 a system the H semi-empirical H . . the N pathways of . N harmonic e . fer benzothiazole with Finally ar and to of the ding S N for S N opiolic Acid and Benzotiazol-2Amino by Chemometrics opiolic Acid and PM3 to of number step, a ding S) specially accor points D , E.Nkengfack, using mechanism. bellow) and om Pr Chemistry useful mechanism H, II accor optimized , Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran. Shahid Bahonar University of Kerman, , Faculty of Sciences, second D mechanism. investigation, and the activity G, the distance and was
° D Augustin (I stationary In ection A operties Exploring , Study of Synthesis of Biologically Active Pyrimido Active Synthesis of Biologically Study of favorable pr dir oposed kinetic (i.e. 0.1 Mohammad Mohammadalizadeh, Mehdi Mousavi, Hassan Sheibani Mehdi Mousavi, Hassan Mohammad Mohammadalizadeh, system pr necessary Frisch, most located .Mbafor antitumor investigate T the the method. pathways the specific to the eaction of in r is benzothiazol-2amino Æleen in is I seems quantities PM3 ent steps have to be followed. At the first step, thr ent steps have to be followed. At the first successive show it Joseph that and in and fer their e possible work pharmacological using ahe, Thus energies acid Department of Chemistry together close W pathway esman two ensur this components during [2,1-b]Benzothiazoles fr [2,1-b]Benzothiazoles ences: to esting the of the least Helen J.B.For oduces. opiolic ought Benzothiazoles inter thiazoles and benzothiazolo quinazlines exert antiviral activity thiazoles and benzothiazolo hand, pr aim pr at pathways, dif optimized used br become all thermodynamic activation that Refer 1) (2003) 2) POSTER
Application of Soft-Modeling Approaches to Resolution of Electron Donor- Acceptor Complex Formation of Morpholine and 2,4,6-Trimorpholino-1,3,5-Triazin With Iodine in Different Solutions
Tayyebeh Madrakian*, Masoumeh Mohammadnejad, Faezeh Hojati Faculty of Chemistry, Bu-Ali Sina University, Hamedan, Iran
Interaction of morpholine and a synthetic ligand (2,4,6-Trimorpholino-1,3,5-triazin) with iodine (I2) has been investigated spectrophotometrically in chloroform and dichloromethane solutions using soft modeling approaches. Evolving factor analysis (EFA) and multivariate curve resolution-alternating least squares (MCR-ALS) are used for complete resolution of the measured spectrophotometric data. Once the number of different species was determined, an initial estimate of their concentration profiles is obtained using EFA. When the initial estimations of the concentration are available, an alternative least squares (ALS) iterative optimization is initiated using these estimations to obtain the matrix of pure spectra ST (molar absorption coefficients for each species) and the matrix of concentration profiles C which best fit the experimental data matrix, D. During the optimization some constraints (non-negativity, for concentration and absorption values, concentration closure) are applied to ensure that the final solution is chemically meaningful. The complex formation constants in different solutions were determined from the concentration profile. The influences of solvent properties on the formation constant of the resulting charge-transfer complexes are discussed. It is obvious that the stability of complex increase significantly with decreasing donor number and increasing dielectric constant of solvent and these results emphasis on the formation of ion pair complex between iodine and two ligands.
Also the results show in both solvent, the Kf for I2-MTZ complex is lower than I2- MOR complex that indicate influence of structure of ligand on the stability constant. This study demonstrates the utility of analyzing data using MCR-ALS for gaining insight into complex processes, especially those of biological significance. The MCR-ALS analysis can be initiated using either empirically or mathematically generated pure component spectra (e.g., PCA) or concentration profiles (e.g., EFA). If the molar absorptivity of each pure component spectrum can be determined, actual concentration profiles can be generated and then formation constant can be determined.
References: 1) M.J.S. Dewar, A.R. Lepley, J. Am. Chem. Soc. 83 (1961) 4560. 2) C. Raby, J. Lagorce, A. Jambut, J. Buxeraud, G. Catanzano, Endocrinology 126 (1990) 1683. 3) R. Tauler, Chemom. Intell. Lab. Syst. 30 (1995) 133. 4) S. Wold, K. Esbensen, P. Geladi, Chemom. Intell. Lab. Syst. 2 (1987) 37.
90 POSTER in 6- to 91 Sci. ent ) cyclic Symp. HF/ fer classes alkaloid OH a ( dif plant. 3-pyridyl slv the Sco. Equilibrium oposed e J. alkaloid G at for D pr ar lower some ( the + is ) Can. + of for contain Chem. cycle RNH Am. principal isogenic ( alkaloids esults computed 157-60. solv r tobacco. which oc. G and e the mechanical Pr D 32: most + be wer . burley Determination 0 vac -22 G Plant to of D tobacco. plants + genotypes quantum ) ed values substances 2 in
O b experimental 2 thermodynamical Physiol. amines H pk ( initio A the , Iran. 2402-2409. vap Experimental basic alkaloid G tabacum content ab 5.06838*10 consider D of 7:75-106. 104, with + is tobacco. ) was secondary absolute Sci. on using RN solution. oup ehran North Branch. ( , Behshar nicotine 2000, Ammonia: dominant gr in solv and Nicotiana A of eement G a and , Asni Ashari M B In D obacco nicotine 2 aliphatic T nicotine e - and agr . = hydrazide ar Chem. 0 for and Adv G solvent, investigated egulation phas D plants. r good alue in Aqueous Solution for Nicotine Solution alue in Aqueous homozygous Phys. maleic in the bases in V J. in was gas nicotiana, and of b Recent as solution Alkaloids in , Moradi Sh. rimethylgallium 1 the T fects 4697-4701. Acids, well 1993. values
in of ef b ogenous describe as nicotine 18, solution. alkaloids. pk 105, nitr found to 1051. aqueous , Islamic Azad University T , Islamic Azad University the manipulation accumulation tabacum. in carboxylic Reaction of 101, Chem. 2001, used olatile nishes animals tobacco B. V A co Antonio Chaer Nascimento., 1999, Ab initio calculation of Absolute pka values in aqueous co Antonio Chaer Nascimento., 1999, Ab initio aqueous physiological value in calculations
fur of b was in materials Alkaloid pK and 1970, irtash Research & Education Center irtash Research & Education Chem. Nicotiana Chem. Comput. Gas-Phase esent J. theory model Accumulation, in 1985, Moradi Robati Gh R. Moradi Robati Gh Halogenated pr mechanism (PCM) 1- T e Phys. Phys. nicotine alkaloid J. J. The and ar 2001, 3:15 biochemistry 1997, absolute 1977, ., for Atkinson., ., A V model of 2- Chemistry Faculty eaction and Chaplin., alkaloid r Carr .O. many Morphological 1997, Thiols, . . functional W one, J.F W values B., COREST Alkaloids the . Bar Calculation. Saunders., 1974, and, and . pkb compounds. M. Physiology density Infor Alcohols, principal continuum Robert, Calculation J.W initio
389-425. d, Sims, Sims., nucleus. Mennucci, Ab Among and pp tobaccos.The Cossi, the and Ab initio Calculation of Absolute pK Calculation of Ab initio S.L.Gay 1981, and J.L. . J.L. . C. Bull. P organic . . . ., C. is absolute , and cial Aliphatic L.P L.P L.P L.P L.P of level) ences: Orleans. polarizable ogenous 289-94. compounds. Bush, Aleksey Bush, Bush, Amovilli, Adamo, Bush, Bush, Nicotine nitr derivatives. commer 31+G calculate The classes of Refer 1) Constant 2) 3) 4) 5) 50: 6) New 7) 8) solution. 9) Clarissa, O. Silva Edilson, C. da Silva, and Mar genotypes. POSTER
Studies on the Quantitative Relationship Between the Retention Indices of Essential Oils and Their Molecular Structures
Mehdi Nekoei, Majid Mohammadhosseini, Farzad Sadeghi Department of Chemistry, Islamic Azad University, Shahrood branch, Shahrood, Iran
Plant essential oils and their extracts have been greatly employed in folk medicine, food flavoring, fragrance and pharmaceutical industries [1]. The thuriferous juniper (juniperus thurifera L.) is located on the western part of the Mediterranean basin [2]. Juniper when applied externally is useful in cases of rheumatism, sciatica and dermatitis and has been reported as having therapeutic effects in the treatment of neurasthenic neurosis when used as a bath. It is also reported as having antimicrobial properties [3].A simple, strong, descriptive and interpretable model, based on a quantitative structure-retention relationship (QSRR), is developed using stepwise multiple linear regression (SW-MLR) approach for prediction of the retention index (RI) of essential oil components. By molecular modeling and calculation of descriptors, four significant descriptors related to the retention index values of the essential oils, were identified. The proposed methodology was validated using leave-one-out and leave-group-out cross validation using division of the available data set into training and test sets. The results illustrated that the linear techniques such as MLR combined with a successful variable selection procedure are capable to generate an efficient QSRR model for predicting the retention indices of different compounds. A model with low prediction error and good correlation coefficient was obtained(R2calibration=0.955, R2prediction=0.944, Q2LOO=0.947, Q2LGO=0.945, REP(%)=4.74). This model was used for the prediction of the RI values of some essential oil components which were not used in the modeling procedure.
References:
1) Kusmenoglu, S.; Baser K.H.C.; Ozek, T. J. Essent. Oil. Res.1995,7,441-445. 2) Adams, R.P. Trafford Publ., Vancouver, BC (2004). 3) Achak, N.; Romane, A.; Alifriqui, M.; Adams, R. J. Essent. Oil. Res. 2008,20, 200-204.
92 POSTER e in as of 93 the the oved wer been using liquid peaks multi- for various such aspirin, addition es of while impr of has d values an ablets extraction feine useful mixtur egion e co-eluted pKa usage r caf ar acetaminophen Standar ovide substances After performance . the and pr of , MCR-ALS has been or investigation to err and complex , Qazvin, Iran. multiple spectral 1 high ofiles. es (MCR-ALS) es (MCR-ALS) [3], of % Acetaminophen of theory pr as nm techniques absorbing shown of defined UV acetaminophen 0.1-7 ocesses such determination be amide. pr been analysis as esolution r 220-330 with for other has numerical in the 2000. [4], mathematical ed estimation such as in tool , Hossein Nemati systems omatic with 2 ded evolving ar techniques etc.) initial method ecor measur r these Rockville, abstract known nating Least Squar nating Least e for of powerful was industrial a wer acylated formulated formulations as used separation absorption, determinations an spaces (MCR-ALS) including is Convention, when is was techniques es spectra , complex , Hamid Abdollahi es in flow injection analysis [6]. In this study is 1 735. A) method active UV–V of 335. -V possible, 3722. (EF squar this is The UV acetaminophen (1985) study However quantitative pharmaceutical (2001) least (1995) The the disadvantages Pharmacopeial escence, 110 the 2040. analysis 67 the (1/2) Chemometrics overlap es of analytes without any prior separation or extraction step. Although both es of analytes without any prior separation to allow indicated intervals. in nating (fluor States years[1]. 438 319. to Analyst several (1993) tablets. Chem. factor 0.2 samples 4-acetamidophenol) alter 65 Acta applied and spectral United eal many (1993) have r Anal. with e Shukla, , titrations 12 for (90-103%) Chem. Chim. necessary[2]. C. been they Evolving and I. es auler . wher T esolution range r Revision. has methods Anal. R. Anal. Chem. acetaminophen oscopic acetaminophen Singh, usually esults, r 24th of ocedur e curve D. Anal. Recoveries synthetic pr other ar and esolution and quantification of mixtur Mohammadreza Khanmohammadi spectr Fleming, of to MCR-ALS ends S. 7.60-10.60 esolution of complex mixtur r substances, T experimentally Ahmed, ed using (N-acetyl-paminophenol, (N-acetyl-paminophenol, (HPLC) successful applied. S. ofiles 2- Chemistry Department, Institute of Advanced Studies in Basic Sciences, Zanjan Iran Institute of Advanced Studies 2- Chemistry Department, nandez-Cassou, nandez-Cassou, over codeine celo, titrimetric pr Pharmacopeia, multivariate means. active them Her Her also give Bar determination Kowalski, by S. S. compar or B. D. The for systems , , 1- Chemistry Department, Faculty of science, Imam Khomeini International University Faculty of science, Imam Khomeini 1- Chemistry Department, PLS for States was variated e. Application of Multivariate Curve Resolution Alter Curve Resolution of Multivariate Application Srivastava, ophotometric r oscopic ences: omatography [5], r echnique for Quantitative Determination of Acetaminophen in Pharmaceutical T in Pharmaceutical Determination of Acetaminophen echnique for Quantitative auler auler K. and T T T samples. Saurina, Saurina, was omatography M. United R. J. R. J. esolution eal Acetaminophen determined excipients chr spectr PCR softwar r spectr equilibria in chr applied acetaminophen pH obtained concentration method 1) Refer r 2) 3) 4) 5) 6) POSTER
Simultaneous Spectrophotometric Determination of Lead and Mercury in Waste Water by Least-Squares Support Vector Machine and Partial Least Squares Methods
Ali Niazi1,2, Ateesa Yazdanipour1, Zahra Ahmari1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, BArak ranch, Arak, Iran 2- Sama College, Islamic Azad University, Arak Branch, Arak, Iranrak, Iran
The simultaneous determination of lead and mercury mixtures by using spectrophotometric method is a difficult problem due to spectral interferences. By multivariate calibration methods such as PLS and LS-SVM [1-3] it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a suitable preprocessing method for multivariate calibration of mixtures without loss of prediction capacity using spectrophotometric method. This study describes an analytical methodology for simultaneous determination of lead and mercury mixtures of whit spectrophotometric method and multivariate calibration technique (PLS and LS-SVM) with preprocessing by OSC. Calibration graphs were linear in the range 0.1-2.3 and 0.2-3.5 mg mL-1 for lead and mercury, respectively using 4-(2-thiazolylazo)-resorcinol as complexing agent. The experimental calibration matrix was designed with 25 mixtures of these chemical. The RMSEP for lead and mercury with PLS, OSC-PLS and LS-SVM were 0.1278, 0.3465 and 0.1026, 0.1415 and 0.0214, 0.0047, respectively. This procedure allows the simultaneous determination of lead and mercury in real matrix samples good reliability of the determination was proved.
References:
1) A. Niazi, J. Ghasemi, M. Zendehdel, Talanta, 74 (2007) 247 2) A. Niazi, S. Jameh-Bozorghi, D. Nori-Shargh, J. Hazard. Mat., 151 (2008) 603. 3) A. Niazi, A. Azizi, M. Ramezani, Spectrochim. Acta Part A, 71 (2008) 1172.
94 POSTER in 95 for om the the ning fr is at drugs, field LS-SVM multiple lear attracted calculated to for LS-SVM e these model has of of wer the performed compounds machine Y) important 0.0429 of A superior finity 84 was of the W af LS-SVM of A most oduction om quality fr studies (GET intr the finity RMSEP binding af the as the methods, with QSAR the optimization in of that oach , Arak Branch, Arak, Iran , Arak Branch, Arak, developed fecting binding other descriptors ability af 1 appr geometry over shown ediction ediction pr pr This describe factors ediction algorithm structural , Arak Branch, Arak, Iran , Arak Branch, Arak, initio of have to pr the Ab also for drugs. used high ability popular and of Results performance important IC50. a , Amir Ezatpanah that the 1,2 as is 603. applied finity of Pharmaceutical Pharmaceutical finity of 1) finity showed most b ent Chemometrics Methods ent Chemometrics af was model quantum (2008) (TR the (SVM) a fer 1 enhances model of b 151 , Ali Niazi 293. 1 model generalization binding one design (RMSEP=0.3260). Mat., 247. This of machine Mechanic (1999) esulted d. eceptor r for r 9 PLS drastically QSAR, [1-3]. The (2007) Hazar vector emarkable Lett., , Faculty of Science, Islamic Azad University , Faculty of Science, r and of J. e. 74 ediction Sasan Sharifi pr es. hormone ocess and 6–31++G**. Pr information alanta, descriptors ocedur the ediction of Binding Af ediction support oid T squar pr set, Pr applications for thyr Nori-Shargh, The Neural Compounds Using Dif Compounds Using ovides investigation least D. oung Researcher Club, Islamic Azad University oung Researcher Club, basis the pr , advantages structural the (RMSEP=0.4628) Zendehdel, for 2- Y modeling model. extensive its partial M. and known andewalle, suggested the MLR to V the a and is in J. Among chemistry ligands gained Due with not Jameh-Bozorghi, build Ghasemi, . 1- Department of Chemistry oxin e ession chemical S. J. and study Drug. to Suykens, egr level, wer thyr r ences: comparison Niazi, Niazi, each YP QSAR A. A. J.A.K. A computational mimic B3L for method community attention which with quantum linear Refer 1) 2) 3) POSTER
Spectrophotometric and Thermodynamic Study of Praseodymium with 4-(2-Pyridylazo) Resorcinol Complex using Chemometrics Methods
Ali Niazi*1,2, Bahareh Yasar1, Mehrana Motiee2 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
-1 The protonation and Pr(III) complexation of 4-(2-pyridylazo) resorcinol (PAR) in ionic strength 0.1 mol L KNO3 at 10-40°C has been studied by global analysis. In spectrophotometric titrations, linear or near-linear dependence of concentration profiles and the existence of minor species can cause difficulties in the evaluation of the data. Both calculated absorption spectra and the corresponding equilibrium constants are not or only poorly defined. The result is the inability to reliably fit a reasonable model to the data. In second order global analysis, a number of spectrophotometric titrations with different initial concentrations are simultaneously analyzed. In this way, conditions for the significant formation of all species can be obtained and consequently the concentration matrix is augmented to full rank. EQUISPEC is a computer program using the matrix based MATLAB environment for second order global analysis of spectrophotometric equilibrium data. The spectrophotometric titrations were carried out at least in four different metal to ligand ratios. At least the values of thermodynamic parameters for those mentioned reactions were determined. Reaction (charge omitted) SQUAD (T=25°C) Global Analysis (T=25°C) M+L® ML 8.02 ± 0.05 7.97 ± 0.07 M+2L ® ML2 6.72 ± 0.04 6.69 ± 0.06 M+H+L ® MHL 2.28 ± 0.04 2.31 ± 0.04
References:
1) J. Ghasemi, A. Niazi and M. Meader, J. Braz. Chem. Soc., 18 (2007) 267. 2) R. Dyson, S. Kaderli, M. Maeder, A. Zuberbuhler, Anal. Chim. Acta, 353 (1997) 381.
96 POSTER -1 s of 97 The The GA- . good mV 5 and stripping [2,3]. and of conditions oxidation simultaneous pulse rate by voltammogram espectively r OSC-PLS the samples to scan performed ential for s, soil , Genova, Italy due fer experimental followed in was dif bismuth, 200 GA-PLS, [1], of ariable Selection 3 and PLS, oblem, bismuth developed method pr optimum by time HMDE adsorptive a 0.5 and The , Arak Branch, Arak, Iran , Arak Branch, Arak, was ficult an on fect of V antimony and dif by , Genova University a for
1.1 GA-OSC-PLS is -1 antimony accumulation , Riccardo Leardi bismuth of 2 mL , Arak Branch, Arak, Iran , Arak Branch, Arak, 5.7, of modulation. samples ng and (GA-OSC-PLS) and echnology soil pulse es Ag/AgCl, 5.6 voltammetry vs. application types 10-750 ential squar by antimony 10.1, mV fer determination ent and of using dif fer least , Samira Sadeghi and 2 86. -400 by dif 365. 8.2 bismuth of in using 10-800 (2008) e and 16.7, (2007) scan simultaneous bismuth 623 wer accumulation 52 bismuth 311. potential and ection-partial the and , Faezeh Jaberi , Faculty of Science, Islamic Azad University , Faculty of Science, and corr Spec., antimony Chem., 1,2 12.3 ranges (2003) of allows e, Sci. oved. e 59 e adsorptive voltammetric oanal. pr signal wer linear an antimony by Anal. accumulation antimony J. was mixtur alanta, Ali Niazi* Electr of T ocedur The of J. of . pr oung Researcher Club, Islamic Azad University oung Researcher Club, di, HCl, Can. bismuth mV involves bismuth M di, Lear 2- Y e This . 30 Amjadi, and R. 4.5 Lear and E. of esolution R. r determination ocedur Niazi, determination determination pr A. The Azizi, Sharifi, the medium algorithm-orthogonal algorithm-orthogonal 1- Department of Chemistry antimony height espectively S. A. r antimony of The oltammetric Determination of Antimony and Bismuth: Ef of Antimony and oltammetric Determination for V 3- Department of Pharmaceutical and Food Chemistry and T and Food Chemistry and 3- Department of Pharmaceutical ences: Niazi, Niazi, pulse Ghasemi, acidity A. J. A. genetic A Comparative Study Between PLS, GA-PLS, OSC-PLS and GA-OSC-PLS in the Simultaneous in the Simultaneous and GA-OSC-PLS OSC-PLS PLS, GA-PLS, Study Between A Comparative e: eliability A voltammetric method. adsorbed ar and simultaneous overlapping. RMSEP OSC-PLS, r Refer [1] [2] [3] POSTER
QSAR/QSPR Study of Toxicity of Nitrobenzene Derivatives and Alcohols by Mechanic Quantum and Structure Descriptor by Chemometrics Methods
Sasan Sharifi1, Ali Niazi*1,2, Fahimeh Rezaei1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
Today information achievement in chemical systems is more easy regard to by gone, that related to take advantage of computer. By helping of computer, mathematics and statistics make a series of chemical rules named it chemometrics that help us in assessment field, interpretation of information, improve and modeling process and tests and extraction of maximum chemical information from experiment data. One of the most important applications of chemometrics is QSAR that proceeded to relationship between biologic activity and chemical structure. Aim of this research is making new QSAR model for examining amount of toxicity of 95 compounds of nitrobenzene derivations and 33 kinds of alcohol. In this research first drawing compounds in Chem-Draw software and than improve in Chem3D software with AM1 semi experimental way. By helping improve structure achieve descriptor, related to WHIM and GETAWAY from Dragon software. Related descriptor deleted taking advantage of PCA. With orthogonal descriptor correction (ODC) tried to achieve best graph of score for both groups of compounds. These graphs help us to determine some molecules as training and using PLS way for regression and modeling of quantitative models. Genetics Algorithm (GA) uses for selecting best descriptors to make another PLS model [1]. Results for both groups as follows: According to statistical parameters and calculating amount ODC-PLS (RMSEP=0.0241) way for nitrobenzene and GA-PLS (RMSEP=0.0342) way for toxicity of alcohol.
References: 1) A. Niazi, S. Jameh-Bozorghi, D. Nori-Shargh, J. Hazard. Mat., 151 (2008) 603.
98 POSTER , 99 The data been Local doing model QSAR, ogenic of have after model. onegativity heter investigations. ODC-PLS (ANN) multivariate electr Finally QSAR . 6–31++G**. phenol QSAR this investigation of set, to Many es Method of network the softness, statistical, accuracy ODC-PLS. basis model. for applied toxicity poor neural eliability Among dness, of r the account known har , Arak Branch, Arak, Iran , Arak Branch, Arak, a 0.0182 1 oven build elatively artificial of r pr extensively taking ediction to with and pr compound. that energies, and been ovides level, the ediction , Arak Branch, Arak, Iran , Arak Branch, Arak, (PLS) pr each pr has method it show YP es for of for as model the B3L or is squar ection-Partial Least Squar ection-Partial Least values err , Farnaz Samnejad the method, e 1,2 603. limited, formed at HOMO–LUMO least , suggested model calculated squar e is (2008) the rather partial wer is of 151 between mean Y) experimental , Ali Niazi* study 1 A performed chemometrics W oot (MLR), polarizability r Mat., and A studies quality was d. used (QSTR) e-Activity Relationships (QSAR) Study of Phenol (QSAR) Study Relationships e-Activity ocessing with (GET the ession QSAR Hazar epr , Faculty of Science, Islamic Azad University , Faculty of Science, J. in egr pr moment, r calculated ability Sasan Sharifi fecting MLR commonly optimization af ODC elationship linear descriptors r of dipole ediction most Nori-Shargh, between pr as factors D. oung Researcher Club, Islamic Azad University oung Researcher Club, scaling, multiple geometry structural high e-toxicity 2- Y as potential, usefulness MLR, eement also initio agr such ogenic by Orthogonal Descriptor Corr ogenic by Orthogonal important Quantitative Structur Quantitative structur Ab showed and ostatic studies. practical good Jameh-Bozorghi, [1]. most 1- Department of Chemistry the S. Heter electr mean-centering, , model methods QSAR the ences: ophilicity in of Niazi, and quantitative A. esulted A derivatives charges, electr one analysis used However PLS parameters r Refer 1) POSTER
Simultaneous Spectrophotometric Determination of Cobalt, Copper and Nickel Using 4-(2-thiazolylazo)-resorcinol by Partial Least Squares and Parallel Factor Analysis
Ali Niazi1,2, Giti Yamini1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
PARAFAC analysis [1, 2] is a multi-way method originating from psychometrics. It is gaining more and more interest in chemometrics and associated areas for many reasons: increased awareness of the method and its possibilities, increased complexity of the data dealt with in science and industry, and increased computational power. A three-way analytical methodology experimentally based on spectrophotometric and parallel factor analysis (PARAFAC) chemometrics analysis was assessed for the quantification of cobalt, copper and nickel in synthetic and water samples. The study was carried out in the pH range from 2.0 to 12.0 and with a concentration from 0.1-1.5, 0.2-1.9 and 0.1-1.9 mg mL-1 of cobalt, copper and nickel, respectively. Multivariate calibration models PLS [3] at various pH and PARAFAC were elaborated from spectra deconvolution. The best models for this system were obtained with PARAFAC and PLS at pH=4.2 (PLS-PH4). The applications of the method for determination of real samples were evaluated by analysis of cobalt, copper and nickel in water samples with satisfactory results. The accuracy of the method, evaluated through the root mean square error of prediction (RMSEP), was 0.2268, 0.1342, 0.2120 with best calibration curve by PARAFAC and 0.3120, 0.2110, 0.2892 by PLS-PH4 model for cobalt, copper and nickel, respectively.
Reference: 1) A. Niazi, J. Ghasemi, A. Yazdanipour, Anal. Lett., 38 (2005) 2377. 2) A. Niazi, M. Sadeghi, Chem. Pharm. Bull., 54 (2006) 711. 3) A. Niazi, A. Yazdanipour, Pharmaceutical Chem. J., 41 (2007) 170.
100 POSTER e of the 101 of graphs studied 0.2178, e e e wer experimental wer ophotometric mixtur determination The calibration ocess spectr 3]. pr copper binary [2, of ophotometric ophotometric and riton X-114= 0.5% (w/v), riton X-114= 0.5% (w/v), between oved. detection simultaneous M) the calibration graphs wer pr -5 simultaneous esolution bismuth varied r performed and the was e e for , Arak Branch, Arak, Iran , Arak Branch, Arak, and The wer . wer phase allows e OSC copper (RMSEP) , Arak Branch, Arak, Iran , Arak Branch, Arak, 1 and and determination espectively r ocedur separation , PLS pr the ediction concentrations the as of pr 523. This bismuth The of copper . such of or fecting (2007) and err eliability , Kobra Karimi af r 67 e 1,2 cations. A, espectively good r squar oxyl naphthol blue concentration=4×10 Part bismuth these chemical chemometrics for of Acta
Ali Niazi* -1 mean 421. e-concentration The es C, hydr samples ° pr e-concentration and Simultaneous Spectr and Simultaneous e-concentration mL OSC-PLS, oot r ochim. for ng (2007) , Faculty of Science, Islamic Azad University , Faculty of Science, e 30 mixtur matrix and The application . 146 25 Spectr race Amounts of Bismuth and Copper by PLS and OSC-PLS Bismuth and Copper by PLS race Amounts of used eal , surfactant. 1020. r PLS the 3.5-24 as Mat., with by and copper been d. and with (2006) 17 X-114 and has azdanipour oung Researcher Club, Islamic Azad University oung Researcher Club, CPE) Y Hazar J. [1] A. , designed 2- Y Soc., synthetic 27-150 0.0116 riton (after T in and of was bismuth Chem. of using copper azdanipour 0.0433, copper range extraction Y Ghasemi Braz. Determination of T Determination matrix 1- Department of Chemistry J. J. A. the and and and Cloud Point Extraction for Pr Extraction Cloud Point Point in ences: Niazi, Niazi, Niazi, A. A. A. Cloud determination equilibrium time=20 min and temperatur and optimized. Under the optimum experimental conditions (i.e., pH=6.0, surfactant concentration T the optimum experimental conditions and optimized. Under linear bismuth calibration concentrations 0.1025 bismuth Refer 1) 2) 3) POSTER
Orthogonal Signal Correction- Partial Least Squares Method for Simultaneous Spectrophotometric Determination of Cobalt, Copper and Nickel
Ali Niazi*1,2, Marjan Mehran1, Masomeh Asgari1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
The simultaneous determination of cobalt, copper and nickel mixtures by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least square (PLS) [1] regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) [2, 3] is a preprocessing technique used in the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. Also, the OSC-filtered data give much simpler calibration models with fewer components than the ones based on original data. The experimental noise can destroy this relation but by removing the noise using OSC filtering, the OSC–PLS score plot depict in a more clear way. In this study, the calibration model is based on absorption spectra in the 220-380 nm range for 21 different mixtures of nickel, cobalt, and copper. Calibration matrices were formed from samples containing 0.1-1.6, 0.1-1.2, and 0.1-1.8 µg mL-1 for copper, cobalt and nickel, respectively. The RMSEP for nickel, cobalt, and copper with PLS were 0.082, 0.117, and 0.112, and with PLS- OSC were 0.015, 0.006, and 0.016 µg mL-1, respectively. This procedure allows the simultaneous determination of nickel, cobalt, and copper in synthetic and real samples and good reliability of the determination was proved.
References:
1) A. Niazi, A. Yazdanipour, J. Hazard. Mat., 146 (2007) 421. 2) A. Niazi, A. Azizi, M. Ramezani, Spectrochim. Acta Part A, 71 (2008) 1172. 3) A. Niazi, J. Zolgharenin, M.R. Davoodabadi, Ann. Chim., 97 (2007) 1181.
102 POSTER a a is of ent of e, may 103 e, initial fer kinds dif including the quantum, combined combined them ocedur ocedur allergic a A on of pr pr sets arious skin V several two of some oposed chemical simple e pr into as employed. selection ough a typical stepwise dependent this we wher e centage oducing 1 was data By such pr compounds. per highly featur the article ficient e by ar ent method coef e Selection Strategy , Arak Branch, Arak, Iran , Arak Branch, Arak, curr depletion splitting topological. glutathione especially models compounds, by the elation the of and of in for corr development steps, es , Shamsi Rafatpanah esulted 1 oss-validation r allergic cr obtained operty Relationship operty Relationship model the , Arak Branch, Arak, Iran , Arak Branch, Arak, selection centage of Skin Allergic of centage of Skin quantum model skin structur e of building calibration e-Pr of QSDR usually descriptors. 72. on a e equation, QSPR featur ar chemical model 100 the for molecular (2007) centage of based as , Mina Montazeri All elation 1 develop on per 592 performances models the to pool corr such them a the Acta, e om QSDR esent splitting fr oposed efor depletion validation). epr ranking Chim. r methodology pr data of , Faculty of Science, Islamic Azad University , Faculty of Science, convenient (or ther descriptors to of was Anal. , Maryam Ghiasi obtained ediction of Depletion Per ediction of Depletion 1,2 and models ent descriptors. most (CDFS) fects fer ediction epeating all of the model building studies. It should be noted that thr epeating all of the model building studies. Elyasi, was ef ediction the pr dif method M. many pr calculated the for 2- Sama College, Islamic Azad University 2- Sama College, Islamic splitting, A Novel Quantitative Structur A Novel e Ali Niazi* model select with geometric and selection wer to oduces Javadnia, e selection initial pr and Model for Pr K. e der this investigate or suggested featur training) SPSS o is In T compounds charge, (or using ten-parametric descriptors 1- Department of Chemistry run, of Glutathione Compounds: A Combined Data Splitting-Featur Glutathione Compounds: study -fitted. splitting set ences: splitting. splitting-featur Hemmateenejad, ession over QSDR B. egr A molecular topological, calibration performed data data training/validation/test sets, and r r be data large multilinear Refer 1) POSTER
Successive Projection Algorithm-Based Wavelength Selection in Multi-component Spectrophotometric Determination by PLS: Application on Copper, Nickel and Zinc Mixture
Ali Niazi*1,2, Masomeh Asgari1, Marjan Mehran1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
Successive Projection Algorithm (SPA) is a suitable method for selecting wavelengths for PLS (partial least squares) calibration of mixtures with almost identical spectra without loss of prediction capacity using spectrophotometric method. The method is based on the development of the reaction between the analytes and hydroxyl naphthol blue (HNB) at pH 7.0. A series of synthetic solution containing different concentrations of copper, nickel and zinc were used to check the prediction ability of the SPA-PLS models. The RMSEP for copper, nickel and zinc with SPA and without SPA were 1.5490, 1.8529, 2.2841 and 4.3256, 4.8962, 5.6321, respectively. Calibration matrices were 0.30-1.20, 0.05-1.20 and 0.05-0.80 mg mL-1 for copper, nickel and zinc, respectively. This procedure allows the simultaneous determination of cited ions in natural, tap and waste waters good reliability of the determination was proved.
References:
1) A. Niazi, A. Yazdanipour, J. Hazard. Mat., 146 (2007) 421. 2) A. Niazi, A. Azizi, M. Ramezani, Spectrochim. Acta Part A, 71 (2008) 1172. 3) A. Niazi, J. Zolgharenin, M.R. Davoodabadi, Ann. Chim., 97 (2007) 1181.
104 POSTER e of es and and and 105 each squar alkanes squar sets; es. descriptors function alkanes least mean a linear potentials as of geometrical squar and oot r linear ediction (RI) pr chemical of least ostatic (PLS) with alkenes and es index index electr partial topological, and test ability quantum squar and for etention some r least etention r including alkanes of training, ser , Arak Branch, Arak, Iran , Arak Branch, Arak, ediction ession 378. the pr LS-SVM egr partial data r of linear oups, of (2008) in high gr of ediction 22 descriptors pr RI ee linear 1,2 (MLR), , Arak Branch, Arak, Iran , Arak Branch, Arak, thr ediction the the ent Chemometrics Methods ent Chemometrics pr in oduction showed for of molecules ession fer multiple intr the chemical all egr r to , Ali Niazi* for model the 1 divided for Chemometrics, 603. J. Modeling linear that suggested . Also, quantum is (2008) superior applied esulted r e Retention Relationship Study Relationship e Retention e etc. randomly Heinzen, 151 . shown some study The multiple wer .E.F studies was e. V Mat., espectively the have r d. set Mehrana Motiee of (QSRR) energies, unes, QSRR models calculate Y ocedur in data Hazar to pr , Faculty of Science, Islamic Azad University , Faculty of Science, Results J. R.A. means LUMO members The These by used 25 and elationship [2]. ediction r Junkes, pr LS-SVM. was modeling and of Nori-Shargh, Silva for Quantitative Structur Quantitative the 54 calculated. HOMO D. oung Researcher Club, Islamic Azad University oung Researcher Club, da e etention theory in (LS-SVM) established B. ability 1- Y 110, e–r wer 0.0024 not atom, was the initio of e of es Kuhnen, Ab each of Linear Alkanes and Alkenes using Dif of Linear Alkanes wer machines structur at [1]. C.A. Jameh-Bozorghi, 1- Department of Chemistry structur enhances parameters ediction S. which consisting vector pr ences: Souza, onic of charges Niazi, alkenes them or E.S. A. quantitative A and local electr of molecular support alkenes, err drastically Refer 1) 2) POSTER
Quantitative Structure Retention Relationship Study of Linear Alkanes and Alkenes using Different Chemometrics Methods
Mehrana Motiee1, Ali Niazi*1,2 1- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran 2- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran
A quantitative model was developed to predict the antimicrobial activity of synthesized 2,4-hexadienoinc acid derivatives [1]. Each kind of compound was represented by several calculated structural descriptors involving constitutional, topological, geometrical, electrostatic and quantum-chemical features of compounds. Modeling of the antimicrobial activity of 2,4-hexadienoinc acid derivatives as a function of molecular structures was established by means of the partial least squares algorithm. The subset of descriptors, which resulted in a low prediction error, was selected by genetic algorithm [2, 3]. This model was applied for the prediction of the antimicrobial activity of some 2,4-hexadienoinc acid derivatives, which were not in the modeling procedure. Relative errors of prediction lower than 0.8% were obtained by using the genetic algorithm-partial least squares (GA-PLS) method. The developed model has good prediction ability with a root means square error of prediction of 0.1493 and 0.0231 for PLS and GA-PLS models, respectively.
References:
1) B. Narasimhan, V. Judge, R. Narang, R. Ohlan, S. Ohlan, Bioorg. Med. Chem. Lett., 17 (2007) 5836. 2) J. Ghasemi, A. Niazi, R. Leardi, Talanta, 59 (2003) 311. 3) A. Niazi, A. Azizi, R. Leardi, Can. J. Anal. Sci. Spec., 52 (2007) 365.
106 POSTER ) e e 6 B 107 225- wer wer . The es minimum range (vitamin component simultaneous the PC-WNN the samples. espectively chitectur for ar and over principal pyridoxal obtain ehran, Iran plasma to the and , T ) network 2 of PC-ANN B PC-WNN es . human absorbance 3 scor ANN, optimized and layer the e echnology (vitamin the , Arak Branch, Arak, Iran , Arak Branch, Arak, with
wer 6 input B es synthetic model an in and riboflavin
measuring as 2 ), 1 B , Arak Branch, Arak, Iran , Arak Branch, Arak, by B , 1 chitectur B used PC-WNN , ar 0 component-wavelet-neural component-wavelet-neural vitamins e B In avelet-Neural avelet-Neural oosi University of T , Jahanbakhsh Ghasemi (vitamin wer for 1 designed these 2609. e PC-WNN of principal ocessing technique [2]. The wavelet neural network (WNN) is a ocessing technique [2]. The wavelet neural was network. of RMSEP on (2004) mixtur 32. thiamine ), 37 0 matrix The B neural based (2006) Lett., variables 16 determination calibration The Anal. (vitamin artificial vitamins. the calibration the , Pegah Saligheh Fard all dai, (JSIAU), algorithm the PCs. acid for 1,2 , Faculty of Science, Islamic Azad University , Faculty of Science, 37. , Faculty of Science, K.N. T , Faculty of Science, of for Mor of
and -1 Principal Component-W Principal I.A.U. folic A. L novel m data a of (1987) Sci. applied 2 J. mg Ali Niazi* experimental Nadaf, number E. Syst., transforms The oposed ent 1-20 oung Researcher Club, Islamic Azad University oung Researcher Club, esponse Soltani, r pr Lab. fer avelet transform is a powerful signal pr Niazi, of K. set. 2- Y successfully dif the A. we Network as Multivariate Calibration Method for Simultaneous Calibration Method Network as Multivariate determination wavelet Intell. of was the using samples [3] ophotometric Determination of Folic Acid, Thiamine, Riboflavin and Pyridoxal of Folic Acid, Thiamine, ophotometric Determination work, ediction Abbasi, of pr by B. Nori-Shargh, 30 1- Department of Chemistry 3- Department of Chemistry method Chemom. D. the esent (PCA) for pr Spectr ophotometric old, ences: for nm Niazi, W Ghasemi, or the J. A. S. oposed In Spectr in human plasma [1]. W combination analysis constructed err 450 0.7561, 0.6891, 0.9652, 0.6889 and 0.5123, 0.2365, 0.5715, 0.5834 and 0.1254, 0.1394, 0.1024, 0.3102, r 0.7561, 0.6891, 0.9652, 0.6889 and 0.5123, pr Refer 1) 2) 3) POSTER
Extraction and Simultaneous Spectrophotometric Determination of Copper and Cobalt by TAN With Partial Least Squares
Ali Niazi*1,2, Reza Moradi1 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
Partial least squares modeling is a powerful multivariate statistical tool applied to extraction spectrophotometric simultaneous determination of mixtures of copper and cobalt. The method is based on the formation of complexes of TAN with copper and cobalt. The TAN complexes are quantitatively extracted into dichloromethane and the resolution of the mixtures is accomplished by partial least squares (PLS) [1]. Orthogonal signal correction (OSC) [2] is a preprocessing technique used in the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 350-750 nm range for 25 different mixtures of copper and cobalt. Calibration matrices ranges were 1.0-300.0 and 1.0-200 ng mL−1 and detection limits were 0.4 and 0.3 ng mL−1 for copper and barium, respectively. A series of synthetic solutions containing different concentrations of copper and cobalt was used to check the prediction ability of the PLS and OSC-PLS models. The RMSEP for copper and cobalt with OSC and without OSC was 0.7317 and 0.1884, 8.2738 and 5.2563, respectively. The method was successfully applied to the analysis of spiked water (natural, tap and waste waters) samples.
References:
1) J. Ghasemi and A. Niazi, Microchem. J., 68 (2001) 1. 2) J. Ghasemi and A. Niazi, Talanta, 65 (2005) 1168.
108 POSTER by the PLS and 109 ficult in by esults. r dif analysis solutions nickel a the is nickel the cadmium and for of attention and satisfactory method cobalt, spectra method with cadmium for
cadmium the -1 of considerable mL nickel g cobalt, m cobalt, ophotometric and absorbance of for e , Arak Branch, Arak, Iran , Arak Branch, Arak, eceived r The capability 2 spectr 0.4-1.3 es Support by The mixtur cadmium . have (RMSEP) and nickel. of nickel , Arak Branch, Arak, Iran , Arak Branch, Arak, es in Simultaneous es in Simultaneous (PLS) and cobalt, es and of ediction espectively 0.15-1.0 r , Faezeh Jaberi pr 2 esolution r PLS. squar of cadmium or The 0.004, than cadmium err least 536. 0.3-1.15, e cobalt, determination esults r 0.005, ences. of 86. (2008) squar cobalt, partial the 19 ranges, of , Samira Sadeghi by better (2008) and 0.003, 1,2 interfer Soc., means 623 and oot 311. r oduced Chem. determination , Faculty of Science, Islamic Azad University , Faculty of Science, spectra evaluated pr (LS-SVM) The Chem., 0.083 e Ali Niazi* concentration (2003) to determination Braz. J. 59 wer , the oanal. due LS-SVM 0.104, in , machines alanta, Electr ed T performed. that ophotometric J. samples A Comparative Study Between Least-Squar Study Between A Comparative ophotometric Determination of Cobalt, Cadmium and Nickel of Cobalt, Cadmium ophotometric Determination oung Researcher Club, Islamic Azad University oung Researcher Club, di, ector Machine and Partial Least Squar ector Machine and 0.078, azdanipour V Y simultaneous e was vector chemistry 2- Y Lear spectr A. measur matrix Amjadi, R. wer The e . E. Spectr explained eal r wer Niazi, methods support data Goodarzi, A. models analytical and Sharifi, es 1- Department of Chemistry M. S. in simultaneous espectively r analytes ences: to esultant Niazi, Niazi, LS-SVM r Ghasemi, the synthesis A. A. J. oblem Least-squar chemometrics for multicomponent analysis. A comparison was made between LS-SVM [1, 2] and PLS [3] methods by applying made between LS-SVM [1, 2] and PLS analysis. A comparison was chemometrics for multicomponent them of nickel, pr chemometrics and of The Refer 1) 2) 3) POSTER
Spectrophotometric and Thermodynamic Determination of Acidity Constants of Hydroxy Naphthol Blue in Different Solvents by DATAN
Ali Niazi*1,2, Simin Moradi1, Sadaf Mahmoudzadeh2 1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran 2- Young Researcher Club, Islamic Azad University, Arak Branch, Arak, Iran
The acidity constants of Hydroxy Naphthol Blue (HNB) were determined spectrophotometrically at 15-35oC and at constant ionic
-1 strength 0.1 mol L (KNO3) in pure water as well as in aqueous media containing variable mole percentages (10-50%) of organic solvents. The organic solvents used were methanol, ethanol, N,N-dimethyl formamide (DMF), dimethyl sulfoxide (DMSO), acetonitrile and dioxan. The acidity constants of all related equilibria are estimated using the whole spectral fitting of the collected data to an established factor analysis model. DATAN program was applied for determining of acidity constants and pure spectra of different form of Hydroxy Naphthol Blue. The obtained results indicated that acidity constants decrease as the content of an organic solvent in the medium increases. There are linear relationship between acidity constants and the mole fraction of various organic solvents in the solvent mixtures. Effect of various solvents on acidity constants and pure spectrum of each component are also discussed. At least the values of thermodynamic parameters for those mentioned reactions were determined.
References:
1) A. Niazi, M. Ghalie, A. Yazdanipour, J. Ghasemi, Spectrochim. Acta Part A, 64 (2006) 660. 2) A. Niazi, A. Yazdanipour, J. Ghasemi, A. Amini, Sh. Bozorgzad, M. Kubista, Chem. Eng. Commun., 195 (2008) 1257. 3) A. Niazi, J. Zolgharnein, M.R. Davoodabadi, Spectrochim. Acta Part A, 70 (2008) 343.
110 POSTER . - ol to ee NIR der and that 111 ANN ANN thr or make esults . r Raman contr and models in A Iran. Springer making, abilities, to eduction common edictions (PLS) Publishers, r of calibration ycopersicon The e the pr demand espectively r (L PLS2 the out fruit. second juice depicted of in-line higher a data om of ediction Practice. squar e or espectively ehran, Iran or Science fr edictive of stage. a r using 335–342. pr , T pr serious Republic index, err tomato 3 and wer as carried tomato Most a e 43: least in , SSC, color e The multivariate of The Elsevier eparation, oducts [4]. color for Islamic maturity wer ones, pr ed. pr Theory squar been and components) [1]. partial applied applications oduced ed and combination fields pH 0.38 pr has their echnology ANN develop a T two s textbook. ol ehran, mean e
was T tomato c to a compar sample on i , by SSC and (PCR), o parameters r analyses models and and t of t first measur oot (SSC), Ther a contr r e , N. Mohammadi 1 also industrial for m 0.30 e. m e based the ence in ANN o ession o metabolites The oduction acidity Biology oscopy T versus arbiat Modares University University including algorithm wer m 0.09 attributes f efer quality egr for Intr r content r e while [3]. qualitative (with and o h Chemometrics: s i samples and and PCA spectr agricultur the C s and work, values 0.38, d y secondary solids output models l Modares the sugars n Postharvest , B. Gobadian a selected 1 and etation (1988). Networks, 0.15 with n a used and centering and Chemometrics L. main y Raman grading built es component of A manual edicted p arbiat was solids, soluble e T o among was the and attributes. pr 0.33 interpr Neural use v of the c i science mean as s t of featur spectra to techniques. of o a r sorting, t by Kaufman, e i PCA USA) t Principal RMSEPs t c the in essing such food oscopy Sciences, n e Artificial and wer calibrating amount opagation a obtained in p egr of analytical Analysis L. escence r spectral u avakoli Hashtjin with S classification spectr simplicity . T n was Q followed study fruit om fluor and tomato (1995). a e including Scientific, between , T fr back-pr 2 and v Faculty (2007). i m es of espectively). t this r a Michotte, index of R. c using mapping Raman major of u R considerable r Simultaneously calibration g a t nondestructively A.J.M.M. for featur S.N., esulted SSC, derivative s use Physics, n r methods (Thermo application color a mapping is i e s to ripening Fantoni, distinction d der fruit 221–227. and the used n U and involve parameters (2007). eijters, algorithm t Mill) e , R. Malekfar o i 1 make fruit and objectives Deming, Atomic W this spectral ficients 595: u . N r and color wer desirable of first-or F ning G.P and and The a 488. Calibration potential instrument the coef quality favorable M.M.C. Acta, during for E. could modeling a B.G.M., and lear Pp: [2]. essi, eat 293. complexity (ANN) for eira, made gr Sor tissue the esculentum, major oach a Pp: elation Raman attributes spectra. make Chimica r=0.91 Ferr consuming Molecular E., is between PCR and A.M. Nikbakht e Thuijsman, of corr appr with the and andeginste, and epicarp smoothing and 3- Department of Electrical Engineering, Faculty of Engineering, University of Urmia, Urmia, IRAN. Engineering, Faculty of Engineering, 3- Department of Electrical time limitations networks could V Netherlands. RJ., quality ther for on the edicting obust e r Analytica pr ar PLS models Heidelberg. . The e, Mill.) dispersive the D.L., ycopersicon be computational that A.M.K. ceptr Santangelo, by A , neural (L for (r=0.94 spectra to o, intelligent dam, Berlin ences: Department 1- Department of Agricultural Machinery Engineering, Faculty of Agriculture, T Machinery Engineering, Faculty 1- Department of Agricultural per A., oscopy oaches. laboratory 2- an Pedr Massart, Braspenning, oved erlag omato T determine techniques and models spectra. calibration methodology artificial layer accuracy pr Savitsky–Golay (RMSEP) as Furthermor models Raman indicated appr Refer 1) V 2)Lai, esculentum, [3] Amster [4] spectr POSTER
Identification of Binding Mode and Determination of Binding Constant Between DNA and Quinones by Chemimetrics Programs
Hossein Peyman1, Mohammad Bagher Gholivand2, Soheila Kashanian2, Hamideh Roshanfekr1 1- Islamic Azad University of Ilam 2- Razi University of Kermanshah, Department of Chemistry
In this work, contrary to routine works, we use three steps for the investigation of the interaction mode of compounds with DNA and obtain the calculation parameters as follows: First step is including obtain the number of species that participate in the interaction [1-7]. In this step we used PCA method and INDICES program [8]. Second step is the recognition of the interaction mode by instrumental experiments. In this step the results of the first step is very useful, because with consider of the obtained number of species and our knowledge about way of changes for every interaction, it was possible the explanation the obvious changes in different methods and then selection of correct interaction mode and the best molecular formula for products [9]. And finally third step is including the calculation of parameters such as binding constant by the predesigned equations and calculation soft wares. In this step we used SQUAD and EQUISPEC programs [8]. In this study we recognition of mode of interaction and determination of binding constant between DNA and Danthron and Quinizarin by chemometrics programs that as mention above. In first step determined four species in during of interaction that tow species of them are DNA and Quinone and other species are new compounds that product, in second step with consider of species number and way of UV-Vis spectra change we suggest H-biding and intercalation modes for interaction, and finally in third step for each mode binding constant was determined by SQUAD and EQUISPEC programs that are 1600 and 1000 respectively.
References: 1) Malinowski, E. R. Factor Analysis in Chemistry, 2nd ed.; Wiley: New York, 1991. 2) Meloun, M.; Havel, J.; Ho¨gfeldt, E. Computation of Solution Equilibria; Horwood: Chichester, 1988. 3) Malinowski, E. R. Abstract factor analysis of data with multiple sourcesof error and a modified Faber-Kowalski F-test. J. Chemom. 1999,13, 69- 81. 4) Malinowski, E. R. Determination of the number of factors and the experimental error in a data matrix. Anal. Chem. 1977, 49 (4), 612-617. 5) Elbergali, A. K.; Nygren, J.; Kubista, M. An automated procedure to predict the number of components in spectroscopic data. Anal. Chim.Acta 1999, 379 (1), 143-158. 6) Dean, J. M. In MultiVariate Pattern Recognition in Chemometrics Illustrated by Case Studies; Brereton, R. G., Ed.; Elsevier: Amesterdam,1992. ı 7) Meloun, M.; Cÿ apek, J.; Miksˇ ´k, P.; Brereton, R. G. Critical comparison of methods predicting the number of components in spectroscopic data. Anal. Chim. Acta 2000, 423 (1), 51-68. 8) Ghasemi, H. Peyman, and M. Meloun, Study of Complex Formation between 4-(2-Pyridylazo) Resorcinol and Al3+, Fe3+,Zn2+, and Cd2+ Ions in an Aqueous Solution at 0.1 M Ionic Strength. J. Chem. Eng. Data 2007, 52, 1171-1178.
112 POSTER of 2]. AN T 113 such metal [1, favors (metal DA perform 1 of by fields that 1: 4.68±0.10, and e para-tert-butyl ar using many determination cavity eactions r interactions
guest in the p for exclusively -rich p target ene used obtained complexes cation- form between their ehran, Iran. the important to was to was is complexation es very with e, e . (PCA) elated r shown ar efor ogram complex formation metal-ligand pr ces structur of interact They Ther been of for . AN analysis
f T ene ehran Branch, T espectively r DA have complex 2+ LogK constant ert-Butyl Calix[n]ar Zn binding solution. selectively chemistry in Calix[n]ar and
ligands component studied can 2+ , Central T constant, the All Pb we , of formation conditions. 2+ which analytical equilibria Ni noncovalent (1998). Principle the , of Stability work, 3+ by and With Para-T aspect Cr methods. 2+ and , this 10482 systems 2+ In solvent. Co other 120, experimental host matrix stabilized medicinal for e important and Zn investigation our with Soc., ar new ophotometric, 2+ data organic and studied. in ed the in , Islamic Azad University One (1997). that ophotometric Studies of Complexation Studies of ophotometric , Pb for Chem. Spectr 2+ design ions 4.66±0.11 1303 widely studies, to cations Am. compar ene , Ni J. Amir H. M. Sarafi, Afsaneh Amiri, Fatemeh Pirouzi* Amir H. M. Sarafi, 97, ocesses. and . absorbance ., 3+ metal Sepctr pr been and with made Rev the compounds parameters , Cr in onmental data 2+ Spencer calix[n]ar have been key .A. Chem. guest T 4.65±0.10 . transition envir be , ecognition r have complexes of Co ligands can some Department of Chemistry charged ent components ratio) Ditchfield, Dougherty and ophotometric of fer chemistry of para-tert-butyl R.
4.67±0.11, attempts molecular dif D.A. ene ds: constants spectr ligand ous ences: Miklis, Ma, with to number inclusion the .C. industrial J.C. P Numer intriguing the Stability as ions calix[n]ar the on ion 4.66±0.11, Keywor Refer 1) 2) POSTER
Prediction of the Retention Time GC-MS of Organic Compounds Based on Molecular Structural Descriptors Using MLR and Wavelet-Neural Network Methods
Z. Garkani-Nejad, H. Rashidi-Nodeh Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran.
A quantitative structure properties relationship (QSPR) study has been done on the Retention time of 31 organic compounds using multiple liner regression (MLR) and Wavelet-artificial neural network (WNN) modeling methods. The Dragon and Hyperchem softwares were employed for calculating molecular descriptors. Then multiple linear regression (MLR) procedure was employed for selecting suitable subset of descriptors. The best selected 4 descriptors were (H2m, HATS2m, IC4, and MATS1v). Then selected 4 descriptors were used as inputs for a Wavelet Packet 2-D and artificial neural network (ANN) model. The best artificial neural network model was a fully-connected, feed forward back propagation network with a 4-4-1 architecture. Correlation coefficient between the experimental and calculated values of Henry constant for the WNN and MLR models were R = 0.976 and R = 0.952 for the training set and R = 0.992 and R = 0.939 for the test set, respectively. Comparison of the results indicates that the WNN method has a better predictive power than the MLR method.
References:
1) S. Cheng, J. Liu, Bioresource Technology 100 (2009) 457–464 2) M. Shafafi Zenoozian, S. Devahastin, Journal of Food Engineering, 2 (2009) 219-227 3) HYPER CHEM, software, Version 7.0. Hypercube, Inc., (2002). 4) MATLAB, Mathworks Inc., Version 7.6.0. USA. (2008a).
114 POSTER e. ed the 115 some (ANN) that ediction of wavelet- ) descriptor pr compar chitectur 1/2 a ar be (E best the as network indicates The For . 5-7-1 should a neural potential esults r employed with values methods. the espectively r artificial was of These half–wave . ANN, network (MLR), modeling , Rafsanjan, Iran the and algorithm on -ANN Comparison ession espectively 411-430 r -ANN WT egr opagation done r pr WT Stepwise and model. (2001) ANN, liner been the back ork, ANN MLR First, and Y for has d -ANN in Calculating -ANN in ali-e-Asr University in 219-227 for New multiple methods. -ANN sets
forwar (QSER) INC, (2009) WT R=0.928 inputs methods. 2 using other as the feed and study the ediction Dekker for pr used cel Ketones e than modeling and Mar Engineering, R=0.998 wer and e R=0.933 Z. Garkani-Nejad, H. Rashidi-Nodeh Z. Garkani-Nejad, H. power Food -ANN) Relationship wer of ave Potential of Some Organic Compounds ave Potential of training and fully-connected, (WT set nal a Aldehyds ochemistry'', the edictive descriptors Jour 73 pr for was electr of Comparison of ANN and WT of ANN and Comparison R=0.977 ochemistry training network of Half-W better selected was the model Electr R=0.735 Devahastin, ''organic e for neural very S. a Then, containing Chemistry department, Faculty of Science, V Chemistry department, ficient and e. has network ficient coef artificial 0.807 Hammerich, coef Zenoozian, ocedur = O. pr method R neural elation compounds ences: the Shafafi wavelet- Lund, elation corr -ANN Quantitative-Structur M. H. A organic and selection artificial Corr set, with WT Refer 1) 2) POSTER
Simultaneous Spectrophotometric Determination of Silicate and Phosphate in Boiler Water of Power Plant andSewage Sample by Partial Least Squares and Simplex Design Methods
M. Rohani1, S. Dadfarnia1, M. A. Haji Shabani1, Jahan B. Ghasemi2 1- Chemistry Department, Faculty of Sciences, Yazd University, Yazd, Iran 2- Chemistry Department, Faculty of Sciences, K.N.T University of Tech., Tehran, Iran
Partial least squares modeling as a powerful multivariate statistical tool applied to the simultaneous spectrophotometric determination of silicate and phosphate in aqueous solutions. The concentration range for silicate and phosphate were 20-600 ppb and 0.4-3 ppm, respectively. The experimental calibration set was composed with 30 sample solutions using a mixture design for two component mixtures. The absorption spectra were recorded from 500 to900 nm. The optimum conditions were obtained with simplex method. The values of root mean square difference (RMSD) for silicate and phosphate using partial least squares (PLS) were 1.703ppb and 0.02 ppm, respectively. The effects of various cations and anions on detection of silicate and phosphate were investigated. The method was used for determination of silicate and phosphate in boiler water at power plant, well water and sewage samples.
References: 1.Kurita Kōgyō Kabushiki Kaisha, Kurita Handbook of Water Treatment, Kurita Water Industries, 1985. 2.Tetsuyuki Taniai, Masaaki Sukegawa, Akio Sakuragawa and Atsushi Uzawa,Talanta,2003,61,905-912. 3. F. J. Valle Fuentes and S. del Barrio Martín, Analyst, 1995, 120, 85 – 88. 4.Mary Ann H. Franson , Standard Methods for the Examination of Water and Wastewater, American Public Health Association, 1981. 5. A.Youssef El-Sayed, Y.Z.Hussein and M.A.Mohammed, Analyst, 2001, 126, 1810-1815. 6. F. Mas-Torres, A. MunÕz, J. M. Sela and V. Cerda, Analyst, 1997, 122, 1033-1038. 7. Cristiane X. Galhardo, Jorge C. Masini, Anal. Chim. Acta, 2000,417, 191–200. 8. K. Grudpan a,b, P. Ampan a, Y. Udnan, S. Jayasvati, S. Lapanantnoppakhun, J. Jakmunee, G.D. Christian, J. Ruzicka, Talanta,2002, 58, 1319-1326. 9. P.Linares , M.D.Luque de castro,M.Val Carcel,Talanta,1986,33,889-893. 10. F.M s, J.M.Estela,V.Cerd ,Inter.J.of Envi. Anal. Chem., 1991, 43, 71-78. 11.Yoshio Narusawa, and Takahiro Hashimoto, Chemistry Letters,1987,16,1367-1370. 12.K. Grudpan, P. Ampan, Y. Udnan, S. Jayasvati, S. Lapanantnoppakhun, J. Jakmunee, G. D. Christian,and J. Ruzicka, Talanta,2002,58, 1319-1326. 13.Yong-Sheng Li, Yu Muo, Hou-Mei Xie, Anal. Chim. Acta, 2002,455, 315–325. 14.Kobra Zarei,Morteza Atabati, and Mehdi Nekoei, Annali di Chimica,2007,97,723-731. 15.Abbas Afkhami and Maryam Abbasi-Tarighat, Anal. Sci. 2008, 24,779-783. 16. Nobutake Nakatani, Daisuke Kozaki, Wakako Masuda, Nobukazu Nakagoshi,Kiyoshi Hasebe, asanobu Mori,Kazuhiko Tanaka, Anal. Chim. Acta, 2008, 619,110-114. 17.Mikaru Ikedo, Masanobu Mori,Kazumasa Kurachi,Wenzhi Hu,and Kazuhiko tanaka, Anal.Sci.,2006,22,117-121. 18. J. GhasemiU, A. Niazi, Microchemical Journal, 2001, 68, 1-11. 19.H. Martens, T. Naes, Multivariate Calibration, John Wiley, New York, 1991. 20.D.M. Haaland, E.V. Thomas, Anal. Chem. 1998, 60, 1193. 21. K.R. Beebe, B.R. Kowalski, Anal. Chem.1987, 59,1007A. 22. S. Wold, P. Geladi, K. Esbensen, J. Ochman, J. Chemom.1987,41,1
116 POSTER to on an
the -7 117 as months Based variables ene espect, 9.2×10 Ion r two the om this fr clozapine-triiodide Plackett-Burman least ontium In 455–475. calix-6-ar on at Str between on levels range for for (1960) based 2 two used based linear ode conditions. Sensor the be elationship ions r electr elative to alkali, alkaline earth and wide can ocess a (I) ode 438-447. ode, the It pr Tl echnometrics, ode r T over electr for (2007) best elate Potentiometric
7 membrane ion 4.5-10. the corr of e to sensor variables”, esponse r Sensors, triiodide oposed electr Membrane acquir range e”, eated thallium(I) the cr to pH PVC for on quantitative membrane polymeric was the of Ionophor of in adopted Sensitive esponse model study r Neutral chloride) and was influencing ene using Experimental Design ene using Experimental a the activity as for of nstian “Optimization (RSM) ahya Kazemi, Akram Sadat Hamidi ahya Kazemi, Akram Ner Selective 320-326. polynomial a a poly(vinyl designs A of significantly (2008) new level mv/decade Salehiyan, Sayed Y a good. e developed as experimental strategies for modeling and optimizing of the influence of strategies for modeling and optimizing e developed as experimental 76 . ee exhibits that F “Design applied. of thr methodology alanta T was new sensor Design of a New Thallium(I)-Selective Electr a New Thallium(I)-Selective Design of 58.5±0.1 Based on Calix[6]ar Sharghi, variables of surface Shokatynia, The H. of “Some design comparatively D. design”, dioxacyclohexadecane-2,9-dione performance slope ode. a was Kazemi, the eening . Response Bahram, electr Behnken, ions Department of Basic Science, Agriculture and Natural Resources University of Sari, Iran Science, Agriculture and Natural Resources Department of Basic scr with . on S.Y
, -1 of M. experimental , the stastistical design wer , the stastistical design For D.W used. Box-Behnken metal e. mol.l
Box, -2 using was . variables Farhadi, ences: levels esponse Shamsipur r ee Kh. M. G.E.P In this study some ionophor design thr and 1.2×10 without any considerable divergence in potential. The selectivity of the pr without any considerable divergence in potential. transition Refer 1) 2) ion-pair 3) 1,10-Diaza-5,6-benzo-4,7 POSTER
The Components of the Iranian Rosemary Essential Oil Characterized and Identified Using (GC-MS) Combined With the Curve Resolution Techniques
Mehdi Jalali – Heravi1, Rudabeh–Sadat Moazeni1, Hassan Sereshti2 1- Department of Chemistry, Sharif University of Technology, Tehran, Iran. 2- Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran.
Characterization and determination of the complex mixture of the essential oil of Rosemary were identified using gas chromatography – mass spectrometry (GC-MS) combined with the chemometric resolution techniques. Various chemosmetric methods such as morphological score (M.S.) [1] and fixed size moving window - evolving factor analysis (FSMW-EFA) [2] were used for determining the number of components, zero concentration and selective regions. Orthogonal projection resolution (OPR) [3] and heuristic evolving latent projection (HELP) [4] as non-iterative methods and augmented multivariate cure resolution - alterative least squares (Aug-MCR-ALS) [5] as an iterative method were used for analyzing the overlapping/embedded peaks. A total of 68 components were identified by direct similarity searches. This number was extended to 138 with the help of the chemometric techniques. Major constitutions of the Rosemary essential oil were 1,8-Cineole (23.47%) , a-Pinene (21.74%) , Champhor (7.21%) and Berneol (3.38%).
References:
1) H. Shen, L. Stordrange, R. Mane, O.M. Kvalheim, Y.Z. Liang, Chemom.Intell. Lab. Syst. 51(2000)37. 2) M.Maeder, A.D.Zuberbuhler, Anl. Chim. Acta 181(1986)287. 3) Y.Z. Liang, O.M. Kvalheim, Anal. Chim. Acta 292(1994)5. 4) O.M. Kvalheim, Y.Z. Liang, and Anal. Chem 64(1992)936. 5) S. Navea, A. de Juan, R. Tauler, Anal. Chim. Acta 446 (2001)187.
118 POSTER in ce 31 set set the the the test 119 For of These test Air -1.029(± esent for V training encode indicate methanol work. factor a epr S r States , of and V this 0.83) oss-validation in oups; values (± cent cr gr and United etention r , per two espectively These the r +1.351 variable ent , F=2571.853 B fer into efraction r compositions. Laboratory dif and , Iran basicity ch six between 0.086) leave-many-out divided at phase (± molar and independent zesear SPRESS=0.138. of as model mobile -1.355 of and RMSE=0.131 acidity randomly A (1983), used elationships (RP-HPLC) excess r e e 2008. 455 bond wer the various the wer =0.941 edibility 0.042) 2 in ol. is cr R=0.973, 1190, Q V (± E find data e ogen of the to wer olume omatography hydr QCPE, V 0.308 RP-HPLC phase, set – parameters A, chr used in etention S the r ogram, examined was parameters 186 (LFER) , University of Mazandran, Babolsar , University of Mazandran, Pr specifications: liquid mobile esent training 0.089) etention r epr (± r for 2009 and these further omatography Orbital B o T 44, chr statistical technique following step solute -0.335 and of elationships solute r of the E the performance A first nal olume (MLR) V Molecular parameters , has the Jour high gives energy 0.050) in F=402.938. terms. ediction Seyedeh Maryam Sadeghi, Mohammad Hossein Fatemi Seyedeh Maryam Sadeghi, between ession (± ee V pr model fr .Poole, phase Empirical and in which Chemistry egr Then statistical ediction of Retention Factor of Organic Compounds in Compounds in of Organic of Retention Factor ediction r C.F Department of Chemistry and ent Mobile Phase Compositions in RP-LC by LFER Parameters Compositions in RP-LC by LFER ent Mobile Phase 0.220 set Pr S Semi 1075,2005 evers + Linear r fer E, intraction linear obtained solute. in data Medicinal AC, B, of Dif equation olume of parameters (1990). best V A, 0.111) phase. the 6 MOP RMSE=0.167 nal A, this e; (± M.H.Abraham, the on multiple LFER ar volume Jour for set of version mobile compounds , 3.898 Stewart, lnP omatog work as . ences: test R=0.959, opean applied a K= e Chr J.J.P this J, M.H.Fatemi, Eur In organic water parameters polarity/polarizability McGowan and Log 0.028) wer was suitability Refer 1) 2) 3) 4) Academy POSTER
Development and Validation of a Reversed-Phase HPLC Method for Simultaneous Estimation of Carbamazepine and Phenytoin Using an Experimental Design
E. Konoz1, M.H. Fatemi2, H. Baghri sadeghi1, Sh. Lashgari1 1- Department of Chemistry, Islamic Azad University, Central Tehran Branch. 2- Department of Chemistry, Mazandaran University, Babolsar.
A simple, precise and accurate reversed- phase HPLC was developed and validated to measure simultaneously of Carbamazepine and phenytoin. A central composite design was used. The factors considered in the optimization process were percentage v/v of acetonitril, methanol and flow rate. The chromatographic separation was performed using a C18 column. An optimized mobile phase of acetonitril/methanol/water (20:50:30 v/v) at a flow rate of 1 mlmin-1 was used. Quantitation was accomplished with the internal standard method, the procedure was validated by linearily, accuracy, robustness and precision. The method was found to applicable for determination of the drugs in tablet formulations and the results of the developed method were compared with those of the UV spectrophotometric method to access the active titled drugs content [1].
Reference:
1) G.Srinubabu, K. Jaganbabu, B. Sudharani. K. Venugopal, G. Girizasankar, J. V. L. N. S. Rao.Chromatogr. 64 (2006) 95-100.
120 POSTER e e, of by on the the and 121 with e(IV) black T in to natur 2. ds, 20-300 in bir and based due agricultur tellurium(IV) e range fish, Chapter determined selenium sylvanite, extent Se(IV) and determination e comparison by futur model onics, as time of in alloys. with ed 1994, wer the the certain electr such in ork, in Y equir to some r found eactions to nm r is ease selenium(IV) calibration New simultaneous ehran,Iran found of ,T 524 incr to tellurium(IV) ding tellurium(IV) Inc. tellurides, , at onutrient of and may (PLS-1) tellurium 3 ession and oxidation accor manufacturing, ophotometric micr ellurium applied Dekker of ed es T a form e in Alloy Samples also, cel echnology as glass [2]. 369-373. rate epar determination the squar 1996. absorbance Mar selenium pr in es Regr in the selenium(IV) the for pyrites, alloys selenium(IV) successfully least uses in Survey) of on editors; of , Ali Reza Zarei ence ecognized 512(2004) riton X-100, in acid medium and 25°C. The absorption riton X-100, in acid medium and 25°C. The 2 ir r lead metals was has es fer in ease dif Acta, Demand also simultaneous partial Benson compositions other is decr 1095-1098. with other Geological mixtur it for the ellurium Mixtur the method had Chim. and the Selenium met on but (U.S. The with determination
113-123. that oduction. onment", Anal. [1]. 42(1995) brass , , Bu-Ali Sina University Hamadan, Iran , Bu-Ali Sina University life, work, pr oposed methods based , Abbas Afkhami 1 pr Envir is works. measuring ellurium", this health alanta, T the samples T 361(1998) The occasionally by , Faculty of Sciences, Lorestan University Khoramabad,Iran , Faculty of Sciences, in In Shamsipur everyday e combination plumbing pigment develop acid and ed simultaneous ar in M. in good Acta, Feng, in method to and J. the [4,5]. and eaction. est synthetic r The lead used esence of a nonionic surfactant, T Nahid Sarlak Chim. Selenium ,"Selenium and monitor for chiefly , sulphuric the inter e maintain quantities is of of several Anal. eplace chemical but Ghasemi oducts Chen e r wer to samples 2- Faculty of Chemistry A Comparison of Partial Least Squar of Partial A Comparison in J. pr Surveys: X. in to ement, described. Small ther in is ang, and deposits initiation W Frankenberger also only . and Artificial Neural Networks for Kinetic Spectr and Artificial Neural Industry solutions humans) Mousavi, Sangiorgio, T measur X. condition, . . . industrial 3- Faculty of Applied Sciences, Malek-AshtarUniversityof T Sciences, Malek-AshtarUniversityof 3- Faculty of Applied P selenium [3], not Determination of Selenium and T Determination after W the M.F tellurium(IV) 1- Department of Chemistry and networks ang, of tetradymite. and and oduction, chamber W "Mineral using and . pr found Y Rossi or (including of and is intervals methods ofiles S. neural own, s pr uncombined alloy Br 2 Mayland ophotometric ences: ent . dust, F Khajehsharifi, Zhang, Ferri, biological . the fer H. R.D. B. T H. with Selenium mammals metal possibility in tellurium, flue dif in spectr artificial ions with permanganate ion in the pr kinetic s selenium(IV) Refer 1) 2) 3) 4) 5) POSTER
Modeling of Methylene Blue Electroactive Label Signal in Pencil Graphite Based DNA Biosensors
M.S. Hejazi 1, 2, R.E. Sabzi 3, 4, F. Golabi 2, B. Sehatnia3 1- Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran 2- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran 3- Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran 4- Institute of Biotechnology, Urmia University, Urmia, Iran
Mathematical modeling of methylene blue (MB) signal in ssDNA and dsDNA on pencil graphite electrode is described. In order to study MB signal property in the electrode, single stranded oligonucleotide immobilized as the probe on the electrode with highest current signal was taken as ssDNA. The probe and target DNAs was 20 mer oligonucleotides corresponding to consensus sequence of HPV major capsid protein L1 gene. Hybrids of various complementary and non-complementary oligonucleotides with the probe were considered as dsDNA with different hybridization degrees. Modeling was carried out by incorporation of only stable forms of dsDNA hybrids. Effect of hybridization degree on current signal in various forms was studied. A factor named AHP (Average Hybridization Percentage) for verifying the hybridization events was defined. Results showed that there is a significant mathematical relation between the calculated AHP and MB signals.
Keywords: biosensor, HPVp, pencil graphite electrode, methylene blue, modeling, DNA hybridization.
References:
1) M.S. Hejazi, E. Alipour and M.H. Pournaghi-Azar, Immobilization and voltammetric detection of human interleukine-2 gene on the pencil graphite electrode, Talanta, 71 (2007) 1734 2) W. Yang, M. Ozsoz, D.B. Hibbert and J.J. Gooding, Evidence for the Direct Interaction Between Methylene Blue and Guanine Bases Using DNA- Modified Carbon Paste Electrodes, Electroanalysis, 14 (2002) 1299 3) M.H. Pournaghi-Azar, M.S. Hejazi and E. Alipour, Developing an electrochemical deoxyribonucleic acid (DNA) biosensor on the basis of human interleukine-2 gene using an electroactive label, Anal. Chim. Acta, 570 (2006) 144 4) R.E. Sabzi, B. Sehatnia, M.H. Pournaghi-Azar and M.S. Hejazi, Electrochemical detection of Human Papilloma Virus (HPV) target DNA using MB on pencil graphite electrode, J. Iran. Chem. Soc. 5 (2008) 476. 5) M.S. Hejazi, B. Sehatnia, E. Alipour, M.H. Pournaghi-Azar and R.E. Sabzi, Electrochemical Detection and Discrimination of Human Papilloma Virus (HPV) Target DNA in mixed DNA Compounds (Submitted). 122 POSTER
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at pr samples The espectively be r 3.5 2.0-14.6 simultaneous eal and r pH can for and wastewater fast of and , method. and fer es Method , Shahin-shahr 2 conium(IV), oposed buf 2.0-11.5 technique zir pr oposed synthetic simplicity es pr samples and for of was The acetate . echnology , Shahreza Branch. Shahreza, Iran , Shahreza Branch. in obtained water e eal applied r least-squar done wer
selectivity be of e thorium(IV) technique 4+ espectively 1141-1151. r 13-18. of Zr for wer can 181-188. 181-188 partial (PLS) spiked and , Hamid Reza Pouretedal
on 1 ppm, (2003) (4-Sulfopheney-azo)-naphthaline-3,6-disulfonic (4-Sulfopheney-azo)-naphthaline-3,6-disulfonic e (2006) ppm 4+ in
(2003) (2003) 59 4+ Th ements method 131 Zr . squar of based 487 487 investigation alanta oxy-2- and 1.8±0.2 2.0-14.6
T , , Islamic Azad University to Mater 4+ Acta Acta measur least oposed Th and ranges and der pr method dous and of or Chim. Chim. Behnaz Shafiee Jafarifar The in partial linear D. . Hazar agent Anal. Anal. 1.7±0.3 J. 2.0-11.5 751-756. the ions.1,8-Dihydr of using Azimi, studied Simultaneous Determination of Thorium(IV) and of Thorium(IV) Determination Simultaneous conium(IV) Ions Using Partial Least Squar conium(IV) Ions H. e ophotometric (2004) determination G. ADNS, orkestani, orkestani, Zir Abdollahi, T T 63 wer ranges e. SP B. complexing determined K. K. method simultaneously spectr nein, a M e conium(IV) is 2- Faculty of Science, Malek-ashtar University of T 2- Faculty of Science, alanta as T zir (IV), -V wer ocedur anions 1- Department of Chemistry pr Ahmadi, Ahmadi, Zolghar UV Ghasemi, Seifi, used J. and J. simultaneous A and Sh. S. Sh. 1.0×10-4 concentration conium for was of ophotometric detection oposed zir the pr cations ence: of in Abdollahi, Ghorbani, and Ghasemi, Ghasemi, Ghasemi, ADNS) spectr the J. J. H. R. J. esence A thorium(IV) (SP pr limits some employed Conclusion: (IV) ions of 1) Refer 2) 3) 4) 5) POSTER
Prediction of IAM-LC Retention of Some Drugs From Their Molecular Structure Descriptors and LFER Parameters
Hoda Shamseddin, Mohammad Hossein Fatemi Department of Chemistry, University of Mazandran, Babolsar, Iran
In this work multiple linear regression (MLR) was carried out for the prediction of retention factor of 40 basic and neutral drugs in immobilized artificial membrane chromatography. IAM chromatography [1-5] was performed using morpholinepropane sulfonic acid (MOPS) and phosphate buffer saline (PBS) at pH 7.4 as the aqueous component of the mobile phase. We find a Quantitative Structure Retention Relationship (QSRR) model by using linear free energy relationships (LSER) parameters [6, 7] and also
IAM IAM theoretical derived molecular descriptor. Root mean square error in MLR models in prediction of log k wPBS and log k wMOPS are 0.332 and 0.351, respectively, while these values are 0.371 and 0.500 for LFER model. Inspection to these value indicate that the statistical parameters of MLR model are better than LFER model. The credibility of obtained MLR model was evaluated by using leave-many- out cross-validation and y-scrambling tests. The selected descriptors of obtained MLR model are: number of C atoms (nC), FHASA fractional HASA (HASA/TMSA) ( ), min (>0.1) bond order of N atom ( N ), number of CL atoms (nCL), average bond order of N atom ( min ). Cross-validation test was applied to MLR models in order to further examine the validity of obtained models. The PN statistics of leave-five-out cross validation test is Q2=0.927 and Q2=0.913 for MLR model, in prediction of IAM and IAM respectively. The results of examination indicate the applicability of theoretical derived molecular log kPBS log kMOPS descriptors in QSRR prediction of immobilized artificial membrane retention of basic and neutral drugs
References:
1) S. Ong, H. Liu, X. Qiu, C. Pidgeon, J. Chromatogr. A 728 (1996) 113–128. 2) T. Salminen, A. Pulli, J. Taskinen, J. Pharm. Biomed. Anal. 15 (1997) 469–477. 3) A. Reichel, D.J. Begley, Pharm. Res. 15 (1998) 1270–1274. 4) A. Taillardat-Bertschinger, F. Barbato, M.T. Quercia, P.-A. Carrupt, M. Reist, M.I. La Rotonda, B. Testa, Helv. Chim. Act. 85 (2002) 519. 5) A. Taillardat-Bertschinger, C.A.M. Martinet, P.-A. Carrupt, M. Reist, G. Caron, R. Fruttero, B. Testa, Pharm. Res. 19 (2002) 729. 6) MH. Abraham, A. Ibrahim, AM. Zissimos , J. Chromatogr. A 1037 (2004) 29-47. 7) M. Vitha, PW. Carr, J. Chromatogr. A 1126 (2006) 143-194.
124 POSTER T X of for for the the the of 125 than of on variable loadings assessed than matrix thumb vector (activity), terms spite variables The (MLR) the or of ough In larger in e. err thr datasets rule latent variable ession a and ends times tr the variables. egr As r literatur complicated of (QSAR) five variables the e matrix the of linear or in mor dependent most studies. err oposed as a new simple variable individual or of numbers at equations: e the the explaining number elationships ar e The multiple r QSAR eported in r of ar terms vector the f e for and that ones. the following those and ease e-Activity Relationships Data e-Activity Relationships mor PLS E
e-activity used loadings variables models. , Hamedan, Iran two . of decr with y(n×1) of optimal variables the the to cases, ed in is latent of of esults structur the r form e these commonly ar espectively the (PLS) r the , compar e oblem , In y in es ar number pr (descriptors), descriptors 417. and significance the this 87-985941-0-9. data. quantitative squar estingly X the (MLR) to descriptors the esented of to the ISBN statistically two (1979) with Inter pr least , Bu-Ali Sina University variables e of 112 ession about 1996, equal wer (activity). often selected 1. esults. solutions egr loadings partial r is r Acta -fitting the the Employing the performed
exactly models [2-4] (2004) of e Chim. Masoumeh Hasani*, Masoud Shariati-Rad Masoumeh Hasani*, over linear on be eliable edictand independent r 44 211. pr information data. of Copenhagen, wer One esponding ariable Selection Method Based on the Partial Least Based on the Method ariable Selection Anal. Sci. showing and must the event derived based ection-sum of PLS latent loadings (DOSC-SPLSLL) has been pr ection-sum of PLS latent loadings (DOSC-SPLSLL) [1]. corr have pr steps.PLS old, (1988) to is Faculty of Chemistry multiple matrix 2 W the models . to obust the as r H. Publishing, Comput. q the identical, model and elated auto-scaled e modeling Inf. loadings method such gave eduction Thor and r the A Simple V A Simple the corr eported P 1, wer r e in esents PLS The Kowalski, Chemometr . ar es, Chem. om method J. vol. the compounds epr J. fr r method, B.R. es Loadings: Application to Quantitative Structur es Loadings: Application method used of variable scor the methods that e the of of ar the method. MLR and f , of descriptors E + espectively ect orthogonal signal corr Hawkins, X(n×m) Gerlach, Squar + r ences: . extracted , e Hoskuldsson, Hoskuldsson, y Tq modeling matrix e variables TP D.M. A. A. R.W = = y Multivariate applying number necessary selection X
wher the and the The dir selection wer validation simplicity selected PLS Refer 1) 2) 3) 4) POSTER
Investigation of Optimum Extraction Conditions for Determination of Quercetin in Sea Parsnip (Echinophora Spinosa L.) by Using Experimental Design and HPLC.
Mohammadreza Hadjmohammadi, Vahid Sharifi Department of Chemistry, University of Mazandaran, Babolsar, Iran.
Flavonoids are natural products widely distributed in the plant kingdom [1] and are well known due to their antioxidant [2] and anticancer [3] properties. Thus, the extraction of these phytochemical compounds is essential if they are to be of prophylactic or therapeutic value in human health. The traditional one-factor-at-a-time approach to process extraction optimization is time consuming and the interactions among various factors may be ignored [4]. In this project, optimum conditions for extraction of quercetin from Sea parsnip (Echinophora Spinosa L.) which is one of the vegetables in Iranian diet were determined using experimental design and HPLC method. Leaves and stems of Sea parsnip were collected from western Iran, (Sanandaj, Kurdistan province). Central composite design was used to investigate effects of five experimental factors including: volume of extraction solvent, percentage of methanol in aqueous extraction solvent, concentration of HCl, extraction time, and temperature. Method of stepwise multiple linear regression (MLR) was employed to select the most important factors and to calculate the coefficients relating these factors to extraction recovery of quercetin. Grid search method was used to find the optimum conditions for extraction of this compound. Optimum conditions were 25 mL of 70% aqueous methanol containing 2.0 M HCl refluxed for 3 hours at 90°C. The extraction at optimum conditions for quercetin show good repeatability with relative standard deviations of
%3.6 and recoveries about 96%. HPLC system consisted of a Spherisorb C18 (250×4.6 mm, 5 mm) column and UV detector set at 370 nm. The appropriate mobile phase for separation and determination was mixture of acetonitrile, 0.025 M phosphate buffer (25/75 v/v) with pH= 2.4 and flow rate of 1 mlmin-1. Limit of detection (LOD), Linear dynamic range, Intra-day and Inter-day R.S.D for quercetin was 0.19, 0.19- 48.0, mgml-1 (R2 0.99), 3.0% and 4.0% respectively.
References:
1) B. Heimhuber, R. Galensa, K. Herrmann, J. Chromatogr., 1988, 439: 481-483. 2) Cao, G. H., Sofic, E. and Prior, R. L., Free Radic. Biol. Med., 1997, 22:749-760. 3) Ren, W., Qiao, Z., Wang, H., Zhu, L. and Zhang, L. Med. Res. Rev., 2003, 23:519-534 4) M. Wettasinghe, and F. Shahidi, J. Agric. Food Chem., 1999, 47 , 1801-1812.
126 POSTER ] e is α of oss [3]. and 5- 127 now then cr wer most AM1 draw 0.60), is e to [1, cytosolic xanthine the wer inhibitor a eatment of model ethanol be method tr is es used to enzyme pteridin-4[3H]- It for (10.97%, MLR was e oxidase This [2]. of semi-empirical structur Leave-One-Out oxypyrazolo selection or inhibition wer a Inc.) [1]. thought by exogenous err is , Ilam, Iran indicated substituted it ago dihydr out e step, humans Xanthine and cube, of model
ar variable 50 to optimized humans, and as oxypyrazolo oxypyrazolo years IC absolute In Hyper MLR carried of The 70 tissues. purines 7, used oxidase of models. mean was substituted bacteria or diseases, 0.01. development was nearly es err of and om ediction For hypoxic fr (version . pr was esulted xanthine of r metabolism. e model serum the endogenous structur the algorithm rat the absolute for of of spectrum abarak inhibit validation species, purine in In softwar gradient espectively e of in r oss that wide and mean cr Genetic a studied molecular eoxygenation QSAR. , Reza T r of squar 3. sets, of ago oxidation range , Faculty of science, Ilam University , Faculty of science, and in or the was involved performance err HyperChem the of . mean during wide ersion the V medications century a for gout. e method oot (QSAR) a r validation in elative wo Novel Classes of Xanthine Oxidase Inhibitors wo Novel Classes validation oxidase 195–217 r 58:87–114. 9: pathogenesis the over damage espectively several oss and and evaluate r derivatives. softwar Rev cr the mean optimization Shahin Salimpour to cell including milk until Chem modeling esponsible and in of r sets, xanthine 25:750–755. elationship in r as The tissues and or of Med ed rans acid, err T Pharmacol DRAGON ent calibration pyrimidine used Curr ] employed uric Soc derivatives, conditions by algorithm fer a - validation implicated radicals e-activity for e activity was of dif discover 5 (2006) was elative molecules. r edominantly ee (2002) e C and fr the F pr [1, been first Biochem medical the 0.30) (MLR) is many structur mean Szabo of descriptors was in oduction ocedur Roleira since e A, that oxygen pr pr (1997) inhibits E, elated r ession Polack–Rabier of has calibration (XO) ] Pyrimidine Derivatives, T ] Pyrimidine Derivatives, 1497 (7.25%, CM the esent α QSAR Study of Substituted Pteridin-4[3H]-One and Dihydr Pteridin-4[3H]-One of Substituted QSAR Study egr pteridin-4[3H]-one that oxypyrazolo ce r ozhkin and the otein for pr Chemometrics Lab, Department of chemistry Chemometrics Lab, structur nandes quantitative derivatives, Harris be sour Fer (LOO-CV) Nivor , oxidase oxidase 0.22), , , V dihydr educes linear 0.58) F using P [1, 5 - r to calculate work, ences: to and substance chemical substituted this Massey Borges Pacher Xanthine known metalloflavopr Xanthine important any oxidase hyperuricemia In one the method used multiple validation For (4.93%, pyrimidine (8.60%, Refer 1) 2) 3) POSTER
Application of Orthogonal Array Design for the Optimization of Sample Preparation for Determination of Chromium, Copper, Lead, Iron, Manganese, Molybdenum, Nickel and Zinc in Human Hair by Flame and Electrothermal Atomic Absorption Spectrometry
Fariba Tadayon1, Mohammad Saber Tehrani1, Mahmod. R. Sohrabi2, Shiva Motahar2 1- Department of Chemistry, Science and Research Branch, Islamic Azad University, Tehran,Iran 2- Department of chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
Metal pollution and its health effects present a challenge currently facing the developing countries. Metal poisoning is usually difficult and expensive to assess or screen in these countries because of limited resources, which means that policies, guidelines, regulations and institutional managements are limited. So, in this regard the analysis of human hair is useful in monitoring the levels of certain trace elements in the body. Hair samples can be collected easily and painlessly, and they require no special care when stored. Unlike blood, hair gives information about the intracellular accumulations of trace elements. Sample preparation is the critical step of any analytical protocol, and involves steps from simple dilution to partial or total dissolution. In this study, orthogonal array design (OAD) was applied for the first time to optimize pre-treatment and determination of lead, chromium, copper, iron, manganese, molybdenum, nickel and zinc in human hair by flame and electrothermal atomic absorption spectrometry. The risk of sample contamination during the sampling and digestion was minimized. Prior to analysis, hair samples were washed with solution 1% (w/v) sodium diethyldithiocarbamate. Four relevant factors, i.e. volume of nitric acid and hydrogen peroxide, digestion time and temperature were selected and the effects of each factor were studied at three levels on the digestion efficiency of human hair samples, and optimized for determination of heavy metals. The statistical analysis revealed that the most important factors contributing to the digestion efficiency are temperature and volume of nitric acid. The optimized method has been employed to digest hair samples of residents of Iran. Also, the average composition of hair from Tehran (heavy industry dominates) was compared with the average composition of hair from the population living in a non-industrialized area of Iran (Takab province).
References:
1) Frisch M., Schwartz B.S., Environ. Health Perspect., 110 (2002) 433 – 436. 2) Sreenivasa Rao K., Balaji T., Prasada Rao T., Spectrochim. Acta., 57 (2002) 1333 – 1338. 3) Pereira R., Ribeir R., F. Goncalves, Sci. Total Environ., 327 (2004) 81 – 92. 4) Taguchi G., Elsayed E.A., Hsiang T., Quality Engineering in Production Systems, McGraw-Hill, New York, 1989. 5) Madaeni S.S., Koocheki S., Chem. Eng. J., 119 (2006) 37 – 44. 128 POSTER ) ,
e of -1 Cr eat L ent cial not and well 129 g wer trace gr is fer m owing critical ement Co, of samples hair of gr dif and trueness. analytical of 1.0 for method is , commer is esults to of of hair r non-invasive hair a measur samples e a ehran, Iran (0.5 human dose , T in the with hair along common ough Zn elements of Estimated analysis, nal a exposur thr determination The ehran, Iran to uncertainty is assessment uncertainty . solutions and (1.098±0.007) inter , T bias. toxic The the ehran,Iran 4 changes the ometry Prior of Mo, calibration to water , T human , in ement statement collected ometry Cr a is AAS constant calibration it months. estimate Co, ET dynamic assess o spectr as measur T of to the described. . two deionized to the biomarkers e individuals e. of calculated , A. Ghorbani without ar 3 157 e of of - (0.973±0.012)and and ar used, weeks used e AAS 151 is tracing ocedur calculate uncertainty ) ET , Saveh Branch, Saveh, Iran , Saveh Branch, Saveh, bias absorption HCl, om pr ehrani advantages ement to fr -T evaluation complete 2008 determination widely ( exposur 6 using 711-716. . , be the 0.1M the 19(2005)195–201. , addition, several atomic measur technique , , Mo, and Zn Determination Zn Determination , Mo, and expanded to 176 constant method In been analytical ranging (1.036±0.110), for ─ has Review ed and
the , M. Saber the and 2 171 assessing
Biology (DDTC), 388(2007) hair have used However ouden Y othermal and calibration and Science
determination was elements. consider d time-scale Chem, and calculate selection calibrate of electr 440(2001) o on samples AAS not to T ement oportional , Islamic Azad University specimen, (0.904±0.017), e othermal Atomic Absorption Spectr othermal Atomic . bias Medicine 2 ET by is Pr Bioanal standar O Acta, in laboratories. elements 2 elements hair used opriate H urine Measur the Anal esult Zn work, r ., exposur Chim. S appr , Science and Research Branch, Islamic Azad University , Science and Research these and sample , and M., for testing , 3 Elements , N. Mashkouri Najafi oportional ) trueness. of 1 concentrations R. -1 essential the diethyldithiocarbamate esent pr Anal. L o hair pr race hosain g
of (1.019±0.026), HNO T blood average m advantages, application .X.., analytical F of the is: of to Thus, higher aqif chemical ement Uncertainty of Co, Cr of Co, ement Uncertainty J. adayon e In W sodium
into An this assessing ed 10.0 human status Rius, . T N., hair it. uncertainty many F In and calculating J., in many 5.0, ., (w/v) health. mixtur Cespon-Romer ( contains M for with opose in . a Riu, insight , their compar used. Measur 4- Department of chemistry pr and human 1% in R., and an to and in Human Hair by Electr in Human Hair ; d C. in A.,Violante , expanded was nutritional we 2- The University of Shahid Beheshti School of Science Dept. of Chemistry Shahid Beheshti School of Science Dept. 2- The University of human d When M. applied implemented. Rabbani with Mo the Due ., Boque, the for technique associated espectively ovides A 3- Department of Chemistry r of , and A., manner pr Alimonti concentrations ecovery afterwar r Zn standar design Cr G. obtained, ences: washed oto, and e Co, ebra-Biurrun 1- Ph D. Student of Chemistry Department, Science and Research Branch, Islamic Azad University Chemistry Department, Science and 1- Ph D. Student of ection Forte Y Mar Ghorbani outine esult Monitoring importance importance. contaminants. sampling dir element established systematically uncertainty evaluation wer digested r for r Nested average Mo Refer 1) 2) 3) 4) POSTER
Statistical Process Control of Edible Salt Production to Improve Salt Quality at National Standard Level
Gholamreza Vatankhah*1, Nahid Tavakkoli2, efat Asghari2 1- Faculty of Conservation, Art University of Isfahan 2- Chemistry Department, Isfahan Payame Noor University
Statistical process control (SPC) is a collection of powerful problem solving techniques, which is useful in achieving process stability as well as improving process capability through the reduction of variability and can be used in any process. SPC has been widely employed in many industries, but relatively little has been written on the successful application of SPC in the food industry. Among the many SPC methods, control charts have been found to be the most popular SPC tool in food industry [1,2]. In this study some of characteristics of edible salt in a refined and crystalline edible salt factory were investigated. The production variables studied were included Calcium, Magnesium and Sulfate impurity level and purity of Sodium Chloride. Characterization of edible salt was done according to National Iranian Standard. Minitab release 15.1 was used to construct the graphical outputs as well as for statistical calculations in this study. Xbar-R control charts have been constructed on the data obtained from this manufacturing line to discover and correct assignable causes, so that the process capability can be determined [3].Xbar-R control charts indicated that some observed variables were not in statistical control and capability results show that they were out of tolerance limits and production process was instable. Through processing data and follow up studies some assignable causes for faulty product were discovered and all observed variables except Magnesium were converted as capable and process was improved. All these activities show that in small sized companies, statistical quality control could be a useful component of production line provided that sufficient finance and qualified personal are utilized.
References:
1) Srikaeo, K., Furst, J.E., Ashton, J., "Characterization of wheat-based biscuit cooking process by statistical process control techniques" Food Control 16 (2005) 309- 317. 2) Ben, M., Jiju, A., "Statistical process control: an essential ingredient for improving service and manufacturing quality" Managing Service Quality 10 (2000) 233-238. 3) Motorcu AR., Gullu A., " Statistical process control in machining, a case study for machine tool capability" Materials and Design 27 (2006) 364- 372.
130 POSTER in ce 31 set set the the the test 119 For of These test Air -1.029(± esent for V training encode indicate methanol work. factor a epr S r States , of and V this 0.83) oss-validation in oups; values (± cent cr gr and United etention r , per two espectively These the r +1.351 variable ent , F=2571.853 B fer into efraction r compositions. Laboratory dif and , Iran basicity ch six between 0.086) leave-many-out divided at phase (± molar and independent zesear SPRESS=0.138. of as model mobile -1.355 of and RMSE=0.131 acidity randomly A (1983), used elationships (RP-HPLC) excess r e e 2008. 455 bond wer the various the wer =0.941 edibility 0.042) 2 in ol. is cr R=0.973, 1190, Q V (± E find data e ogen of the to wer olume omatography hydr QCPE, V 0.308 RP-HPLC phase, set – parameters A, chr used in etention S the r ogram, examined was parameters 186 (LFER) , University of Mazandran, Babolsar , University of Mazandran, Pr specifications: liquid mobile esent training 0.089) etention r epr (± r for 2009 and these further omatography Orbital B o T 44, chr statistical technique following step solute -0.335 and of elationships solute r of the E the performance A first nal olume (MLR) V Molecular parameters , has the Jour high gives energy 0.050) in F=402.938. terms. ediction Seyedeh Maryam Sadeghi, Mohammad Hossein Fatemi Seyedeh Maryam Sadeghi, between ession (± ee V pr model fr .Poole, phase Empirical and in which Chemistry egr Then statistical ediction of Retention Factor of Organic Compounds in Compounds in of Organic of Retention Factor ediction r C.F Department of Chemistry and ent Mobile Phase Compositions in RP-LC by LFER Parameters Compositions in RP-LC by LFER ent Mobile Phase 0.220 set Pr S Semi 1075,2005 evers + Linear r fer E, intraction linear obtained solute. in data Medicinal AC, B, of Dif equation olume of parameters (1990). best V A, 0.111) phase. the 6 MOP RMSE=0.167 nal A, this e; (± M.H.Abraham, the on multiple LFER ar volume Jour for set of version mobile compounds , 3.898 Stewart, lnP omatog work as . ences: test R=0.959, opean applied a K= e Chr J.J.P this J, M.H.Fatemi, Eur In organic water parameters polarity/polarizability McGowan and Log 0.028) wer was suitability Refer 1) 2) 3) 4) Academy POSTER
Development and Validation of a Reversed-Phase HPLC Method for Simultaneous Estimation of Carbamazepine and Phenytoin Using an Experimental Design
E. Konoz1, M.H. Fatemi2, H. Baghri sadeghi1, Sh. Lashgari1 1- Department of Chemistry, Islamic Azad University, Central Tehran Branch. 2- Department of Chemistry, Mazandaran University, Babolsar.
A simple, precise and accurate reversed- phase HPLC was developed and validated to measure simultaneously of Carbamazepine and phenytoin. A central composite design was used. The factors considered in the optimization process were percentage v/v of acetonitril, methanol and flow rate. The chromatographic separation was performed using a C18 column. An optimized mobile phase of acetonitril/methanol/water (20:50:30 v/v) at a flow rate of 1 mlmin-1 was used. Quantitation was accomplished with the internal standard method, the procedure was validated by linearily, accuracy, robustness and precision. The method was found to applicable for determination of the drugs in tablet formulations and the results of the developed method were compared with those of the UV spectrophotometric method to access the active titled drugs content [1].
Reference:
1) G.Srinubabu, K. Jaganbabu, B. Sudharani. K. Venugopal, G. Girizasankar, J. V. L. N. S. Rao.Chromatogr. 64 (2006) 95-100.
120 POSTER e e, of by on the the and 121 with e(IV) black T in to natur 2. ds, 20-300 in bir and based due agricultur tellurium(IV) e range fish, Chapter determined selenium sylvanite, extent Se(IV) and determination e comparison by futur model onics, as time of in alloys. with ed 1994, wer the the certain electr such in ork, in Y equir to some r found eactions to nm r is ease selenium(IV) calibration New simultaneous ehran,Iran found of ,T 524 incr to tellurium(IV) ding tellurium(IV) Inc. tellurides, , at onutrient of and may (PLS-1) tellurium 3 ession and oxidation accor manufacturing, ophotometric micr ellurium applied Dekker of ed es T a form e in Alloy Samples also, cel echnology as glass [2]. 369-373. rate epar determination the squar 1996. absorbance Mar selenium pr in es Regr in the selenium(IV) the for pyrites, alloys selenium(IV) successfully least uses in Survey) of on editors; of , Ali Reza Zarei ence ecognized 512(2004) riton X-100, in acid medium and 25°C. The absorption riton X-100, in acid medium and 25°C. The 2 ir r lead metals was has es fer in ease dif Acta, Demand also simultaneous partial Benson compositions other is decr 1095-1098. with other Geological mixtur it for the ellurium Mixtur the method had Chim. and the Selenium met on but (U.S. The with determination
113-123. that oduction. onment", Anal. [1]. 42(1995) brass , , Bu-Ali Sina University Hamadan, Iran , Bu-Ali Sina University life, work, pr oposed methods based , Abbas Afkhami 1 pr Envir is works. measuring ellurium", this health alanta, T the samples T 361(1998) The occasionally by , Faculty of Sciences, Lorestan University Khoramabad,Iran , Faculty of Sciences, in In Shamsipur everyday e combination plumbing pigment develop acid and ed simultaneous ar in M. in good Acta, Feng, in method to and J. the [4,5]. and eaction. est synthetic r The lead used esence of a nonionic surfactant, T Nahid Sarlak Chim. Selenium ,"Selenium and monitor for chiefly , sulphuric the inter e maintain quantities is of of several Anal. eplace chemical but Ghasemi oducts Chen e r wer to samples 2- Faculty of Chemistry A Comparison of Partial Least Squar of Partial A Comparison in J. pr Surveys: X. in to ement, described. Small ther in is ang, and deposits initiation W Frankenberger also only . and Artificial Neural Networks for Kinetic Spectr and Artificial Neural Industry solutions humans) Mousavi, Sangiorgio, T measur X. condition, . . . industrial 3- Faculty of Applied Sciences, Malek-AshtarUniversityof T Sciences, Malek-AshtarUniversityof 3- Faculty of Applied P selenium [3], not Determination of Selenium and T Determination after W the M.F tellurium(IV) 1- Department of Chemistry and networks ang, of tetradymite. and and oduction, chamber W "Mineral using and . pr found Y Rossi or (including of and is intervals methods ofiles S. neural own, s pr uncombined alloy Br 2 Mayland ophotometric ences: ent . dust, F Khajehsharifi, Zhang, Ferri, biological . the fer H. R.D. B. T H. with Selenium mammals metal possibility in tellurium, flue dif in spectr artificial ions with permanganate ion in the pr kinetic s selenium(IV) Refer 1) 2) 3) 4) 5) POSTER
Modeling of Methylene Blue Electroactive Label Signal in Pencil Graphite Based DNA Biosensors
M.S. Hejazi 1, 2, R.E. Sabzi 3, 4, F. Golabi 2, B. Sehatnia3 1- Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran 2- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran 3- Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran 4- Institute of Biotechnology, Urmia University, Urmia, Iran
Mathematical modeling of methylene blue (MB) signal in ssDNA and dsDNA on pencil graphite electrode is described. In order to study MB signal property in the electrode, single stranded oligonucleotide immobilized as the probe on the electrode with highest current signal was taken as ssDNA. The probe and target DNAs was 20 mer oligonucleotides corresponding to consensus sequence of HPV major capsid protein L1 gene. Hybrids of various complementary and non-complementary oligonucleotides with the probe were considered as dsDNA with different hybridization degrees. Modeling was carried out by incorporation of only stable forms of dsDNA hybrids. Effect of hybridization degree on current signal in various forms was studied. A factor named AHP (Average Hybridization Percentage) for verifying the hybridization events was defined. Results showed that there is a significant mathematical relation between the calculated AHP and MB signals.
Keywords: biosensor, HPVp, pencil graphite electrode, methylene blue, modeling, DNA hybridization.
References:
1) M.S. Hejazi, E. Alipour and M.H. Pournaghi-Azar, Immobilization and voltammetric detection of human interleukine-2 gene on the pencil graphite electrode, Talanta, 71 (2007) 1734 2) W. Yang, M. Ozsoz, D.B. Hibbert and J.J. Gooding, Evidence for the Direct Interaction Between Methylene Blue and Guanine Bases Using DNA- Modified Carbon Paste Electrodes, Electroanalysis, 14 (2002) 1299 3) M.H. Pournaghi-Azar, M.S. Hejazi and E. Alipour, Developing an electrochemical deoxyribonucleic acid (DNA) biosensor on the basis of human interleukine-2 gene using an electroactive label, Anal. Chim. Acta, 570 (2006) 144 4) R.E. Sabzi, B. Sehatnia, M.H. Pournaghi-Azar and M.S. Hejazi, Electrochemical detection of Human Papilloma Virus (HPV) target DNA using MB on pencil graphite electrode, J. Iran. Chem. Soc. 5 (2008) 476. 5) M.S. Hejazi, B. Sehatnia, E. Alipour, M.H. Pournaghi-Azar and R.E. Sabzi, Electrochemical Detection and Discrimination of Human Papilloma Virus (HPV) Target DNA in mixed DNA Compounds (Submitted). 122 POSTER
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T , , Islamic Azad University to Mater 4+ Acta Acta measur least oposed Th and ranges and der pr method dous and of or Chim. Chim. Behnaz Shafiee Jafarifar The in partial linear D. . Hazar agent Anal. Anal. 1.7±0.3 J. 2.0-11.5 751-756. the ions.1,8-Dihydr of using Azimi, studied Simultaneous Determination of Thorium(IV) and of Thorium(IV) Determination Simultaneous conium(IV) Ions Using Partial Least Squar conium(IV) Ions H. e ophotometric (2004) determination G. ADNS, orkestani, orkestani, Zir Abdollahi, T T 63 wer ranges e. SP B. complexing determined K. K. method simultaneously spectr nein, a M e conium(IV) is 2- Faculty of Science, Malek-ashtar University of T 2- Faculty of Science, alanta as T zir (IV), -V wer ocedur anions 1- Department of Chemistry pr Ahmadi, Ahmadi, Zolghar UV Ghasemi, Seifi, used J. and J. simultaneous A and Sh. S. Sh. 1.0×10-4 concentration conium for was of ophotometric detection oposed zir the pr cations ence: of in Abdollahi, Ghorbani, and Ghasemi, Ghasemi, Ghasemi, ADNS) spectr the J. J. H. R. J. esence A thorium(IV) (SP pr limits some employed Conclusion: (IV) ions of 1) Refer 2) 3) 4) 5) POSTER
Prediction of IAM-LC Retention of Some Drugs From Their Molecular Structure Descriptors and LFER Parameters
Hoda Shamseddin, Mohammad Hossein Fatemi Department of Chemistry, University of Mazandran, Babolsar, Iran
In this work multiple linear regression (MLR) was carried out for the prediction of retention factor of 40 basic and neutral drugs in immobilized artificial membrane chromatography. IAM chromatography [1-5] was performed using morpholinepropane sulfonic acid (MOPS) and phosphate buffer saline (PBS) at pH 7.4 as the aqueous component of the mobile phase. We find a Quantitative Structure Retention Relationship (QSRR) model by using linear free energy relationships (LSER) parameters [6, 7] and also
IAM IAM theoretical derived molecular descriptor. Root mean square error in MLR models in prediction of log k wPBS and log k wMOPS are 0.332 and 0.351, respectively, while these values are 0.371 and 0.500 for LFER model. Inspection to these value indicate that the statistical parameters of MLR model are better than LFER model. The credibility of obtained MLR model was evaluated by using leave-many- out cross-validation and y-scrambling tests. The selected descriptors of obtained MLR model are: number of C atoms (nC), FHASA fractional HASA (HASA/TMSA) ( ), min (>0.1) bond order of N atom ( N ), number of CL atoms (nCL), average bond order of N atom ( min ). Cross-validation test was applied to MLR models in order to further examine the validity of obtained models. The PN statistics of leave-five-out cross validation test is Q2=0.927 and Q2=0.913 for MLR model, in prediction of IAM and IAM respectively. The results of examination indicate the applicability of theoretical derived molecular log kPBS log kMOPS descriptors in QSRR prediction of immobilized artificial membrane retention of basic and neutral drugs
References:
1) S. Ong, H. Liu, X. Qiu, C. Pidgeon, J. Chromatogr. A 728 (1996) 113–128. 2) T. Salminen, A. Pulli, J. Taskinen, J. Pharm. Biomed. Anal. 15 (1997) 469–477. 3) A. Reichel, D.J. Begley, Pharm. Res. 15 (1998) 1270–1274. 4) A. Taillardat-Bertschinger, F. Barbato, M.T. Quercia, P.-A. Carrupt, M. Reist, M.I. La Rotonda, B. Testa, Helv. Chim. Act. 85 (2002) 519. 5) A. Taillardat-Bertschinger, C.A.M. Martinet, P.-A. Carrupt, M. Reist, G. Caron, R. Fruttero, B. Testa, Pharm. Res. 19 (2002) 729. 6) MH. Abraham, A. Ibrahim, AM. Zissimos , J. Chromatogr. A 1037 (2004) 29-47. 7) M. Vitha, PW. Carr, J. Chromatogr. A 1126 (2006) 143-194.
124 POSTER T X of for for the the the of 125 than of on variable loadings assessed than matrix thumb vector (activity), terms spite variables The (MLR) the or of ough In larger in e. err thr datasets rule latent variable ession a and ends times tr the variables. egr As r literatur complicated of (QSAR) five variables the e matrix the of linear or in mor dependent most studies. err oposed as a new simple variable individual or of numbers at equations: e the the explaining number elationships ar e The multiple r QSAR eported in r of ar terms vector the f e for and that ones. the following those and ease e-Activity Relationships Data e-Activity Relationships mor PLS E
e-activity used loadings variables models. , Hamedan, Iran two . of decr with y(n×1) of optimal variables the the to cases, ed in is latent of of esults structur the r form e these commonly ar espectively the (PLS) r the , compar e oblem , In y in es ar number pr (descriptors), descriptors 417. and significance the this 87-985941-0-9. data. quantitative squar estingly X the (MLR) to descriptors the esented of to the ISBN statistically two (1979) with Inter pr least , Bu-Ali Sina University variables e of 112 ession about 1996, equal wer (activity). often selected 1. esults. solutions egr loadings partial r is r Acta -fitting the the Employing the performed
exactly models [2-4] (2004) of e Chim. Masoumeh Hasani*, Masoud Shariati-Rad Masoumeh Hasani*, over linear on be eliable edictand independent r 44 211. pr information data. of Copenhagen, wer One esponding ariable Selection Method Based on the Partial Least Based on the Method ariable Selection Anal. Sci. showing and must the event derived based ection-sum of PLS latent loadings (DOSC-SPLSLL) has been pr ection-sum of PLS latent loadings (DOSC-SPLSLL) [1]. corr have pr steps.PLS old, (1988) to is Faculty of Chemistry multiple matrix 2 W the models . to obust the as r H. Publishing, Comput. q the identical, model and elated auto-scaled e modeling Inf. loadings method such gave eduction Thor and r the A Simple V A Simple the corr eported P 1, wer r e in esents PLS The Kowalski, Chemometr . ar es, Chem. om method J. vol. the compounds epr J. fr r method, B.R. es Loadings: Application to Quantitative Structur es Loadings: Application method used of variable scor the methods that e the of of ar the method. MLR and f , of descriptors E + espectively ect orthogonal signal corr Hawkins, X(n×m) Gerlach, Squar + r ences: . extracted , e Hoskuldsson, Hoskuldsson, y Tq modeling matrix e variables TP D.M. A. A. R.W = = y Multivariate applying number necessary selection X
wher the and the The dir selection wer validation simplicity selected PLS Refer 1) 2) 3) 4) POSTER
Investigation of Optimum Extraction Conditions for Determination of Quercetin in Sea Parsnip (Echinophora Spinosa L.) by Using Experimental Design and HPLC.
Mohammadreza Hadjmohammadi, Vahid Sharifi Department of Chemistry, University of Mazandaran, Babolsar, Iran.
Flavonoids are natural products widely distributed in the plant kingdom [1] and are well known due to their antioxidant [2] and anticancer [3] properties. Thus, the extraction of these phytochemical compounds is essential if they are to be of prophylactic or therapeutic value in human health. The traditional one-factor-at-a-time approach to process extraction optimization is time consuming and the interactions among various factors may be ignored [4]. In this project, optimum conditions for extraction of quercetin from Sea parsnip (Echinophora Spinosa L.) which is one of the vegetables in Iranian diet were determined using experimental design and HPLC method. Leaves and stems of Sea parsnip were collected from western Iran, (Sanandaj, Kurdistan province). Central composite design was used to investigate effects of five experimental factors including: volume of extraction solvent, percentage of methanol in aqueous extraction solvent, concentration of HCl, extraction time, and temperature. Method of stepwise multiple linear regression (MLR) was employed to select the most important factors and to calculate the coefficients relating these factors to extraction recovery of quercetin. Grid search method was used to find the optimum conditions for extraction of this compound. Optimum conditions were 25 mL of 70% aqueous methanol containing 2.0 M HCl refluxed for 3 hours at 90°C. The extraction at optimum conditions for quercetin show good repeatability with relative standard deviations of
%3.6 and recoveries about 96%. HPLC system consisted of a Spherisorb C18 (250×4.6 mm, 5 mm) column and UV detector set at 370 nm. The appropriate mobile phase for separation and determination was mixture of acetonitrile, 0.025 M phosphate buffer (25/75 v/v) with pH= 2.4 and flow rate of 1 mlmin-1. Limit of detection (LOD), Linear dynamic range, Intra-day and Inter-day R.S.D for quercetin was 0.19, 0.19- 48.0, mgml-1 (R2 0.99), 3.0% and 4.0% respectively.
References:
1) B. Heimhuber, R. Galensa, K. Herrmann, J. Chromatogr., 1988, 439: 481-483. 2) Cao, G. H., Sofic, E. and Prior, R. L., Free Radic. Biol. Med., 1997, 22:749-760. 3) Ren, W., Qiao, Z., Wang, H., Zhu, L. and Zhang, L. Med. Res. Rev., 2003, 23:519-534 4) M. Wettasinghe, and F. Shahidi, J. Agric. Food Chem., 1999, 47 , 1801-1812.
126 POSTER ] e is α of oss [3]. and 5- 127 now then cr wer most AM1 draw 0.60), is e to [1, cytosolic xanthine the wer inhibitor a eatment of model ethanol be method tr is es used to enzyme pteridin-4[3H]- It for (10.97%, MLR was e oxidase This [2]. of semi-empirical structur Leave-One-Out oxypyrazolo selection or inhibition wer a Inc.) [1]. thought by exogenous err is , Ilam, Iran indicated substituted it ago dihydr out e step, humans Xanthine and cube, of model
ar variable 50 to optimized humans, and as oxypyrazolo oxypyrazolo years IC absolute In Hyper MLR carried of The 70 tissues. purines 7, used oxidase of models. mean was substituted bacteria or diseases, 0.01. development was nearly es err of and om ediction For hypoxic fr (version . pr was esulted xanthine of r metabolism. e model serum the endogenous structur the algorithm rat the absolute for of of spectrum abarak inhibit validation species, purine in In softwar gradient espectively e of in r oss that wide and mean cr Genetic a studied molecular eoxygenation QSAR. , Reza T r of squar 3. sets, of ago oxidation range , Faculty of science, Ilam University , Faculty of science, and in or the was involved performance err HyperChem the of . mean during wide ersion the V medications century a for gout. e method oot (QSAR) a r validation in elative wo Novel Classes of Xanthine Oxidase Inhibitors wo Novel Classes validation oxidase 195–217 r 58:87–114. 9: pathogenesis the over damage espectively several oss and and evaluate r derivatives. softwar Rev cr the mean optimization Shahin Salimpour to cell including milk until Chem modeling esponsible and in of r sets, xanthine 25:750–755. elationship in r as The tissues and or of Med ed rans acid, err T Pharmacol DRAGON ent calibration pyrimidine used Curr ] employed uric Soc derivatives, conditions by algorithm fer a - validation implicated radicals e-activity for e activity was of dif discover 5 (2006) was elative molecules. r edominantly ee (2002) e C and fr the F pr [1, been first Biochem medical the 0.30) (MLR) is many structur mean Szabo of descriptors was in oduction ocedur Roleira since e A, that oxygen pr pr (1997) inhibits E, elated r ession Polack–Rabier of has calibration (XO) ] Pyrimidine Derivatives, T ] Pyrimidine Derivatives, 1497 (7.25%, CM the esent α QSAR Study of Substituted Pteridin-4[3H]-One and Dihydr Pteridin-4[3H]-One of Substituted QSAR Study egr pteridin-4[3H]-one that oxypyrazolo ce r ozhkin and the otein for pr Chemometrics Lab, Department of chemistry Chemometrics Lab, structur nandes quantitative derivatives, Harris be sour Fer (LOO-CV) Nivor , oxidase oxidase 0.22), , , V dihydr educes linear 0.58) F using P [1, 5 - r to calculate work, ences: to and substance chemical substituted this Massey Borges Pacher Xanthine known metalloflavopr Xanthine important any oxidase hyperuricemia In one the method used multiple validation For (4.93%, pyrimidine (8.60%, Refer 1) 2) 3) POSTER
Application of Orthogonal Array Design for the Optimization of Sample Preparation for Determination of Chromium, Copper, Lead, Iron, Manganese, Molybdenum, Nickel and Zinc in Human Hair by Flame and Electrothermal Atomic Absorption Spectrometry
Fariba Tadayon1, Mohammad Saber Tehrani1, Mahmod. R. Sohrabi2, Shiva Motahar2 1- Department of Chemistry, Science and Research Branch, Islamic Azad University, Tehran,Iran 2- Department of chemistry, North Tehran Branch, Islamic Azad University, Tehran, Iran
Metal pollution and its health effects present a challenge currently facing the developing countries. Metal poisoning is usually difficult and expensive to assess or screen in these countries because of limited resources, which means that policies, guidelines, regulations and institutional managements are limited. So, in this regard the analysis of human hair is useful in monitoring the levels of certain trace elements in the body. Hair samples can be collected easily and painlessly, and they require no special care when stored. Unlike blood, hair gives information about the intracellular accumulations of trace elements. Sample preparation is the critical step of any analytical protocol, and involves steps from simple dilution to partial or total dissolution. In this study, orthogonal array design (OAD) was applied for the first time to optimize pre-treatment and determination of lead, chromium, copper, iron, manganese, molybdenum, nickel and zinc in human hair by flame and electrothermal atomic absorption spectrometry. The risk of sample contamination during the sampling and digestion was minimized. Prior to analysis, hair samples were washed with solution 1% (w/v) sodium diethyldithiocarbamate. Four relevant factors, i.e. volume of nitric acid and hydrogen peroxide, digestion time and temperature were selected and the effects of each factor were studied at three levels on the digestion efficiency of human hair samples, and optimized for determination of heavy metals. The statistical analysis revealed that the most important factors contributing to the digestion efficiency are temperature and volume of nitric acid. The optimized method has been employed to digest hair samples of residents of Iran. Also, the average composition of hair from Tehran (heavy industry dominates) was compared with the average composition of hair from the population living in a non-industrialized area of Iran (Takab province).
References:
1) Frisch M., Schwartz B.S., Environ. Health Perspect., 110 (2002) 433 – 436. 2) Sreenivasa Rao K., Balaji T., Prasada Rao T., Spectrochim. Acta., 57 (2002) 1333 – 1338. 3) Pereira R., Ribeir R., F. Goncalves, Sci. Total Environ., 327 (2004) 81 – 92. 4) Taguchi G., Elsayed E.A., Hsiang T., Quality Engineering in Production Systems, McGraw-Hill, New York, 1989. 5) Madaeni S.S., Koocheki S., Chem. Eng. J., 119 (2006) 37 – 44. 128 POSTER ) ,
e of -1 Cr eat L ent cial not and well 129 g wer trace gr is fer m owing critical ement Co, of samples hair of gr dif and trueness. analytical of 1.0 for method is , commer is esults to of of hair r non-invasive hair a measur samples e a ehran, Iran (0.5 human dose , T in the with hair along common ough Zn elements of Estimated analysis, nal a exposur thr determination The ehran, Iran to uncertainty is assessment uncertainty . solutions and (1.098±0.007) inter , T bias. toxic The the ehran,Iran 4 changes the ometry Prior of Mo, calibration to water , T human , in ement statement collected ometry Cr a is AAS constant calibration it months. estimate Co, ET dynamic assess o spectr as measur T of to the described. . two deionized to the biomarkers e individuals e. of calculated , A. Ghorbani without ar 3 157 e of of - (0.973±0.012)and and ar used, weeks used e AAS 151 is tracing ocedur calculate uncertainty ) ET , Saveh Branch, Saveh, Iran , Saveh Branch, Saveh, bias absorption HCl, om pr ehrani advantages ement to fr -T evaluation complete 2008 determination widely ( exposur 6 using 711-716. . , be the 0.1M the 19(2005)195–201. , addition, several atomic measur technique , , Mo, and Zn Determination Zn Determination , Mo, and expanded to 176 constant method In been analytical ranging (1.036±0.110), for ─ has Review ed and
the , M. Saber the and 2 171 assessing
Biology (DDTC), 388(2007) hair have used However ouden Y othermal and calibration and Science
determination was elements. consider d time-scale Chem, and calculate selection calibrate of electr 440(2001) o on samples AAS not to T ement oportional , Islamic Azad University specimen, (0.904±0.017), e othermal Atomic Absorption Spectr othermal Atomic . bias Medicine 2 ET by is Pr Bioanal standar O Acta, in laboratories. elements 2 elements hair used opriate H urine Measur the Anal esult Zn work, r ., exposur Chim. S appr , Science and Research Branch, Islamic Azad University , Science and Research these and sample , and M., for testing , 3 Elements , N. Mashkouri Najafi oportional ) trueness. of 1 concentrations R. -1 essential the diethyldithiocarbamate esent pr Anal. L o hair pr race hosain g
of (1.019±0.026), HNO T blood average m advantages, application .X.., analytical F of the is: of to Thus, higher aqif chemical ement Uncertainty of Co, Cr of Co, ement Uncertainty J. adayon e In W sodium
into An this assessing ed 10.0 human status Rius, . T N., hair it. uncertainty many F In and calculating J., in many 5.0, ., (w/v) health. mixtur Cespon-Romer ( contains M for with opose in . a Riu, insight , their compar used. Measur 4- Department of chemistry pr and human 1% in R., and an to and in Human Hair by Electr in Human Hair ; d C. in A.,Violante , expanded was nutritional we 2- The University of Shahid Beheshti School of Science Dept. of Chemistry Shahid Beheshti School of Science Dept. 2- The University of human d When M. applied implemented. Rabbani with Mo the Due ., Boque, the for technique associated espectively ovides A 3- Department of Chemistry r of , and A., manner pr Alimonti concentrations ecovery afterwar r Zn standar design Cr G. obtained, ences: washed oto, and e Co, ebra-Biurrun 1- Ph D. Student of Chemistry Department, Science and Research Branch, Islamic Azad University Chemistry Department, Science and 1- Ph D. Student of ection Forte Y Mar Ghorbani outine esult Monitoring importance importance. contaminants. sampling dir element established systematically uncertainty evaluation wer digested r for r Nested average Mo Refer 1) 2) 3) 4) POSTER
Statistical Process Control of Edible Salt Production to Improve Salt Quality at National Standard Level
Gholamreza Vatankhah*1, Nahid Tavakkoli2, efat Asghari2 1- Faculty of Conservation, Art University of Isfahan 2- Chemistry Department, Isfahan Payame Noor University
Statistical process control (SPC) is a collection of powerful problem solving techniques, which is useful in achieving process stability as well as improving process capability through the reduction of variability and can be used in any process. SPC has been widely employed in many industries, but relatively little has been written on the successful application of SPC in the food industry. Among the many SPC methods, control charts have been found to be the most popular SPC tool in food industry [1,2]. In this study some of characteristics of edible salt in a refined and crystalline edible salt factory were investigated. The production variables studied were included Calcium, Magnesium and Sulfate impurity level and purity of Sodium Chloride. Characterization of edible salt was done according to National Iranian Standard. Minitab release 15.1 was used to construct the graphical outputs as well as for statistical calculations in this study. Xbar-R control charts have been constructed on the data obtained from this manufacturing line to discover and correct assignable causes, so that the process capability can be determined [3].Xbar-R control charts indicated that some observed variables were not in statistical control and capability results show that they were out of tolerance limits and production process was instable. Through processing data and follow up studies some assignable causes for faulty product were discovered and all observed variables except Magnesium were converted as capable and process was improved. All these activities show that in small sized companies, statistical quality control could be a useful component of production line provided that sufficient finance and qualified personal are utilized.
References:
1) Srikaeo, K., Furst, J.E., Ashton, J., "Characterization of wheat-based biscuit cooking process by statistical process control techniques" Food Control 16 (2005) 309- 317. 2) Ben, M., Jiju, A., "Statistical process control: an essential ingredient for improving service and manufacturing quality" Managing Service Quality 10 (2000) 233-238. 3) Motorcu AR., Gullu A., " Statistical process control in machining, a case study for machine tool capability" Materials and Design 27 (2006) 364- 372.
130 POSTER its op the the the the the the The 131 dr , to to for napht- solvent om organic op fr study ding eign single or the (HF-USAME) optimize samples. for this determine to accor disperser In to (1,2-DCB), honey the practices method, used matter headspace concentrations der or oextraction of addition, this was in In method micr In obenzene developed. unknown range out inorganic be of beekeeping new design time. conventional wide and om this a fr ehran, Iran carried should first with analysis compounds. , T over ed using emulsification arise the was the e, organic these design. Box-Benkhen for can for 47. efor o-xylene),1,2-dichlor to om a compar fr assisted e validated eening Ther and (2002) ee scr then wer applied contaminations fr esented 110 pr for technique. bath. be attributed and Box-Benkhen been is esults these technique r ultrasound contamination
been must design have The The Commun., ethylbenzene fiber design, - Ultrasound Assisted Emulsification - Ultrasound Assisted . HS-SDME arbiat Modares University ultrasonic olatile Organic Compound in Honey Compound olatile Organic methods, have monitor (GC-FID) Eur of , T ces. HS-SDME oduct J. and to pr f. fects hollow methods both sour obtained. and Of toluene, ef esults oposed new method for the determination of the target compounds in honey oposed new method for the determination r fiber ent the was detection using These 2001, fer natural , with Placket-Burman methods Placket-Burman a method dif hollow A a fecting (benzene, as honey om undesirable amini, Shahram Seidi, Abolfazl saleh, Mahnaz Ghambarian amini, Shahram Seidi, af new constant by fr eement in December ionization 1. ent analytical conditions. BTEX agr 20 honey fer HF-USAME, of of technique. oposed dif (2006) Department of Chemistry esidues , pr adollah Y good r conditions Y 37 distribution sensitive emulsificated The had optimized Determination of Some V of Some Determination eover contaminated Samples Using Hollow Fiber Samples Using high HS-SDME, egulations, , was be r Thus, 2001/110/EC Mor (HS-SDME) esults under obenzene and r determination Apidologie, can [1]. [2]. omatography-flame , its honey nitr ective experimental conditions. oextraction (HF-USAEME) Comparative With Conventional Headspace Single Dr Headspace Single Comparative With Conventional oextraction (HF-USAEME) Union solvent chr Dir oextractio With the Aid of Response Surface Methodology and Experimental Design Methodology and Experimental the Aid of Response Surface oextractio With ds: and and analytes oducts ences: gas Micr onment eliminated pr oextraction Bogdanov opean Micr S. Council esults shown an excellent applicability of the pr esults shown an excellent applicability of Bee envir Eur composition simultaneous halene with extraction was micr significant significant target r samples Keywor Refer 1) 2) POSTER
Classification of Iranian Bottled Waters as Indicated by Manufacturer’s Labellings
K. Yekdeli Kermanshahi, R. Tabaraki* Chemometrics Lab, Department of Chemistry, Faculty of Science, Ilam University, Ilam, Iran
Water is the most important substance in our daily lives and contains minerals which play an important role in our nutrition. In this study, the chemical composition of Iranian bottled water brands were investigated by correlation analysis, principal component analysis and hierarchical cluster analysis. For this purpose, the chemical composition reported on the label of 73 Iranian bottled waters was used as data set. It was found out that only 26 brands had eight important parameters such as calcium, magnesium, potassium, sodium, chloride, sulfate, bicarbonate and fluoride and 20 brands had acceptable charge balance error. Results
showed that Iranian bottled waters can be divided into 11 classes. Most of them were Ca-Mg-HCO3 type.
References:
1) Garzon, P., & Eisenberg, M. J. (1998). Variation in the mineral content of commercially available bottled waters: Implications for health and disease. The American Journal of Medicine, 105, 125-130. 2) Brereton R. G. (2003), Chemometrics, Data analysis for the laboratory and chemical plant. Chichester: John Wiley. 3) Eisenberg, M. J. (1992). Magnesium deficiency and sudden death. American Heart Journal, 124, 544-549.
132 POSTER e is to B6 UV es", 133 they with min, exact native points B2, squar use 13 an between methods mixtur applied in B1, e alter Riboflavine data methods chemometric simultaneous least that ), overlap, ablets of as 1 ehran , Iran. gradient omatographic by (B the shows mixtur chr fitting these Partial for linear methanol tablets of oaches for spectral Spectra The a successfully chemometric of e pharmaceutical ochloride appr ar methods 45% ee of calibration using these models esults. pharmaceuticals to (pH=2.5). r hydr -E-Rey Branch, T 1 in degr of the B-complex omatography min component. mM ehran, Iran. of analysis performance chr ajali methods study feine high ml/ ol 50 a the caf 1 changing Thiamine each chemometric two liquid compute chemometrics of of for of contr and mathematical to show itamins in B-Complex T itamins in B-Complex ecovery min, The and analytical r performances These and rate 3 421-430(2006) ofen data , Ali Akbar T used phosphate the 2 pp quality The for models e optimal ibupr flow in ar by steps. HPLC tablets. a 2.5 oscopy Anal. the analytical of at ogene determination and components as ophotometry pH values). separate spectr The applied the fer indicated dihydr spectr ater Soluble V be paracetamol, separation ranges elution buf Biomedical B-complex min. , Islamic Azad University at Shahr , Islamic Azad University using as of selected no can e in of comparison ten derivative with ) ar 6 so simultaneous ficient Although Pharma to (B for ef , Maryam Shekarchi* concentration 1 The potassium for of C18 alidation of Chemometrics-Assisted of Chemometrics-Assisted alidation . (PCR) natives equiring phosphate rapid the excipients in 5 r nal
and M determination applied alter om other and Jour Ace ecoveries. nm 14-21(2009) r fr esented (2008) ession ochloride within 1 0.05 pp chemometrics-assisted pr rapidly; egr equilibration e and r each of of , Faculty of chemistry hydr ence phase simple ar esponses and r with e ors techniques 491-499 with ar ophotometric with competent systems, err pp interval as interfer min A ed eversed validation 3 r spectr methods concentration pharmaceuticals", starting Fereshteh Zandkarimi an component no pyridoxine a in part calibration at and methods ediction Development and V Development laboratory on pr compar eported wavelengths' and with Acta r simultaneously nm several 2- Food and Drug Laboratory Research Centre, Ministry of Health, T Research Centre, Ministry of Health, 2- Food and Drug Laboratory e ) e 3 out of ar at principal ar (B methanol, oposed methanol elative - "Simultaneous chemometric r intelligent pr tablets ded and ochimica six-componenet fer "Application "Multivariate by carried M., 200-400 ophotometry for Determination of W ophotometry for of and 80% two The ecor , buf method is A., r determined (PLS) M., spectr to e tools. ar combination study ophotometry ences: been Nicotinamide HPLC Spectr B3 ession eement. DeLuca ), El-Gindy this Khoshayand 2 1- Department of Analytical chemistry 1- Department of Analytical egr In (B spectr and wavelengths r (linear characterized have pharmaceutical separation phosphate changing and agr analysis Refer 1) chemometrics 2) methods", 3) determination POSTER
A Comparison Between LS-SVM and BP-ANN for Simultaneous Spectrophotometric Determination of Some Ingredients in Detergent Powder
Mohammadreza Khanmohammadi, Mohammadhossein Ahmadi Azghandi, Nafiseh Khoddami, Amir Bagheri Garmarudi Chemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
The novel spectrometric method utilizing least-squares support vector machine introduced for simultaneous determination of STTP, SS, and SC in powder detergent as a complex system with serious overlapping absorption peaks. Also, the Artificial Neural Network (ANN) [1-4] modeling was used as an alternative method for the multivariate calibration of the spectrophotometric data. With respect to statistical parameters of methods LS-SVM was selected as a capable model for predication of these ingredients in powder detergent. The proposed method is simple, with no tedious pretreatment step for simultaneous determination of mentioned components in powder detergent. A FTIR spectrometer (Magna 550, Nicolet, Madison,WI, USA) equipped with a DTGS detector, an Ever-Glo source and a CsI beam splitter was applied. The ATR- FTIR spectra were obtained by a 45° ZnSe cell. The data obtained from WINFIRST software were exported in ASCII format.
Keywords: biosensor, HPVp, pencil graphite electrode, methylene blue, modeling, DNA hybridization.
References:
1) Back-propagation Artificial Neural Network and Attenuated Total Reflectance - Fourier Transform Infrared Spectroscopy for diagnosis of Basal Cell Carcinoma by Blood Sample Analysis, M. R Khanmohammadi, A. Bagheri, K. Ghasemi, A. H. Emami, J. Chemo, 2009 2) J. Ghasemi and M. Vosough, Spectrosc. Lett., 2002, 35, 153. 3) A. Niazi, A. Yazdanipour, J. Ghasemi, and A. Abbasi, J. Chin. Chem. Soc., 2006, 53, 503. 4) L. Gamiz-Gracia, A. Velasco-Arjona, and M. D. L. de Castro, Analyst, 1999, 124, 801. 5) E. Dinc, G. Kokdil, and E. Onur, J. Pharm. Biomed. Anal., 2000, 22, 915. 6) P. Vinas, N. Campillo, I. L. Garcia, and M. H. Cordoba, Food Chem., 1992, 45, 349. 7) J. J. B. Nevado, C. G. Cabanillas, and A. M. C. Salcedo, Talanta, 1997, 41, 789.
134 POSTER e as an , was 135 oach range fective acquir such obtained for appr to 2 model detector 1992. data ANN applied capability grinded DTGS The a and England, and cell. chemometric model with , Qazvin, Iran samples the ZnSe fuse ANN Limited, technique first, 45° used application a 39. equipped we its by utilizing Horwood oscopy USA) method, (2006) 45. 153. Ellis fects work, af 326 oscopy spectr 2003 obtained (2000) (1998) this oposed e 9 -NIR Practice, nal, In pr spectr 419 which wer and jour Madison,WI, the ed (NIR) spectra has been used to develop an ef ed (NIR) spectra has DRIFT -NIR oscopy Acta In [1-4]. by Pharmaceutics Theory of spectra DRIFT Spectr size size. Nicolet, ded parameter Chemistry e, nal Chimica ed FTIR of ecor Jour efor r 550, Infrar Industries TR- e particle A Ther Analytica Society wer Near other nanoparticle The national (Magna considerable nal
of Royal [5,6]. and 1326–1330. Inter is Jour format. Folestad, determine fat, samples 647–651 S. range applied. to ometer of Mof (2003) Martin, samples ASCII C. . Haque, the der 244. was (2008) in 2125. E. 128 for or spectr G.P 77 Pharmaceutics spectra in edict Johansson, in oscopy for Estimation the Range of Particle Size of Nano-TiO the Range of Particle Size oscopy for Estimation nanoparticles (2004)
FTIR pr 2 Anthony (2000) Zeng, splitter Analyst model The A
alanta to iO T Spillman, M.O. exported 141 ones T and 125 e fat, o, X.-M. Analysis of beam C.K. Jee [7-9]. Then,
wer Mof (ANN) 2 D. Size e Application of Artificial Neural Network and Near IR Dif and Near IR Neural Network of Artificial Application CsI size Review Peguer iO a T echnology A.C. Steele, Danielsson, T Mohammadreza Khanmohammadi, Nafiseh Khoddami, Amir Bagheri Garmarudi Nafiseh Khoddami, Mohammadreza Khanmohammadi, traditional Roger classification Anna Jee, J.L. Particle MacRitchie, and a to L.G. softwar performance The influence of particle size on near infrar performance The influence network powders. Analyst nano particle ce Powder R.D. H.B. O’Neil, of size Chemistry Department, Faculty of Science, Imam Khomeini International University Faculty of Science, Imam Khomeini Chemistry Department, eton, Blanco, J. Reflectance Spectr sour ntsson, neural er develop Pasikatan, ew ashington, Br methods ences: O’Neil, celo WINFIRST to Otsuka, Beer Marriot, W range -Glo particle Mar Andr O. C. M. C. M.C. A.J. R.G. om The photocatalytic novel artificial homogeneous used of Ever fr Refer 1) 2) 3) 4) 5) 6) 7) 8) 9) POSTER
Simulation of Precipitation Titration for Some Cations Using pH Glass Electrode
A. Nezhadali*; B. Ahmadi Department of Chemistry, Payame Noor University (PNU), Mashhad, Iran
Simulation of precipitation titration for bromide and chloride ions using nitrate solution of different cations with pH glass electrode has been done in this research in such a way. Introducing appropriate reagent under the title of mediator adds to the medium to make an intense change in pH on the end point. So, pH electrode arrangement will be indirectly sensitive to the changes of the cation concentration. The added reagent to the medium should be a weak acid so that the conjugated base of it can approach the end point and make a slightly soluble precipitate with cation titrant. Considering the hydrolysis effects, all chemical equations happened during titration in any moment are written separately for one valence and two valence cations and the solution of equations is done by MATLAB software. This program is able to draw three curves: pH based on titrant volume, dpH
based on reagent concentration and dpH/dv based on titrant volume. In this research, four substances including NaHCO3,
NaHSO3, NaHC2O4 and NaHCrO4 after improving the quantities of them have been used as reagents for determining the end point
of titration. The result indicate that from the amount the four selected reagent , NaHSO3 showed more sensitivity at the function end point of titration and pH changes are more severe at the end point . There are many factors [1-3] that are effective on the simulation of titration including weak acid dissociation constant mediator, the solubility of product for precipitate titrant and titrate (Ksp) and the solubility of product for titrant and the reagent (Kś p). If the reagent acid is stronger then slope of the curve will be better. Studies revealed that if the acid is too weak, we won’t have acid separation accordingly the sensitivity of the curve is also reduced. If the acid is stronger than usual the increase in the anion of acid will cause the formation of more precipitate than the reagent which will result in an independent equilibrium of existing ions to hydrogen ions. The additional Ksp to Kś p will also result in negative error as the intended precipitate separate sooner than the reagent precipitate the result will be the increase in the formation of reagent sediment and the premature end point. Very small proportion of Ksp to Kś p will also result in positive error. Finally it is expected that the method can be developed to complex titrations.
References:
1) T. Kee Hong , Hyang – Zoon Cza and Myung – Hoon Kim , 199th ACS National , Meeting , Boston , April 22-27 , (1990). 2) N. Horea , J. Lorentz, H. Teodor, C. Claudia, C. Gabriela, Some Application of Statistics in Analytical Chemistry, Reviews in Analytical Chemistry, Freud Publishing House, P. 409-456, XVIII (1999). 3) C. C. Rundle, A Beginners Guide to Ion Selective Electrode Measurement, NewYork (2000). 136 POSTER d is o- of on the not ions 137 of in NAS analyte standar does metal on of eparation analyte pr parameters ophenol and p- some of (HPSAM) the depends imagination determination In fective for only advantages ef abriz, Iran determine of method the that used to determination geometrical abriz, T d Addition Method overlapped. tool was signal the addition the d combines ophenol by Bismuth ophenol for to ongly optimization str powerful NASSAM ochemical behavior of o-nitr e analytical ding the a standar of applied as wer for and NASSAM , University of T be Accor part . a ode The used 789. H-point can peaks is [3]. variety developed was electr 2009, NAS against 74, was method lead their space eduction ophenol and 4-Nitr ophenol r [1-2]. Soc. Revised. The and ents -Zeynali*, Parvaneh Najafi -Zeynali*, Parvaneh ents pencil their edictions methodology of Chem. ,2009, pr , Faculty of Chemistry (NASSAM) A interfer concept. interfer the Serb. ent spectra.In this work, the electr Part J. modified design of the , because method to ode With Net Analyte Signal Standar ode With Net Analyte signal Acta concentrations e, known Karim Asadpour of bismuth ents accuracy ochim. vector mixtur addition analyte ah,asebpour T d a the The experimental esence net M. Spectr in data 1620. interfer on pr of the 69, of ents. the the standar Majidi, ee in of ode Bastami, fr those ophenol studied 1997, s M.R. M. signal interfer esponse. it’ r electr with p-nitr been portion Chem. ode and Department of Analytical Chemistry Department of Analytical -Zeynali, -Zeynali, analyte has and known compounds Simultaneous Determination of 2-Nitr Determination Simultaneous Anal. , of electr method modified net Modified Pencil Lead Electr Modified Pencil ences: Lorber Asadpour Asadpour organic ophenol ophenol K. K. A. esence Recently and concentration orthogonalized addition pr nitr depend on the shape of the analyte and interfer depend on the shape of the analyte and nitr bismuth modified Refer 1) 2) 4) POSTER
Simultaneous Polarographic Determination of Antazoline and Naphazoline by Differential Pulse Polarograhy Method and Support Vector Regression
Karim Asadpour-Zeynali*, Payam Soheyli-Azaz Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
A differential pulse polarography (DPP) for the simultaneous determination of antazoline and naphazoline was proposed. It was found that under optimum experimental conditions (pH=6, scan rate=60mV/s, pulse amplitude=0.05V) Antazoline and Naphazoline had well defined polarographic reduction waves with peak potentials at -1.45 and -1.55 V, respectively. In mixture of two compounds serious overlapping polarographic peaks was observed. In this study, support vector regression (SVR) was applied to resolve the overlapped polarograms. The use of support vector regression (SVR), a recently introduced alternative regression technique, for quantitative polarographic analysis has increased over the past few years especially due to it’s high generalization performance and it’s ability to model non-linear relationships as well. In this work, a comparison was made between the performance of SVR and partial least square (PLS) on data set. The experimental calibration matrix was designed with 42 mixtures of these compounds. The concentrations were varied between calibration graphs of these compounds. The results demonstrated that SVR is a good well-performing alternative for the analysis and modeling of DPP data than the commonly applied PLS technique. The proposed method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.
References:
1) A.F. Marchesini, M.R. Williner, V.E. Mantovani, J.C. Robles,H.C. Goicoechea, J. Pharm. Biom. Anal. 2003, 31, 39. 2) C. Goicoechea, C. Olivieri, Anal. Chim. Acta. 2002, 453, 289. 3) B. Hemmateenejad , R. Ghavami , R. Miri , M. Shamsipur, Talanta 2006 68, 1222. 4) M. Meloun , T. Syrovy , A. Vrana. Talanta 2004, 62, 511.
138 POSTER of but was 139 with have used es ound MCR- MCR- image (MCR- ed ely in es ofiles, estimated peaks. that rar mixtur pr backgr us is limited. used squar compar constraints is and e some gives MCR unfortunately ographic ent least fer wer but this fer understood buf that e polar dif abriz, Iran commonly methods purpose set, as concentration nating and of Results for this that ocesses, these fect pr abriz, T For chemistry ef om medium esults methods r fr overlapping calibration analysis. e The data. methods of esolution-alter acidic with r chemical data opriate analytical in es mixtur in matrix calibration curve position. appr the ographic allipour , University of T mixtur in data ographic tools unknown give . analyzed polar ion of chemometrics . 403 polar PCR , optimum 295 was partly , multivariate 809. using multivariate metal om esting or 584 36 powerful and , fr , of by columns sets, e 127, components e ar inter work for 2007
PLSR 2006 totally
the obtaining A -Zeynali*, Javad V the as this 2002, of EF of analysis mixtur Acta . In ediction of Chem of until , Faculty of Chemistry . pr 1087. such methods information ions [5]. well-established Chim one Analyst use . a , Anal 163. is and ograms 384, done . as the e Anal qualitative metal 36, auler , Rev es T . by polar of methods, obtained 2006, R. wer expected ochemical e (MCR) and understanding, Crit Karim Asadpour
2006, as squar the and pur Esteban . calibration Chem. Cruz M - , analysis ove least system Chem. az calculated Multivariate Curve Resolution of Overlapping of Overlapping Curve Resolution Multivariate iterations Duarte sets, calibration ´ı [1-4].Electr Cruz Resolution D impr C. ograms to the Quantitative Analysis of Metals Mixtur Analysis of Metals ograms to the Quantitative - . quantitative Bioanal. was Anal. the other individual A. . az two to partial M ı . for D or J
chemometrics Rev Curve Anal. and der Polar , M. analysis quantitative and or Simao, J. , Crit. with multivariate J. , in the applying ecovering estimation E. r Kessler eactions inputs, containing r . J. Arino by technique Arino, auler for . T of way W Department of Analytical Chemistry Department of Analytical as C C. R. , R. Initial ions Multivariate , quantitative used combined kinetic ed.Despite eliable good r Antunes, Juan, a ect a obtained ences: olyte. ograms Esteban was C. metal Kessler de Alberich, is is . . been possibility dir M. W A. A. M Nowadays analysis, for not ALS) four electr polar consider the ALS those ALS Refer 1) 2) 3) 4) 5) POSTER
Application of Parallel Factor Analysis and Multivariate Curve Resolution-Alternating Least Square for Resolution of Kinetic Data of L-ascorbic Acid Oxidation in Multivitamin Tablets by UV Spectrophotometry
Mohammadreza khanmohammadi. Mohammad Babaei Roochi. Nafise khoddami. Zahra Amani Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
Monitoring the reactions by spectroscopic methods is a powerful tool to obtain a great deal of information about mechanism of reaction. Each spectral band refers to a specific group present in the compounds involved in the reaction. Investigation of the concentration of a particular compound can be followed by the changes in its band during the time. But, in many cases the spectral bands are not selective, and display spectral overlaps. In these cases, multivariate data analysis can overcome most of the problems reported for univariate analysis. Application of PARAFAC and MCR-ALS algorithms for kinetic study of L-ascorbic acid
oxidation by K3Fe(CN)6 and production of dehydro ascorbic acid using UV-Vis spectrophotometric is presented. These techniques have been applied to analysis of evolving system of L-ascorbic acid oxidation.A data analysis approach, based on spectrophotometric resolution was applied to investigate the mechanism of L-ascorbic acid oxidation. Two different oxidative
reagents 1,10-orthophenanthroline iron (III) and K3Fe(CN)6 were examined. Based on reaction times K3Fe(CN)6 was more suitable for this kinetic study. Reaction was completed after about 720 seconds for a 12 ppm solution of L-ascorbic acid.This study proved the capacity of the proposed these two data analysis methods to interpret the evolution of these kinetic reactions, also based on obtained results MCR-ALS is more powerful for following kinetic study. Comparison the results obtained by UV–Vis and chemometics method and reference values in showed the UV-VIS analysis gives relatively high estimates for the prediction of L- ascorbic acid based on kinetic oxidation.
References:
1) W. Okiei, M. Ogunlesi, L. Azeez, V. Obakachi, , G. Nkenchor, Int. J. Electrochem. Sci., 4 (2009) 276 – 287 2) U. Moser, A. Bendich, Vitamin C., in: Handbook of Vitamins Edited by: Machlin Lj, Marcel Dekker, New York; 1990:Ch5 3) M.D. Guillen, N. Cabo, Food Chem. 77 (4) (2002) 503–510. 4) J.M.M.Leitão, J.C.G.Esteves da Silva,Anal.Chim.Acta 559 (2006) 271–280. 5) J.M.D. Cueva, A.V. Rossi, R.J. Poppi, Chemom. Intell. Lab. Syst. 55 (2001) 125–132. 6) A. Espinosa-Mansilla, A.M. de la Peña, H.C. Goicoechea, A.C. Olivieri, Appl. Spectrosc. 58 (2004) 83–90.
140 POSTER of be the and was 141 een- with water (CCD) gr can (n=10) der variables or ecision design 2number waste five Janus design pr is concentration, conditions, variables by and deviations ehran, Iran the 1 d ,T oxide oxide System two-level experiment per a composite ehran, Iran sample optimum experimental between of standar evaluate ors, experiments an the ogen to err osmium(VIII) fects of azd, Iran central of ogen Per a mineral elative hydr r , Mehdi Nekoei d 1993. point 3 Under consisted oss-ef and cr The number . minimize conditions, dam, -1 to that standar (FFD) central the mL is a basically method. een-Hydr azd Branch, Y ng der Amster the in , determination that working , Y or concentration, the in design to In model 0.05 of the een is Elsevier gr this due for Osmium , Science & Research Branch, T , Science & Research . optimization of optimum limit factorial eplicates oach, r but Janus the The
obustness Appr r ed, pH, experiments. , Masoud Reza Shishehbore , Masoud Reza Shishehbore several developed 65-73. the 2 fer detection espectively fractional done. advantage r achieve , A buf -1 the plus consider to e was been (1994) e main mL , Islamic Azad University additional and
used. wer time Chemometrical 298 -1 has ng wer confirmed The A mL echnology Research Institute, Shahid Beheshti University echnology Research was the Acta variables ocess out. , Islamic Azad University ng 400.0 pr 227-241. experiments, Design: method esults. levels variables surfaces Chim. educe r performing r 16 and 430.0 carried of e to (1998) two – , Parviz Shahbazikhah necessary Anal. 1 be at 10.0 der 360 0.4 wher possible esponse need or ona, r optimization Experimental for for strictly must In satisfactory Acta the tested the ophotometric the The the design all in with linear to 3- Department of Chemistry Chim. 0.32% esults, Morgan, r ed spectr system. time. L. Gonzalez-Ar to First, Hasan Bagheri was of without variables S. Anal. D. Application of Experimental Design Methodology to the Optimization the Optimization to Design Methodology of Experimental Application and factorial oxide 1- Department of Chemistry 1.75 eduction kinetic consider r [1-3]. graph determined per a validity Deming, is om ength Gonzalez, Gonzalez, fr evaluated N. ences: used str 2- Department of Nutrition & Food T 2- Department of Nutrition G. G. ogen S. factors quantitative fractional e of Catalytic Kinetic Determination of Osmium by Janus Gr Determination of Osmium of Catalytic Kinetic catalytic 1. A. A. A hydr five wer variables, 1/2 statistical easily randomized. The ionic calibration varied samples Refer 1) 2) 3) POSTER
A New Spectrophotometric Study on the Simultaneous Determination of Benzodiazepines in Plasma employing Multivariate Calibration Methods Combined with Genetic Algorithm on Ordinary and Derivative Spectra
Siavash Riahi*, Kowsar Bagherzadeh, Mohammad Reza Ganjali, Parviz Norouzi Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran
Prediction ability of chemometric methods was investigated by employing them on ordinary and derivative UV spectra to simultaneously determine two highly overlapped Benzodiazepines (Clobazam and Flurazepam) in human plasma samples. Therefore, a numerical simple, accurate and precise method based on spectrophotometric data coupled with multivariate calibration methods, PLS and MLR, combined with GA was developed. A data set of absorption spectra obtained from a calibration set of mixtures containing different amounts of the compounds (according to their linear range which were obtained to be 1.5-17.5 and 2.5-30 For CLO and FLU, respectively) was used to build GA-PLS and GA-MLR models. The models were tested using a dataset constructed from the compounds synthetic solutions. The predictive ability of the designed models was then investigated using a dataset obtained from solutions of pure substance where the percentage recoveries were found to be 101.70 and 101.90 for CLO and 93.70 and 98.90 for FLU via GA-PLS and GA-MLR, respectively. Also the PRESS, RMSD and REP% values were obtained to be 0.4849, 0.2632 and 3.2155 for CLO and 4.3421, 0.7876 and 6.0560 for FLU by GA-PLS and 0.4223, 0.2456 and 2.9985 for CLO and 0.9961, 0.3772 and 2.8058 for FLU using GA-MLR, respectively. The better model (GA-MLR) was also applied to plasma samples. Derivatization ability in resolving spectral overlapping was evaluated when the multivariate methods are adopted for analysis of multicomponent mixtures. So, the chemometric methods were also applied on first-to-second order derivative spectra that resulted in better results in compare with those obtained by employing the methods on ordinary UV spectra, showing relative standard errors less. The proposed methods require no preliminary separation steps and can be used for these drugs analysis in quality control laboratories.
References:
1) Arancibia, J.A., Delfa, G.M., Boschetti, C.E., Escandar, G.M., and Olivieri, A.C. Anal. Chim. Acta, 553: 141-147 (2005). 2) Berzas, J.J., Castaneda, G., and Pinilla, M.J., Talanta, 57: 333-341 (2002). 3) Fisz, J.J. J. Phys. Chem. A, 110: 12977-12985 (2006). 4) Lozano, V.A., Camina, J.M., Boeris, M.S., and Marchevsky, E.J. Talanta, 73: 282-286 (2007).
142 POSTER . F 1 om and 143 then fr the serum partial essants Biomed. to to was and containing orthogonal espectively 0.3524 r performance , and es Pharm. an obtained esults antidepr J. r CLP coupled High solutions C., 0.7451, mixtur d using , Parviz Norouzi and H. ehran, Iran 1 data of ehran, Iran dataset R. tricyclic and drugs. a espectively) IMP oz obtained r set , standar CLP two for the (2007). ehran, T IMP Queir for of ehran, T performed studied and applying binary and (2005). 99.56, ophotometric the was A. calibration GA-PLS. the J. of CLP a between 0.6278 1342-1347 and of spectr alidation by HPLC for om and 42, 139-146 assessed fr Crippa spiking those determination 39, J., , University of T 3-30 om., 100.64 by E. then , Mohammad Reza Ganjali , Mohammad Reza and 0.5600 be 1 comparison Quantification and Anal., employing a concentration Spectr to obtained was (2007). HPLC the (GA). Cesarino and samples (2006). Mass ., 0.0188, P J. Biomed. range found simultaneous eal model (2.5-17.5 samples L. r be e using M.,
nm . M. to the F the wer 342-348 method calculate built Pharm. 2391-2400 data range algorithm serum to J. to for 30, 830, , Faculty of Chemistry the Bianchi Lancas ded A., and B, . S. 200-400 linear oposed obtained of Sci., D., and e ecoveries ecor genetic pr applied r r GA-PLS A. the C. Sep. validated S. their J. wer by the in J. the Ahmed ability , Behrouz Akbari-Adergani also joint omatogr in 1 synthetic N., and in and Chr I. centage was J. values A. Carvalho spectra validate per Rodrigues S. C., spectrum between edictive method C., (CLP)) to C., pr E. the REP% UV compounds e method developed M. Hussein Hiemke The and Papadoyannis eement oz nandes The A., applied wher absorption . and been agr calibration and Fer H. studied S. of Queir K. J., RMSD was esponding Clomipramine Rapid Chemometric Method for Simultaneous Determination Determination for Simultaneous Method Rapid Chemometric has the C., A. M. , Kowsar Bagherzadeh of corr 1,2 Hartter of Imipramine and Clomipramine in Serum and V Clomipramine in Serum and of Imipramine and and substance (2007). espectively construction. Nika PRESS, J., Mohamed r e ., , F satisfactory consisting their Bertucci multivariate . A., method a the V (IMP) . pur , F IMP R., es Koller set 2- Institute of Petroleum Engineering, Faculty of Engineering, University of T Engineering, Faculty of Engineering, 2- Institute of Petroleum . Santos-Neto of model 1- Center of Excellence in Electrochemistry 1- Center of Excellence omatography quantities 955-962 J., W for C., chr ences: for data 44, ent squar evealed analyzing r Siavash Riahi* fer numerical Malfar Samanidou Mohamed Sachse Alves A (Imipramine least array dif used solutions Additionally 2.5022 and liquid test Refer 1) 2) 3) 4) 5) Anal., POSTER
Simultaneous Spectrophotometric Determination of Co(II) and Ni(II) Based on the Complexation Reaction With Phenylfluorone Using Partial Least Squares Regression
Mohammad Alizadeh1, Hamid Daryani2, Morteza Bahram2, Reza E. Sabzi2 1- Department of Chemistry, Faculty of Science, Payam-Nor university, Urmia Center 2- Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran
A simple and sensitive spectrophotometric method for simultaneous determination of nickel and cobalt based on the formation of colored complexes of Ni(II) and Co(II) with 9-phenyl-2,3,7-trihydroxy-6-fluorone (PF) in the presence of Triton X-100, has been developed. Optimum concentrations of PF, Triton X-100 and pH ensuring maximum absorbance were defined. Beer’s law is obeyed 0.1– 1.0 and 0.1– 1.5 mg ml-1 for Ni(II) and Co(II) concentrations, respectively. In order to overcome the interference of Cu(II) and Zn(II) in the determination of Co(II) and Ni(II), quaternary mixtures of Ni(II) and Co(II), Cu(II) and Zn(II) were used as calibration and prediction sets to construct the PLSR [1]. A number of 40 ternary mixtures were selected as calibration set. Their composition was randomly designed for obtaining more information from the calibration procedure. Under these conditions, the calibration models were obtained. The obtained model was validated with a 30 synthetic mixture set containing the considered metal ions in different proportions that were randomly selected. To select the number of factors in the PLS algorithm, a cross-validation method, leaving out one sample at a time, was employed. Both PLS-1 and PLS-2 algorithms [2] were applied and nearly the same results were obtained. The relative standard error (RSE) for the simultaneous determination of 30 test samples of different concentrations of Co(II) and Ni(II) in the presence of Cu(II), Zn(II), was less than 4.0 %. Also the regression coefficients for the predicted concentration of Co(II) and Ni(II) versus added ones were calculated as 0.9947 and 0.9957, respectively. The proposed method was successfully applied to the simultaneous determination of Cu(II), Ni(II) and Co(II) ions in water and in synthetic alloy samples
References:
1) H. Abdollahi, Anal Chim Acta, 442 (2001) 327. 2) Massart D L, Vandeginste B G M, Buydens LMC, Jong S D E, Lewi P J, Smeyers-Verbeke J (1997) Handbook of chemometrics and qualimetrics. Part A Elsevier Science.
144 POSTER es to of on the 145 each on was ignor of position solvents (DLLME) range based ch ely is . the also the optimization experimental experimental levels mer in esear econcentration r and the pr The about extraction elies . r method our oextraction linear various and espectively of and r abriz, Iran of The micr was optimization often 56, aim changing fect ef by and information development graph The abriz, T methodology , Urmia, Iran 3 99%, dispersive samples. separation The any the , Urmia, Iran [1]. a of liquid-liquid [2]. as give that ophotometric Detection one-at-a-time surface performed calibration obtained not experiments biological volume used e possible media The still of the as is dispersive wer and does was , Urmia Center , University of T esponse important it of 130. r acidic , M.A Farajzadeh 2 costs system. in factor water very number and DLLME (2007) is in low conditions the chemistry it race Amount of Nitrite Using race Amount of 39–49 because as diphenylamine, of large 1153 design A optimization nitrite on . Eshghian and . with (2005) of , F enrichment optimum 1 strategy detection. analytical and 291 diphenylamine and in based optimum and of experiments, omatogr is oaniline, composite , Faculty of Chemistry Under ardast of Chr amounts local , Faculty of Science, Urmia University , Faculty of Science, J. economic a p-nitr esence costs Recovery to central . trace pr development and , M.R V ophotometric -1 Pharmaceutics oextraction Followed by Spectr oextraction Followed experiments 1 optimization acid, of the mL the only rising parameters. methodology ultra few using in and nal in spectr ng ficient Langenhove, of as . ef lead This by Jour sulfuric 0.3 H.V rapidly an of of with oaniline best, M. Bahram* ith operating itte, time. not a W W of is its national Application of Experimental Design Methodology in Design Methodology of Experimental Application investigated limits done followed at p-nitr methodology at is B.D. Inter and determination , experimentation levels was with factors. can, 1- Department of Chemistry nitrite ecer r the design concentration five 2- Department of of Science , Payame Noor University 2- Department of Optimization and Determination of T Optimization and V rapid detection Dewulf, of . analyst of at F method and as J. separately nitrite the e, the with urk,
extraction of between T 3- Department of Analytical Chemistry 3- Department of Analytical such -1 most done for of S. of analysis mL (factor) was analytical optimum Dispersive Liquid-Liquid Micr Dispersive Liquid-Liquid experimental ng ences: Kincl, Demeester eaction time r trace the any K. M. -60 2) Nowadays, variable experience of interactions of apply for technique, the parameters and design 2 1) Refer
POSTER
Prediction of Receptor Binding Constant of 6-Methoxy Benzamides, Using ANN and MLR
Mohammad Hossein Fatemi, Fereshteh Dorostkar Faculty of Chemistry, University of Mazandaran, Babolsar, Iran
Schizophrenia is the most common and chronic debilitating mental disorder. The used organic compound in this paper, operate as
the antagonists which block D2 receptor and their antipsychotic potency has good correlation with their capacity to bind to D2 receptor. After the calculation of molecular descriptors, the stepwise multiple linear regression method is used to select descriptors. Then artificial neural network (ANN) and multiple linear regression (MLR) are applied to construct the nonlinear and
linear quantitative structure-activity relationship models. The selected explanatory variables are: GATS5e, CH2RR E-state value, NHR non aromatic E-state value, tot molecular 2-center exchange energy/number of atoms, hydrophobicity of the substituent at
R3 position and resonance effect at R5 position. The standard error for the training, prediction and validation sets using artificial neural network are equal to 0.1134, 0.3225 and 0.2389, respectively. The correlation coefficient for the training, prediction and validation sets are equal to 0.991, 0.945, and 0.968, respectively. Comparison of the results with MLR and ANN indicates that the ANN method has better predictive power than the MLR method.
References:
1) Samuel H. and Barondes M.D. (2006) History of neurotransmitters in psychiatry. The Carlat Psychiatry Report 4: 1-8. 2) Freedman R. (2003) Schizophernia. The new england journal of medicine 349: 1738-1749. 3) Hasegawa K. and Matsuoka S and Arakawa M and Funatsu K (2002) New molecular surface-based 3D-QSAR method using Kohonen neural network and 3-way PLS. Computer and Chemistry 26: 583 – 589.
146 POSTER in of of by on 147 with then solve Nano- carried surface organic to network solubility and ement stands cetin because e the was less der parameter experiments' or Unfortunately oducts. quer eases using . and and easing of in pr concentration measur this 3 esults. , mixtur r ea incr like day design, of studies, incr the ar was fects Then, of by study ef Then ession size barrier w/w), this day surface dermatitis. modeling modeling optimization phase. purpose experimental brain Compounds experimental pharmaceutical for particle all, easing eater conducted. the of therapeutic (2-0.7% of atopic gr with of water nm. the design, patient.In incr , blood to ed was with step and is the the designs First e and to due to ethanol 10-200 each , Ardabil, Iran , Bahar Ebrahim magham , Bahar Ebrahim magham in [2]. eparation 2 compar skin In pr evaporator drug psoriasis considerable softwar as added emulsions experimental in ound and as of systems ar lecithin with the otary consumption was experimental method such r SPSS studied. of such of such nano- size its oblem e by edicted in of pr amount with pr ders step wer oducts fects small ethanol) 241–251. factorial fact pr , Mazandaran University of Medical Sciences, Sari, Iran , Mazandaran University less , PO BOX 163, Gonbad, Iran , PO BOX 163, Gonbad, kind ef , Soheila honary solvent in 2 disor a each of causes membranes very cetin Nanoparticle Emulsion Emulsion cetin Nanoparticle in essions ed (2004) particles emulsification skin concentration minutes) w/w) this natural suitable egr of 357–368. with r organic 280 epar by pr and nano- unwanted most biological particles (60-10 we (2004) linear of Pharm. including , less (0.1% the the eatment J. interphase[1-2].In administrating 58 important emoving time r tr of spontaneous size solution dispersions Int. eparaing and water cetin levels the then ough , Faculty of Pharmacy pr one most water after in with multiple in thr Fessii, quer for body and oil/ , Fereshteh Pourmorad , Fereshteh Pourmorad Biopharm. the ed average 1 elated H. then r sonication , aqueous in 1- Higher Education Center 1- Higher Education oil/water and of using the in drug epar method, 3- Faculty of Science, University of Payam Noor 3- Faculty of Science, and Optimization of Quer Optimization solubility and and Pharm. pr Perrier potentials one molecule J. condition the human's low ween . E. is T cetin w/w) colloidal of dissolved Eur their drug very minutes of with be conditions cetin –Behnken of quer for is optimal determining 15 eparation Using Experimental Design and Multiple Linear Regr Experimental Design and Multiple eparation Using parameters (8-2% to Branc.on, Benita, Lecithin, can kind Pouneh Ebrahimi Box Pr S. Quer for S. a After e Nano-emulsions ar elated solubility bath bioavailability using method, r ethanol instrument. absorption absorption compatibility solubility in (including experimental ences: amilvanan, cetin Experimental Bouchemal, skin T ching oduction: 2- Pharmaceuticl Research Center 2- Pharmaceuticl Research ee S. K. oblems ween Intr having quer emulsions limited tension, enhances pr and Methods: condition out. T phase ultrasonic Conclusion: Zetasizer thr sear Refer 1) 2) POSTER
Comparing Different Subset Selection Methods for Nonlinear Modeling the Acidity Constants of Some Organic Compound in DMSO
Gholamhasan Azimi1, Sara Ebrahimi2, Mohsen Kompany-Zareh2, Yousef Akhlaghi2 1- Department of Chemistry, University of Arak, Arak, 38156-879, Iran. 2- Department of Chemistry Institute for Advanced Studies in Basic Science, Zanjan, Iran
Quantitative structure property relationship (QSPR) has been suggested for the prediction of acidity constant, pKa, of a set of 78 organic components in DMSO [1]. All molecules were drawn into ChemDraw and their structures were pre-optimized using the AM1 method in Chem3D software. A more precise 3D optimization was done by ab initio methods develops for this purpose the Hartree-Fock method in Gaussian03 software and 6-31+G(d) basis set has been used. Using the obtained optimized conformations, a number of quantum- and physico-chemical descriptors were calculated from Gaussian03 and Hyperchem softwares. Also the Dragon software was applying to extract other descriptors such as constitutional, geometrical and topological based on the optimized structures. Genetic algorithm (GA) was used for initial selection of 50 descriptors from the large group of 670 calculated descriptors [2]. The final selection of descriptors using backward elimination method led to 15 descriptors by which all models were constructed. Mallows augmented partial residual plot (APaRP), has been employed as a diagnostic tool to detect nonlinearity [3]. Partial least squares (PLS), radial basis function networks (RBFN) and support vector machines (SVM) were used to build the quantitative models. We described the validation of QSPR models by four training and validation data sets selected based on random division, systematic selection from sorted data and K-means clustering on the factor scores of the original variable matrix along with/without acidity constant values [4]. From these four methods, K-mean clustering on PLS latent variables
2 2 2 produced the best models by all three regression methods. RBFN (r pred = 0.982, q = 0.937 and RSEP=%4.496) and SVM (r pred = 0.977, q2 = 0.929 and RSEP=%4.779) had comparable and better performance in modeling of the nonlinear relationship
2 2 compared with PLS (r pred = 0.966, q = 0.928 and RSEP=%5.882).
References:
1) F.G. Bordwell, “Equilibrium acidities in Dimethyl Sulfoxide solution”, Acc. Chem. Res., 21 (1988) 456. 2) J. Holland, “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Michigan, (1975). 3) Centner, D.L. Massart, O.E. de Noord, “Detection of nonlinearity in multivariate calibration”, Anal. Chim. Acta, 376 (1998) 153. 4) http://people.revoledu.com\kardi\ tutorial\kMean.
148 POSTER is to of ed and was GA- 149 (GA- that esult encode r squar over analyzing nonlinear molecular molecular ession compounds operties for with =0.9462 the 2 pr of against elate R 1021 development r ediction Regr ed in pr numerically theory that elation, to indices ficient The Linear model, advantageous molecular corr compar considering QSPR coef be chemists this 217-225. 1976. For of the was efractive may r descriptors on descriptors (2002) elation between dam, of of of compounds. Multivariate 62 ANN parameters corr , selected. eparation ee Amster e Syst. , Shahrood, Iran focused ed subset other , pr computational elations thr ediction performance In of wer by we Lab. pr A , corr calculation squar However small Algorithm- to Elsevier their a Intell. set, study ANN. ed., indices with echnology 1996. and used outinely in r solutions. e this 2nd establish data Genetic descriptors Inc; choose a e, In to developed. wer data used to Chemom. of like e efractive network r selection ar polymer Dekker Fan, ANN QSPR structur . input T the cel models molecular 140 B. on variables. as neural selection and Mar methods e the for variable ANN five Hu, compilation , chemical used of used D. ork: techniques for edicting Y Z. featur pr with modeled artificial GA-MLR finally been Liu, in New a used include edictions numerical model GA-MLR; , Shahrood University of T , Shahrood University the C. (QSPR) e ed. pr and elation validation of have M. wer useful study 2nd 569-639. corr good and polymers. be , compounds, GA-MLR (ANN) Zhang, polymers; descriptors, checked elations S. (1998) 140 set can Quantitative gives e value. om Relationship operties, GA-MLR R. GA-MLR fr 49 operty M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi and Z. Kalantar M.Ali Ferdowsi, H. by pr polymers: wer interr pr data for 1998;49:569–638. of ed. oach indices; Networks the Zhang, the Chem. molecular . operties =0.9275 appr Faculty of Chemistry 2 Y polymer steps Chem of Pr R obtained ee exhibited X. operties of compar e polymer Phys. compounds. es experimental Pr efractive . e Neural Phys thr r QSPR of QSPR Studies of Refractive Indices of Polymers by GA-MLR and ANN by GA-MLR Indices of Polymers of Refractive QSPR Studies ang, Rev the considers ficient wer each general Rev W . featur indices it ediction This Structur evelen, these chemicophysical W QSPR; Pr
for . with coef The Kr descriptors Annu. Y J. Artificial . Annu to of esults . ds: r datasets e van ao, W . The Y ences: and because Knoll, efractive W the J. . elation r structural eement Bicerano Knoll W D. X. Quantitative analyze MLR) bioactivity the structur MLR the and ANN. corr descriptors behavior constructed. agr Keywor Refer 1) 2) 3) 4) 5) POSTER
Prediction of Aqueous Solubility of Drug-Like Compounds Based on Multilayer Regression and Neural Network Modeling
M. Ali Ferdowsi , H. Nikoofard and N. Goudarzi and Z. Kalantar Faculty of chemistry, Shahrood University of Technology, Shahrood, Iran
On the environmental behavior study of organic contaminants, solubility in water described by the parameter SW is a very important factor in environmental science. Aqueous solubility is a physical property that has been extensively studied. As a property involving water as the solvent, it is important in environmental toxicology and chemistry. The biological toxicity of a contaminant molecule is affected by its ability of being transported and absorbed in environment and in vivo organisms. To overcome the problem of insufficient data in the field of environmental risk assessment, physical chemical properties, and environmental fate of organic chemicals, quantitative structure–property relationships (QSPR) between descriptors of chemical compounds and their physical, chemical and biological properties have been extensively studied. The development of QSPR can aid in the understanding of aqueous solubility mechanism and can obtain a reliable and predictive model for predicting the properties of new chemical substances. QSPR approach has become very useful in the prediction of physical and chemical properties. This approach is based on the assumption that the variation of the behavior of the compounds, as expressed by any measured physical or chemical properties, can be correlated with changes in molecular features of the compounds termed descriptors. The main steps involved in QSPR include: data collection, molecular geometry optimization, molecular descriptor generation, descriptor selection, model development and finally model performance evaluation. The data set is includes aqueous solubility of 145 diverse drug-like organic compounds. All molecules were drawn into Hyperchem and pre-optimized using AM1 molecular mechanics force field. All calculations were carried out at restricted Hartree Fock level with no configuration interaction. An accurate and generally applicable model is derived, consisting on a linear regression equation that involves four DRAGON molecular descriptors selected from more than a thousand available .Once descriptors were generated, a forward stepwise regression method was used to develop the linear model of the property of interest. The descriptors obtained from liner model used as input data in ANN. A feedforward ANN used in this paper consist three layers, namely input, hidden, and output layers. The feed-forward backpropagation ANN applied to predict the solubility data. Network modeling and analysis performed using the MATLAB software with particular use of neural network toolbox. The results obtained show that this ANN modeling able to establish a satisfactory relationship between calculated molecular descriptors and the solubility and could be use to enhance the performance of designing the new derivatives.
References: 1) Kartritzky AR, Maran U, Labonnov VS, Karelson M. Structurally diverse quantitative structure–property relationship correlations of technologically relevant physical properties. J Chem Inform Comput Sci 2000;40:1–18. 2) HyperChem 7.0, Hypercube, Inc., 2002. 3) R. Todeschini, Dragon software for the calculation of themolecular descriptors, Rel. 1.1 for Windows, Milano, 2000. 4) Goodwin, J. J. Drug Discov. Today Technol. 2006, 3, 67. 150 POSTER in as the the the the and 151 who oved e is Milan pr ar oduced and lengths. started structural molecular known obably intr indices ent when e connectivity connectivity or of pr indices Hosoya ar index is fer of of such iener obably physicochemical, dif descriptor pr W of applications started of Randic of kind stage physical which TIs work this of molecular molecular TIs Balaban Estrada. of in this this when of in these by efraction, critical volume , the ch the of chains of idea operties ediction ch All studies term definition by seminal pr in and indices, es. esear quality r the starting esear oduced r the TIs and original of head mention example for the state of of first intr e the clusters 2000. structur with can contribution , ove stage The rules good QSAR/QSPR , Shahrood, Iran on wher d stage index we in etation impr description very thir TIs, parts. A to based Germany certain . the used e molecular ee The otopological important second continuing of interpr ar in echnology thr ways path–clusters, TIs The applications use 97-1001 the connectivity electr most and einheim, widely , into permits successful 77, W chemistry the the Some Constant. paths, , Kier These These range entiation that TIs, been . s. most of includes 2004, TIs. alkanes fer om Law divided of fr 80’ long way dif the of iley-VCH, descriptors. be a have and Acta medicinal W However the in and in indices can s e chemistry existing as ess, Henry's coming oatom points oduction Chem. 70’ of Among 1975. ogr graphs the and well molecular intr in pr kappa the oat. of heter as structur Descriptors, Hall. Cr fields in situations , Shahrood University of T , Shahrood University boiling indices(TI) the for these 31. index and under Hall capacity the Randic molecular selective 35, for indices. contributions still several e in ovement index, Kier Molecular by and opological Index in Description of Chemical Pr of Chemical Index in Description opological complex molecular eb heat is om in account for of e by fr 1995, mor e, impr ch to the topological Kier a Zagr TIs branching indices mor Sci. M.Ali Ferdowsi, H. Nikoofard , N. Goudarzi and Z. Kalantar M.Ali Ferdowsi, H. of Randic by by operties of chemicals such as molar volume, evaporating heat, molar r operties of chemicals such as molar volume, of the oduced changes essur to esear r pr account as derived intr Handbook variable the of describe to developed Faculty of Chemistry history extended Comput. On applied original molecular well to for topological index S. critical Inf. The as also stage state the the and e, e applications es applications Consonni, characterized Application of T Application useful . term extended descriptors This of V is of wer Nikolić, Chem. account it indices. J. the very but A.; featur oduced to e including and connectivity index. ar developed intr temperatur s indices number ences: otopological čević, odeschini, TIs T Estrada, extension 90’ index oduced iener R. E. Mili eat Mathematical topological his intr Randic gr indices branching These that critical biological and toxicological pr the the electr indices, W molecular variable Refer 1) 2) 3) POSTER
2-Dimensional Quantitative Structure-Property Relationship Modeling Study of Some Organic Compounds Henry's Law Constant Based on GA-MLR and MLR
M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi, Z. Kalantar Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran
A comparative study of genetic algorithm multiple linear regression (GA-MLR) and multiple linear regression analysis (MLR) techniques for understanding 2D quantitative structure-property relationship (2D-QSPR) on some organic compounds was conducted using distance and connectivity based topological indices (Wiener, Balaban and Randic Indices). The structures of all organic compounds were drawn into the HYPERCHEM program and and optimized using semi-empirical AM1 method, applying a gradient limit of 0.001 kcal/Å as a stopping criterion for optimized structures. Then topological indecis used as 2D-QSPR were calculated for each compound by the DRAGON software on the minimal energy conformations. Models generated were used to predict the Henry's Law Constants for a set of test compounds. The results indicated that the GA-MLR method proved to be superior of the two in developing 2D-QSPR model in all the cases as compared to MLR. In the training set, the prediction power of GA-MLR was very high and also it performed better in estimating the activity values for the test set. These QSPR approaches may be a useful to screen in new compounds candidates from larger compound libraries to be further evaluated. The R2 and root mean square error values for the training set MLR and GA-MLR were (0.84, 0.369) and (0.91, 0.422) respectively. The prediction results are in very good agreement with the experimental values.
Keywords: 2D-QSPR; Henry's Law Constant;MLR; GA-MLR
References:
1) V. Venkatraman, A. R. Dalby, Z. R. Yang, J. Chem. Inf. Comput. Sci., 2004, 44, 1686–1692. 2) M. Randic and A. T. Balaban, J. Chem. Inf. Model., 2006, 46(1), 57 – 64. 3) Todeschini, Dragon software for the calculation of the molecular descriptors, Rel. 1.1 for Windows, Milano, 2000.
152 POSTER N of the the 153 used AAS) time- good to central hollow a (ET samples. points, design was analytical less as on , composite Qualimetrics ding interactions of start maximized concentration, and ) water showed ometry p model and based accor A central 2 that particular Generally suggested ehran, Iran APDC spectr In (2N+C . natural , T ANOV [9]. factors ehran, Iran chosen method was optimization pH, factors of with and polynomial be fractional as Chemometrics runs and a ee of model, four fects can absorption , Evin, T sensitive such eased ef four the simultaneously of incr biological the ) high esponse, atomic Also, N-1 r ellurium Speciation experimentation Handbook in strategy 1991. (2 polynomial and second-degr HF-LPME , Alireza Ghassempour to development 1 factors parameters. (VI) A ork, design, this Y e of all points. the T erbeke, conditions a design with othermal design ith simple e runs. New for a W the statistics and start value experimental electr varying called tool Sons, find (IV) Smeyers-V ocedur by work, & factorial e to J. best pr T , Farhad Raofie of iley 1 (2×4+4) applying experimental optimum was W the esent Lewi, experimental components. essential performed pr with .J. The Se(VI), followed P e points 20 John step 93. experiments, an ar the extraction of showed obtain the of ed., Jong, next eased d to Se(IV), of (2002) 3r quadratic de systematically the incr of 72 ements der S. developed J. contour established. becoming for or selenium , Ensieh Ghasemi their consisted oxtraction for Selenium and T oxtraction for Selenium 1 , Medicine Plant Institute, Shahid Beheshti University , Medicine Plant Institute, its is sequence 24-1 was In be , Faculty of Science, Shahid Beheshti Uinversity , Faculty of Science, consisted model, and measur and e. examination.In ochem. and Buydens, Experiments, DOE can matrix the work, of ultra-traces CCD e, of technique Michr planned under of factors this a CCD L.M.C. (HF-LPME) surface tellurium In temperatur Analysis ocedur the four ocess variations experimental the Application of Response Ssurface Methodology and Ssurface Methodology of Response Application pr Fractional pr Barzegari, and adequacy the and 2003. esponse total H. r fect multivariate the andeginste, for out. the in oextraction af a performing variables. V oaches, of time esponse dam, Design e, is determination r , of By run Najafi, Nahid Mashkouri Najafi* micr univariate fects Central Composite Design for Modeling and Optimization of Hollow Design for Modeling and Optimization Central Composite appr on efor ef and B.G.M. Fiber Liquid Phase Micr Fiber Liquid Phase was Estimated Amster N.M. (DOE) checking , 1- Department of Chemistry than phase Ther duration number objectives main them [10,11]. parameters ). 2- Department of Phytochemistry p Montgomery After (CCD) Massart, the Elsevier ences: ent liquid C. Massumi, A, speciation fer D.L. chemometric D. A. esponse(EF). In experiments consuming methods between particular fiber for Dif extraction design being points(C including fitting. r Refer 1) 2) Part 3) POSTER
Prediction of Retention Indices of Some Essential Oils Using Linear and Nonlinear QSPR Methods
Nasser Goudarzi, H. Salimi and M. Arab Chamjangali Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran
In this work, two QSPR methods were applied for modeling and prediction of retention indices of some essential oils with descriptors calculated from the molecular structure alone. The stepwise multiple linear regression and genetic algorithm methods were used to select descriptors which are responsible retention behavior of these compounds. Then artificial neural network (ANN) and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure–property relationship models. The QSPR models were validated by cross-validation as well as application of the models to predict the retention indices of external set of compounds, which did not have contribution in model development steps. The obtained results using ANN with different variable selection methods were compared with MLR. It revealed that the ANN models were much better than MLR model. The root means square error of prediction (RMSEP) for training and prediction sets by GA-MLR and GA-ANN models were 0.36, 0.26, 0.11 and 0.15, respectively. Also, the relative standard error of prediction (RSEP) for training and prediction sets by GA-MLR and GA-ANN models were 7.02, 3.52, 7.68 and 3.88 respectively.
References:
1) N. Goudarzi, M. Goodarzi, Molecular Physics, 2008, 106, 2525. 2) M. H. Fatemi, N. Goudarzi, Electrophoresis, 2005, 26, 2968.
154 POSTER e: A. 30 ar der) was 179 C. of . linear it solute values P oduces 1-or pr the containing calculation ANN alho. of information these V of factors in of of descriptors which ors water J.J.Car oach omatography-mass err ehran, Iran in e ediction etention training symmetry Structural r chr pr stepwise-multiple selected in and the squar comparison the acetate LC-MS-MS The liquid of oss-validation edict The model then by mean . cr (Mor10m), pr use , Gachsaran, Iran to by and
The by MLR (neighborhood ehran Branch), T optimization of set, ammonium analysis masses sets. used water 2 development. espectively e r that in After data content evaluated using wer in ater Using QSRR Appr ater Using atomic e e over training model by aghoobi wer pesticides subsequent solute and network. (ANN) ANN MLR 1.337131, mixtur of nal the and information each and and oups of neural models weighted inter gr for / network
, Fateme Y step 1 as pesticide ANN 0.85173 artificial well the Structural neural e chemical descriptors superiority as an calculated these wer e of extraction set the esolve of r artificial wer sets fourteen , Faculty of Engineering, Azad University , Faculty of Engineering, test to input (MLOGP2), (SIC0). significant 3D-MorseSignal10 and nal test as e showed Amir H. M. Sarrafi used der) nal solid-phase obustness mor exter , Faculty of Science, Azad University (Central T , Faculty of Science, used r measuring (MLR) (PMIZ), descriptors e 0-or of was by partition exter for of and wer select ession parameters Some and to factors inertia-z elution egr method r analyzed of .Alpendurada used phase. symmetry validation training descriptors esidue statistical linear that octanol-water etention was r for Gradient The 2- Department of Chemistry mobile moment . multir these ediction of Retention of LC-MS Pesticides in W LC-MS Pesticides of Retention of ediction the a [1]. water Pr the times multiple of Goncalves.M.F esults. step method in r calculated factor as edict Moriguchi C. (neighborhood pr 1- Department of Chemistry Principal work next ometry ences: ed to other ession etention r onimo. this the Evaluation egr etention In pesticides spectr methanol r Squar (SIC1), content In used of and r successful Refer 1) Jer POSTER
Factorial Analysis and Response Surface Optimization of a Peroxyoxalate Chemiluminescence of Trazinyl Derivative in the Presence and Absence of Some Surfactants
A. Yeganeh-faal1, T. H. Shayeste1, J. Ghasemi2, M. Bordbar3 1- Department of Chemistry, Payam noor University,Hamadan, Iran 2- Department of Chemistry, Faculty of Sciences, Khaje nasiredin University, Tehran, Iran 3- Department of Chemistry, Islamic Azad University, Qom-Branch, Qom, Iran
Fractional factorial designs have been used in a lot of studies aiming to improve processes and to spare measurement time. On the other hand, the optimization of analytical procedures by multivariate techniques [1] is faster, more economical and effective than the traditional “one-at-a-time.” Peroxyoxalate chemiluminescence (PO-CL) is well known as a powerful means of detecting various fluorophores [2] NHPh SO Na H 3 N N N N NH N N(EtOH)2 PhHN N SO3Na N
N(EtOH)2 In this work, fractional factorial design was applied for investigation of the experimental variables of a chemiluminescence system basis on indirect chemiluminescence from triazinyl dye 4,4'- Bis {[4-anilino-6-bis (2-hydroxyethyl) amino-1,3,5-triazin-2-yl] amino} stilbene-2,2'-disulfonic acid- disodium salts. Result showed that interaction of this factors was considerable. Thus one at the time technique to optimization of this system con not be used and don't be useful optimization method. Accordingly we use the response surface method to optimization of this chemiluminescenc system. A chemometric approach, including a fractional factorial design for screening and simplex optimisation, was successfully applied to develop a chemiluminescence of luminol system. Also the quenching effect of same cations on optimized system was investigated. The method is suitable for quantification purposes if equipped with FIA.
References:
1) S.L.C. Ferreira, A.S. Queiroz, M.S. Fernandes, H.C. dos Santos, Spectrochim. Acta B 57 (2002) 1939. 2) J.G. Burr, Chemi and Biolominescence, Marcell Dekker, New York, 1985, pp. 245-258. 3) Salah M. Sultan and et al, Talanta 49 (1999) 1051–1057
180 POSTER l- of om this was 181 fr ogen It of hydr tetracosa- isolucin, energy-rich detail. e and in evaluated [1] mor determination e 19-tetraazatricyclo optimization es or [17.3.1.1] imidazol, for wer to 15, studied one 7, use ophor of oxide ehran, Iran was , T can fluor method systems (TII) (histamine, 3 , Kashan, Iran study formation various simplex ogen Per 17-tetraazatricyclo this the in 20-tetramethyl-3, of 14, , Hamadan, Iran quenchers [2]. 6, , M. Salavati detecting modified 2 es 8,14, and cheniluminescent esults r esults of r 2, escer and Study Quenching escer and Study the super ophor The oxide and which means for the (TI) 245-258. per (TI) fluor tetramethyl-3, , J. Ghasemi use of 1 pp. oxalate eported. we ogen 23-decaene-10,12,22,24-tetraol r powerful e 1985, elationship between the chemiluminescence intensity and qountum elationship between the chemiluminescence a 21, hydr ar 22-tetraol parameters number , Payam noor University ) as 111. dingly ork, 2+ Y 20, a2,7,13,18- activated and large , B. Jamalian ,Hg of 1 an
a 11, New , Faculty of Sciences, Kashan University , Faculty of Sciences, Accor kinetic (1985) , 2+ . known oxyoxalate Ester (TCPO), Hydr oxyoxalate Ester escer Cd 177 , Faculty of Sciences, Khaje nasiredin University , Faculty of Sciences, (TII) with
and , 2 well escer excite 3+ Dekker O is fluor 2 Acta Al H cell , fluor 9(26),10,12,14,19, 2+ easily
of eganeh-faal 80. as characteristics Mar 7, Zn Chim. intensity 21-decaene-9, TCPO, (PO-CL) , can 2, act 2+ A. Y of 19, (1969) The Anal. to eaction Cu r 2 , 17, 2+ 1- Department of Chemistry Imai, the Res. esulting intensity-time plots. The r Co Super Modified Simplex Optimization Chemiluminescence Chemiluminescence Optimization Simplex Super Modified 13, K.
on om Reaction of Per om Reaction of Biolominescence, intermediates 11, system[3]. 3- Department of Chemistry fr Chem. and 9, hexacosa-1(25), concentration and based chemiluminescence and tetraazapentacyclo Derivative as Fluor and tetraazapentacyclo 2- Department of Chemistry Acc. chemiluminescence is osn These Miyaguchi, the K. 8(24), with tetraazapentacyclo Chemi l-tyr This fect of Some Cations and Amino Acids on Optimized Chemiluminescence System. and Amino Acids on Optimized fect of Some Cations 19,13] , 6, Rauhut, 1. Ef that work 2, ences Burr 3. Honda, oxide. oxyoxalate this J.G. M.M. K. feicency Per per intermediate. In 1(23), [19. found chemiluminescenc ef computer fitting of the r histamin, quenchers. Refer 1) 2) 3) POSTER
Determination of Dissociasion Constant of Preotonated Form a Triazin Derivative Dye by Spectrophotometric and Spectroflourimetric Method: A Study Chemometrics approach
A. Yeganeh-faal1, G. Dabaghian1, M. Haggo2, M. Bordbar3 1- Department of Chemistry, Payam noor University,Hamadan, Iran 2- Department of Chemistry, Islamic Azad University, Hamadan -Branch, Hamadan, Iran 3- Department of Chemistry, Islamic Azad University, Qom-Branch, Qom, Iran
Acid dissociation constant are imporant parameters to indicate the extent of ionization of molecules in solution and quantifying chemical phenomena such as reactionrates,biological activity,environmental faste at different PH values[1]. During the past two decades application of the micelles in a wide variety of analytical techniques has been a subject of grow inginterest [2]. Several methods for determining the CMC of surfactants have been repored. Among light scatcering, surface tension, spectrophoto- metry, spectroscopy and conductometry. [3-4] In the present work sensitive ,fast and suitable method for calculation of Pka values,of trazine dye (4,4'-Bis{[4-para sulfonate anilino-6-bis(2-hydroxyethyl) amino-1, 3,5-triazin-2-yl]amino}stilbene-2,2'-disulfonic acid- disodium salts) in water were done. The effects of sodium dodecylsulfate (SDS), triton X-100 (TX-100) and cetyltrimethyl ammonium bromide (CTAB) in aqueous solution were used for anionic, nonionic and cationic micelles were investigated. The study was performed in sub and above critical micelle surfactant concentration. Also spectroflourimetric and spectrophotometric data were acquired in mixture ethanol
water solvent. Acidity constant of protonated dye in various pH values of range 1-13 and at ionic strength 0.1m KNO3 and 25°C. were evaluated. The recent advent of powerful chemometric method multivariate curve resolution with constrained alternating least-squares (ALS) is used to determine the number of species and their distribution diagram. Spectra analysis was used for this propose. Pure spectrum of each component and concentration profiles are also acqured. Effect of concentraton and type of surfactants and nan aqueous solvent percent on acidity constant were investigated.
References:
1) D.Kara, M. Alkan, Specyrochemica Acta Part A, 56 (2000) 2753. 2) Z. Yunqin, L. Fary, L.Jing,Talanta, 56 (2002) 705. 3) M.L.Corin, W.D.Hakin, Chem. Soc 269 (1947) 683. 4) Ching-Erh Lin*, Chung-Chuan and et al, Journal of Chromatography A, 835 (1999) 197–207
182 POSTER
ee the etc. 183 than fourier degr initiator between by pipes, as considering parameters. to second important varied is a (DCP) e plastic e mor performed wer that work oxide e e cables, ar ar vinyltrimethoxysilane, experimental per this of of and factors . showed oxide e the various aim wir grafting per dicumyl e of amount ehran Branch. The Results the and wher the using , Babolsar oduce of 1 fects , pr ef peak. silane to the operties. mixer of include , Central T pr new 13-19 concentration. nal performed its of (2008). 38, e inter ar quantification estimate oxide employed variables an to 2008, per modify and runs in intensity The concentration to , E.Zamani Farahani and 2 and 2300–2308 the the , Mazandaran University used. 254, applications ocess is silane Composite, was and pr performed cial experimental the 2005) Science , Islamic Azad University polyethylene Rubber easing was design) , M.H.Fatemi Characterization factors 1 esponse r incr Plas commer Surface Several of The optimize eaction with r osslinking eaction. to 54,397-413,( r E.Konoz cr composite significant Applied time. agent. terms in eased used et, in M.Rogerson.J: most Grafting incr Materials . grafting was (central the 2- Department of Chemistry grafting grafting in Coquer method Determination of Main Factors in Silane Grafting of Factors in Silane of Main Determination level X. as ed the oscopy important design S.Mainprize, Linear Low Density Polyethylene Using Experimental Design Polyethylene Using Experimental Linear Low Density 1- Department of Chemistry J.Polymeric eferr and : estimate Mutel, variables most spectr Grafting pr B. to methodology ege a ed the is oxide some is M.Gilbert, Bigot, fitted per mixing. J. Experimental of infrar surface K.E.Geor of of was method fect ences: Ahmed, case. Bigan, vinyltrimethoxysilane ef time M. Sh.Isac, G.S esponse Polyethylene Silane the transform and each The experimental design consists of a plan of the specific values of the factors to be used for each run. Statistical design using of the specific values of the factors to be used for each run. Statistical design using The experimental design consists of a plan r amount design the Refer 1) 2) 3) POSTER
Prediction of Inhibitor Activity of 1,3,4-Thiadiazole-2-Thion Derivative to Carbonic Anhydrase by QSAR Methodology Using Genetic Algorithm-Artificial Neural Network Technique
Mehdi Mousavi, Solmaz Ahmadgolami Department of Chemistry, Faculity of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran. . The carbonic anhydrase (CA) constiyute are interesting targets for the design of pharmacological agents useful in the treatment or prevention of a variety of disorders such as glaucoma, epilepsy, edema and altitude sickness. A quit new application of the GA inhibitors (CAIs) is their potential use in management of hypoxic tumors. The aim of the presented work is to develop a model for predicting inhibitor activity of thiadiazole thione to carbonic anhydrase based on their molecular structure. In this study QSAR methodology was used to develop a mathematical model for inhibitor activity (log IC50) of 1, 3 , 4 thiadiazole-2-thione to carbonic anhydrase. Different steps have to be followed while applying QSAR. In the first step a set of fifty two 1, 3 ,4 thiadiazole-2-thione were chosen as data set then the data set was divided into two sets, training and prediction sets. In the second step a set of 1500 structural descriptors were calculated for each molecule in the training set. In analyzing the descriptors, 936 out of the 1500 descriptors show high autocorrelation or constant value, so they were excluded from modeling procedure. In the third step, Genetic Algorithm (GA) was used as a feature selection method to choose the most informative descriptors. According to GA calculations, 3 out of 564 remained descriptors were shown to be the most important. In the fourth step, back propagation artificial neural network (BP-ANN) was implemented to model the binding affinity of thiadiazole thione to the carbonic anhydrase for both training and prediction sets using the selected descriptors. The optimum architecture of BP-ANN was 3-3-1. Finally, comparison of calculated and experimental log IC50 of 1, 3, 4 thiadiazole-2- thione for training and prediction sets indicate good correlation and high predictive ability of the designed ANN. Statistical parameters of the result of modeling for training set are r = 0.995, and that of prediction set are r =0.993.
References:
1) M.k.Abdel-Hamid, a.a.Abdel-Hafez; Bioorganic& Medical Chemistry 15, 2007, 6975.
184 POSTER e a e is in of on ) the the W (log sets and b 185 sets, step in (A for featur ability emained r=0.920, excluded based two cognitive
(BP-ANN) r as first etases. e activity ediction deposition into the and and useful 528 pr wer etase calculated peptides -secr used molecule In g edictive e be of network of pr and -secr they g inhibitor and
wer amyloid each divided out b so r=0.922 may to QSAR. high for e -amyloid of for neural 10 b was ar (GA) impairment training and of tangle, value, set agents sets model olactam comparison action applying both , artificial data elation the memory such calculated for algorithm e while by constant the ofibrillary ediction corr calculations, Finally by or pr wer since etase accumulation Amino-capr neur , GA Then opagation modeling. b good genetic mathematical and of olactam Derivatives olactam pr A e. to the -secr a followed g 10-3-1. elation generated of and ANN be e back the shows ding plaques, ar to was activity descriptors the characterized training to literatur 5790. develop and sets of suggests (MLR) for autocorr Accor have der to senile om oduction fr 2007, MLR of pr peptides inhibitor
BP-ANN high disor olactam ession b steps used structural 17, . ediction A Evidence the of step, egr pr superiority r modeling ent chosen e was show AD. [1]. 1497 fer edicting descriptors. and the forth was pr of inhibit Dif Chemistry linear of shows with 1497 Amino-capr accumulation the death for set chitectur of In of that 1372. odegenerative a ar the esult training etase. r esults r Medical out by olactams methodology multiple for model neural neur informative step the 2006, -secr a g associated Mehdi Mousavi, Solmaz Ahmadgolami Mehdi Mousavi, Solmaz activity ANN 969 of is 27, and to step, important. QSAR most optimum and d essive, , Faculity of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran. Shahid Bahonar University of Kerman, , Faculity of Sciences, compounds olactam second that Bioorganic& loss, . develop Aging amino-capr thir the ogr of The most inhibitor study MLR to [2] the pr pathologically is of the two the parameters In a descriptors, the obiol. derivatives AD this toxicity In is be synaptic of In the work e. Neur Amino-capr sets. to choosing e. onal Abdel-Hafez; model twenty of ediction of Inhibitor Activity of Amino-Capr of Inhibitor Activity ediction etase by QSAR Methodology Using MLR and Artificial Neural Network Methodology Using MLR and etase by QSAR (AD) descriptors. olactam for to identification Statistical of Pr A.A. neur vessels, characterize ocedur comparison Sdhmitt; shown in is esented pr structur ediction e analyzing the eventation pr and logIC50 ANN. pr disease DA. AD pr In Also, for f, wer . s selected the or ested methods to Y-secr Department of Chemistry consisting and set. Amino-capr of tissues the Schef Abdel-Hamid, ences: of inter . implemented modeling . molecular set aim designed e S.W M.F eatment om esponsible espectively Alzheimer dysfunction. neural r have tr The their IC50) data training training fr selection descriptors wer using experimental the r Refer 1) 2) POSTER
An Improved HPLC Method for Rapid Quntitation of Atorvastatin Using an Experimental Design
E. Konoz1, M.H. Fatemi2, S. Ardalani1 1- Department of Chemistry, Islamic Azad University, Central Tehran Branch. 2- Department of Chemistry, Mazandaran University, Babolsar.
A rapid and sensitive RP-HPLC method with UV detection for routine control of Atorvastatin in tablets was developed. Chromatography was performed with mobile phase containing a mixture of acetonitrile/water (30mm) (50/50; v/v) with a flow rate of 1 ml min-1 and detector wavelength at 238 nm. The retention time of Atorvastatin was found to be 5.0 min. The procedure was validated by linearity (correlation coefficient = 0.996), accuracy, robustness limit of quntitation and limit detection. The linear dynamic range was from 5-40 mg ml-1. Experimental design was used during validation to calculate method robustness and intermediate precision, for robustness test three factors were considered, percentage v/v of acetonitrile, flow rate and temperature. An increase in the flow rate results in a decrease of concentration found of the drug, with the percentage of acetonitrile and temperature have no important effect on the response. The percentage recovery obtained for Atorvastatin was 99.5%. Limit of detection and limit of quantitation was found 0.84 and 2.54 mg ml-1, respectively, which indicates the method, is highly sensitive. The developed method can be used for routine control of Atorvastatin in tablet formulation.
Reference: G. Srinubabu, K. Jaganbabu, B. Sudharani, K. Venugopal. Chromatographia, 64 (2006) 95-100.
186 POSTER is of At by and 187 their rates This Medicinal stepwise and indicated ning calculated number & comparison network. training weighted model. lear 4/ using The solely models by and oups: MLR masses, neural gr parameters Bioorganic of ent two ficient of Drugs ficient of nek, that chosen elation-lag atomic algorithm. discussed ar fer artificial e descriptors V by into dif . by momentum over the A. wer in autocorr and training ANN drugs e-based divided layers, ehran Branch. weighted between the , Babolsar design. Moran of some Solov'ev descriptors structur . 1 descriptors hidden of index/ .P opagation in of randomly .V BB , Central T Jr the e column, interaction in ee, log wer compound superiority terms back-pr Important the of aals Acr . in encoded W the
nodes accessibility new .E. , S. Ardalani and 2 using W of for der total sets
modeling D compounds van modeled showed elson, espectively r to useful , Mazandaran University test information be Kar number parameters be index, M. value) was and the atomic can F , M.H. Fatemi 1 [1].These as may , Islamic Azad University BB) Fara, and shape study molecules, structural Mean that such training SE (log e: D.C. qualitative 12 this jean , The ar selected (R, E. Konoz of the e with and pet drugs ANN. wer 52 3D main of Dobchev by information parameters of employ e ed The descriptors D.A. drugs parameters optimized , can e ANN, 64 2- Department of Chemistry In These of structur wer Slavov negativities, ANN penetration consider containing o S. molecule. , statistical be 1- Department of Chemistry a ) and number of donor atoms for H-bonds(with N and O). Then the neural network was developed using ) and number of donor atoms for H-bonds(with that 3 inputs. of biases consist of specific essions. electr can barrier them as e fact Kuanar and egr of r and M. values the which 2006. brain , elation to r each 14, set the structur linear Sanderson weights due descriptors ences: the blood- sets, Katritzky data Artificial Neural Network Modeling of the Blood-Brain Penetration Coef the Blood-Brain Modeling of Neural Network Artificial significant the om The
fr first test multiple atomic these total secondary C(sp for between partly nonlinear the Refer A.R. Chemistry POSTER
Predictive Ability of Multivariate Calibration Methods for Simultaneous Quantification of +2 Tebaine and Noscapine Using Chemiluminescence System of Ru(phen)3 and Acidic Ce(IV)
A. Mokhtari, B. Rezaei Department of Chemistry, Isfahan University of Technology, Isfahan, Iran
In this study a comparative study has been performed to determine, predicting ability of multivariate calibration models including PARAFAC, NPLS and PLS for simultaneous quantification of tebaine and noscapine. In this work we employed flow injection (FI) +2 analysis using Chemiluminescence (CL) system of Ru(phen)3 -acidic Ce(IV) for determination of the compouns. In our investigations for determination of alkaloids, we found that, in bath mode, tebaine gives a broad peak whereas noscapine produces a sharp and intense peak. These occur due to different reduction rates of tebaine and noscapine in the reaction. Moreover effect of increasing sulfuric acid concentration is different over the CL intensity of each compound. Typical CL profiles of Tebaine, noscapine and their mixture in FI mode are shown in the Figure below. To use PARAFAC and NPLS models, 16 standard solutions were prepared from different concentrations of tebaine and noscapine (1×10-6 ~ 2×10-5 M) using full factorial design. The CL intensity of each sample was recorded in compromised optimum conditions of Ru(phen)3+2 and Ce(IV). To obtain a three dimensional matrix, CL intensity of each sample recorded for 1000 points of time with intervals of 12ms at 4 different concentrations of sulfuric acid (16×1000×4). For PLS calibration method, compromised +2 concentration of sulfuric acid also applied along with optimum concentrations of Ru(phen)3 and Ce(IV) and CL profile obtained for each sample therefore, a two dimension data matrix constructed (16×1000). The number of factors for the model regression was selected based on the minimum values for the root mean squared error of cross validation (RMSECV). The performance of the models was evaluated using a prediction set (3 samples). The results reveal that NPLS is the best one for predicting of the concentration tebaine and noscapine in the mixture. These results were with accordance to those obtained in our previous study for simultaneous determination of codeine and noscapine [2].
Refrences:
1) A. Smilde, R. Bro, P. Geladi; Multi-way Analysis; John Wiley and Sons August 2004 2) B. Rezaei, T. Khayamian, A. Mokhtari, J. Pharm. Biomed. Anal. 49 (2009) 234–239
188 POSTER of en- 189 metal opted HNMR 1 method of fitting some and of IR by eparation pr multivariate ophotometric 2-(10-oxaphenanthr as constants by spectr and well ) a confirmed 1 as (L stability studied also using , Urmia, Iran e the science wer was oscopic Studies of Cu(II), Ni(II), Studies of Cu(II), oscopic ions
oline-5,6-dion 2+ determined determine e ocyclic complexes highly selective and sensitive to ocyclic complexes highly complex Zn separation to alentina in wer V and each b,
used 3716–3721 2+ used of Ni
be been 2+, 1,10-phenanthr (2008) complexes Prstojevic´ Co can 49 has , 2+ the formation of Cu namely oach which 2395–2404 Polymer a,*,Dusˇica The , Faculty of Science, Urmia University , Faculty of Science, appr with ions
Qin, wo Newly Synthesized Ligands in Acetonitrile Solution Ligands in Acetonitrile wo Newly Synthesized ligand (2007)
2 L constants 26 metal Jingui on ch has been aimed to design heter ch has been aimed to solution. and Nedeljkovic´ Xu,
1 acetonitrile. Li L stability esear in synthesized chemometrics Jovan M the transition a, *,Polyhedr Zhong, of oach for the Thermodynamic and Spectr the Thermodynamic oach for acetonitrile Nasser Samadi, Mina Salamati, Morteza Bahram, Ali Soldouzi Salamati, Morteza Bahram, Ali Soldouzi Nasser Samadi, Mina and 0.01 model between ow d in of r insoluble Jackson Cheng Jankovic´ eat deal of r E. ratios har Department of Chemistry first a data ength eaction Ivana ang*, r nearly Y ). a, mole str 2 Graham (L work two . ionic Chuluo especially of this , Kuljanin Zvimba, In and Zhu N. d-Modeling Appr d-Modeling ions, ophotometric ometry ences: °C complexation metal–ligand Co(II) and Zn(II) Complexes With T Co(II) and Zn(II) 25 John Linna Jadranka Har In the last few years a gr In the last few years metal sensors. complexes 9(10H)-ylidene) The spectr The spectr at Refer 1) 2) 3) POSTER
Modeling of Decolorization of Allura Red solutions Using Response Surface Methodology
E. Ghorbani–Kalhor, A. Naseri*, Soheila Mohammadian Department of Chemistry, Faculty of Science, Islamic Azad University, Tabriz branch, Tabriz, Iran
Azo dyes are used extensively in textile dyeing, finishing operations and contribute to the pollution problems associated with disposal of a considerable amount of wastewater containing residual dyes [1]. The release of those colored wastewaters in the ecosystem constitutes a dramatic source of pollution leading to the perturbation in the aquatic life. Consequently, technological systems for the removal of organic pollutants such as dyes have been recently developed. Chemical methods, especially advanced oxidation process (AOPs) seem to be more promising. Dyes degradation was been the object of several study based on AOPs such
2+ as photocatalytic degradation, ozonation, H2O2 photolysis (UV/H2O2) and Fenton's reaction (H2O2/Fe ). In this study, the removal of Allura Red, one of the synthetic dyes with functional group azo which is used in textile, foodstuff and
2+ pharmaceutical industries, was studied by using Fenton's reaction (H2O2/Fe ). Spectrophotometry was selected as a simple analytical method for determination of Allura Red decolorization efficiency. The effects of relevant parameters (pH and
2+ concentrations of H2O2, Fe and Allura Red) with the decolorization efficiency have been investigated. Different experimental design methods can be used to model the decolorization efficiency [2]. Central composite design method
was selected as a response surface methodology for modeling the degradation process. Independent parameters were CH2O2, CFe2+,
Cdye and dependent variable was decolorization efficiency. Finally a mathematical model was obtained between decolorization efficiency and variables. Obtained model was validated using different experiments.
References:
1) M. P. Ormad, R. Mosteo, C. Ibarz, J. L. Ovelleiro, Applied Catalysis B: Environmental 66 (2006) 58–63. 2) E. Sayan, Chemical Engineering Journal 119 (2006) 175–181.
190 POSTER - is in of can it UV 191 with after . using (AOPs) dyes es variance They of dyes emoval cosmetics, r of orthogonal performing ocess simultaneous equently eatment, in the mixtur pr fr tr the parameters food, oblems. for means of 1991 emained pr r work, by using binary ficulties of VCH, elevant in concentration oxidation this r dif systems printing, ficiency ed., of abriz, IRAN of the ef dyes decolorization e onmental major parameters the fects paper for second investigated ef advanced (MR) envir object the concentration measur of used The Red technological been to elevant is r , dyeing, main edict serious one Pigments, abriz Branch, T investigate pr especially and have the o peaks , T and to But Methyl studied. , T
textile cause ocesses, MR . as pr Dyes was and used Finally and ocess.
Consequently methods, . 27) pr such was 27 overlapped Organic industries decolorization (AR color AR of oxidation ophotometry 27 model fenton
Chemical ong these (H2O2/Fe2+), calculated. of industries Fe2+, among str Red PCR of spectr e f yadi, A. Naseri its is discriminate advanced wer Applications A.C to -V , Islamic Azad University eaction to H. A r H2O2, ficiency the of is UV dyestuf and developed. obtained due ef of of them astewaters constructed and the of W using been one
Fenton's operties analysis ecently the oxygen r Pr has [1]. textile ocess, using ophotometry been e pr detecting the ficiencies model concentrations ef Modeling and Optimization of Simultaneous of Simultaneous and Optimization Modeling dissolved optimized. Synthesis, wer in A (PCR).First for spectr . have simultaneously simultaneous MR Fenton was used dyes photography used using namely; the ession (CCD). and dyes , is as consume Chemistry egr model r 27 color . Decolorization of A.C Red 27 (AR 27) and Methyl Red (MR) Dyes of A.C Red 27 (AR 27) and Methyl Decolorization with widely omising. and such design decolorization AR Color e analysis pr ficiency and life e ar Department of Applied Chemistry Department of Applied of water ef deals determine (Ed.), obtained Next, mor to dyes ophotometry component waste be study pollutants and aquatic composite or ences: to oy Zollinger spectr H. esent eatment. is Synthetic pharmaceutical destr organic seem V necessary multicomponent water Pr principal tr decolonization decolorization central analysis Refer 1) POSTER
Simultaneous Determination of Trimetoprim and Phthalazine Using HPLC and Multivariate Calibration Methods
A. Naseri*1, S. Asadi2, M. R. Rashidi3 1- Department of Applied Chemistry, Islamic Azad University, Tabriz Branch, Tabriz, IRAN 2- Payame Noor University (PNU), Ardebil center, Ardebil, IRAN 3- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, IRAN
High performance liquid chromatography HPLC is used to separate and determine of chemical compounds. When the chromatograms of compound are overlapped, it is necessary to use chemometrics methods for resolution and determination of analytes. But the main problem is arised when two compounds have the same chromatograms. In the present study, multivariate calibration methods were applied to determine two analytes with the same chromatograms. In order to successful performance of this method, molar absorptivity of analytes must be different in selected wavelengths. Chromatograms of Trimetoprim (TMP) and Phthalazine (PHZ) [1] and antibacterial drugs, completely overlapped. In this study these compounds selected as models to determine. Two multivariate calibration methods, classical least squares (CLS) and inverse least squares (ILS) were applied to quantify TMP and PHZ by HPLC with UV detector. The chromatograms were obtained at four-wavelengths (235, 250, 260, 285 nm). Sixty binary mixtures of two compounds were used as calibration set. The validity of building multivariate calibration models was checked using test set consisting of eight samples. The proposed methods were successfully applied to the simultaneous determination and resolution overlapped peaks in HPLC using multivariate calibration methods. It was found that the relative standard error of prediction (RSEP) of CLS for trimetoprim and phthalazine were 0.09-0.76% and 0.05-3.32% respectively. Also the RSEP of ILS for them was found to be 0.09-0.73% and 0.04-2.95% respectively.
References:
1) Di Cocco, G. Orlando, L. Bonanni, M. D'Angelo, K. Clemente, S. Greco, G. Gravante, F. Madeddu, C. Scelzo, A. Famulari, F. Pisani Transplantation Proceedings, 41 (2009) 1201-1203
192 POSTER . of the the 193 for models periodate
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determination by samples. constructed eaction r principle periodate eal r The by the and on ophotometry Network
several ophotometric simultaneous in ocessed iodate methods. based for pr ophotometrically is e for Spectr spectr
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, Zhu : . to in P in and , ds neural and work ences: able Guo iodate ease Mukesh . ANN Khayamian e . . this W T D In iodate simultaneous and decr artificial wer The Keywor Refer 1) 2) 3) POSTER
Applied Artificial Neural Networks Modeling to Uantitative
Structure-Properties Relationship Study of Lipophilicity Activity of Some
Long Hydrocarbon Chain Keto-Diols and Their Phosphates Esters and Acides Derivatives
M R Sohrabi1 Nasser Goudarzi2 F Hamidi1 . . , , . 1- Department of Chemistry, Islamic Azad University, Tehran North Branch, Tehran, Iran 2- Faculty of Chemistry Shahrood University of Technology, Shahrood Iran
The ability of a drug to penetrate various biological membranes, tissues and barriers is a primary factor in controlling the interaction of drug with biological systems. Lipophilicity (P) is defined by the partitioning of a compound between an aqueous and a nonaqueous phas. Correlation of biological activity of some compounds experimented as cardiovascular agents with their ability to penetrate biological membrane, as reflected by their lipophilicities. This study is designed to asscss the corrclation between the theoretically calculated lipophilicity of the some long hydrocarbon chain keto - diols (and their phosphates) and acids sompounds and their biological activity. Quantitative structure-properties relationship (QSPR) has been applied to modeling and predicting the lipophilicty of a series of long hydrocarbon chain keto diols (and their phosphates) and acids. Descriptors that were selected by siepwise multiple linear regression (MLR) technique are polarizability, steric parameter, molecular weigh and number of carbon atoms. These descriptors were used as input for generated 4-7-1 artificial neural network (ANN). The results obtained using ANN and MLR were compared as well as with the experimental values. Also the appearance the these descriptors in QSPR model reveals the role of electronic and structural parameters in lipophilicity of these compounds.
References : 1) J.L.H. Desseux and D. C. Oniciu., J. Med Chem. )2006( 245. 2) C. A. Lipinski, F. Lombardo and P. J. Feenedy,. Ads. Drug. Deiv. Rev., 46 )2003( 3.
194 POSTER e of and was 195 wer fect PH cationic ef calculate by to best the concentration the parameters expriments times salt 9 and ee extraction w/v), thr , Zafar Street optimization Others %( point Method. the experimental ith 10-5 W epeated the r cloud aguchi T was In Investigate by optimized we comparison time. AB) , Daftary shargi alley in concentration we (CT ahid Kiarostami AB paper experiments optimized study CT of ) e this omide Ŷ ar ( incubation In this br parameter In on and Iran. e in fective Based ef Diazinon.Each parameters used ammonium ehran, Department of chemistry ehran, Department of is Parameters. most factors. emperatur T These has the that is time30min. fective extraction ehran, Department of chemistry ehran, Department time ef Cetyltrimethyl on pesticides 265-270,2004-32 optimization, point, concentration, incubation 524, 159,300-305,2008 the Azad University Of T Azad University Of , ,113,313-319,2009-43 incubation aguchi's Experimental Design for Optimization Design for Optimization Experimental aguchi's of salt Sarah Jamshidi, Mahmud Reza Sohrabi, V Sarah Jamshidi, Mahmud and T Cloud Acta, parameters . ophotometrymethod. ophotometrymethod. determination that one of fective Parameters on Diazinon by Cloud Point Extraction on Diazinon by Cloud Point fective Parameters and by 35oc Materials
Chemistry
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e spectr fect organophosphorus ef dous of Ef showed Diazinon; Food Anal
method, and JJ; the the , Hazar Condition
surfactant ehran, Azad University of T ehran, Azad University of esults AB) r temperatur nandez; aguchi, T aguchi study like best Santna one
(CT T Her Our . is Iran, T J to ds: ). , the ZhiQiang; The Ŷ , ( ences: ed 6-7. Zhou Rodriguez Borges equir Diazinon parameter surfactant r mean in chosen 10-7(mol/lit), Keywor Refer 1) 2) 3) POSTER
A Simple and Cheap Double-Beam Photocolorimeter Fabricated for Simultaneous Determination of Binary and Ternary Mixtures
Mohammad-Hossein, Sorouraddin, Masoud Saadati Analytical Chemistry Department, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
Simultaneous determination of two or more compounds in a mixture is an important goal in multicomponent analysis techniques. Several spectrophotometric determination methods have been used for resolving mixtures of compounds with overlapping spectra without preliminary separation. Derivative spectrophotometry [1], continuous wavelet transformation [2], principle component regression (PCR) [3], H-point standard addition method (HPSAM) [4], and recently mean centering of ratio spectra [5] are the most reliable methods for binary or ternary mixtures. Although all these methods are significant advances in multicomponent analysis, nevertheless performing these methods requires using a wide range of wavelengths. In some cases such as using portable LED based photocolorimetric devices it's impossible to gain absorbance in a wide range of wavelengths because of inherent and physical limitation of light sources used in these devices. The present work is to describe a very simple method for the simultaneous determination of binary and ternary mixtures, without need for prior separation steps. The procedure for fabrication of a cheap, simple and portable double beam photocolorimeter is discussed. Tri-color LED (RGB) was employed as light source to obtain data in two or three wavelengths and a light dependent resistor was used as a detector. The method is based on dividing absorbance of the mixture by the absorbance of a standard solution of interfering compounds and subtraction of the obtained data. The mathematical explanation of the procedure is illustrated. After modeling procedure, the method was successfully applied to the simultaneous determination of binary mixture of two dyes and ternary mixture of three dyes. Under the optimum conditions, calibration plot was linear in the analyte concentration range of 1–40 µgmL-1 for Carmoisine and Sunset Yellow and 1-20 µgmL-1 for Brilliant Blue dyes. The relative standard deviations for five replicate determinations of 10 µg mL-1 three dyes were 0.8-3 %.
References:
1) M.I. Toral, S. Pope, S. Quintanilla, P. Richter, Int. J. Pharm. 249 (2002) 117-126. 2) A. Afkhami, D. Nematollahi, T. Madrakian, M. Abbasi-Tarighat, M. Hajihadi, J. Hazard. Mater. 166 (2009) 770-775. 3) J.J.B. Nevado, J.R. Flores, G.C. Peñalvo, Anal. Chim. Acta, 340 (1997) 257-265 4) G. Bagherian, M.A. Chamjangali, H. Eskandari, Spectrochim. Acta, Part A, 67 (2007) 378-384. 5) M. Bahram, T. Madrakian, E. Bozorgzadeh, A. Afkhami, Talanta, 72 ( 2007) 408-414.
196
POSTER 119, 172, 107,
197 84, 86, 171, 115 75, 80, 170, 70,145 59 114, 72, 187 13
73, : .: 60 F 58, H. 45, 67, 71 143, 48, 147 184, 83,154 M.: 161 50 57, 44, 51, 54, 131 B.: 62 R.: 21 47, 150,151,152,153 34 R.: 142, H.: 160, Z.: A.: 95 oud 167 Z.: 14, A. N.: M.: S.: 4, 146 82 S.: 148 83,154 dr 145 147 R.: M. Z.: .: A.: 63
H.: .: F .: 182 Najafabadi Moghadam J.: F M.A.: S.: P E.: K.: 61 Sar Nejad M. - magham M. Nejad M.: emani S.: 124,146, 181, Khezri ostkar dowsi Dashtbozorgi Dashtbozorgi Dini Ebrahimi- Ebrahimi Ebrahimzadeh Ebraim Emamalizadeh Eshghian Dor Ebrahimi Ezatpanah Fadakar Famili Farajzadeh Farzin- Fatemi 120, Fer Ganjali 173 Garkani Ghahr Ghalei Ghambarian Ghasemi Gharaghani Ghasemi Ghasemi Ghasempour 116, Ghasemi- 135
144,145 183 56,190, 48 A.:82,134, 120 181, 11 140 55, 91 173 47, 142,143 56 H.: 43, 79 104 46 S.: 183 B.: M.: 30, 192 49 K.: 16, A.: 116 157 52 51 172, 42, 31,148 50, 6, M.: M. 141 17 G.: 144 130 N.: 194 40 102, S.: 31 53 M.: 12 168, M.: 14, A.: soltan H.: Garmarudi M.: M.: M.: E.: H.: A.: Sadeghi H.: M.J.: .: - A.: N.: Roochi P nia M.: E.: ehgani M.: G. H.: M.: S. Ashari dbar yadi Asanjarani Asgari Asghari Asni Atabaki A Azadi Azimi Azizi Babaei Bagheri Bagheri Bagheri Bagheri Bagherzadeh Baher Bahram Bakhtiari Bayat Beheshti Benvidi Beyramy Bor Chamsaz Chaichi Chehr Dadfar Dabaghian Damavandi Darabi Daryani Authors Index Authors 33, 138,
137, 32, 134 29, 43 24, 17, 28, 42, : 16, : 155 7, 45 H. 179
M. 143 12, : M 46
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: 44, 48,144 B. : M. 40 48
185,186
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Chamjangali 93,159, dalani Abdollahi Abdollahi 35, Abrishamkar Abolhasani Afkhami Aflaki Agah Aghaei Aghajani Ahari- Ahmadgolami Ahmadi-Azghandi Ahmadi Ahmadi-Roudi Ahmadi Ahmari Akhlaghi Akbari-Adergani Akbari Alizadeh Alizadeh Arab Amani Amini Ar Amiri Asadpour Asadi Asadpour 139 174, 178, 154, 140 18,148, 129, 20 188 169 13, 135, A.: 83, 3,
110 184, 159, N.: 160 134, 104 S.: M.: 161 86 57 162 41 18 160, 17, 30 196 90 81 eh K.: 163 85 120, M.R.: 88 H.: 156 111 .: 84 102, .: 55, Najafi J.: T M.: V N.: 158, M.: A.H.: A.: 120, nosfaderani 97 R.: 19, 41 164 M.: B.: H.: Sh.: M.: ar E.: B.: M.R.: 41 R.: V Z.: S.: A.: O.: di J.: ostami Khayyati Khodaveisi Khoddami Khoshayand Khoshkam Kiaee Kiar Kompany-Zar 175 Konoz Kyani Lari Lashgari Lear Louie Mabhooti Madrakian Mahdavi-Ara Mahmoudzadeh Majidi Malekfar Malekzadeh Mani- Mashkouri Mansouri Mehran 179 Massoudi Mehrara Mehri Milani 134, 93, 118 82, 59, 17, 81 23, 163 M.R.: 20, 60 M.R.: 1, S.: 39 28 177 34
162 A.:
112 40 101 34 80 117 .: 157 109 M.: 122 182 60 25, N.: 196 150,151,152,153 T 79 .: .: S.: .: 147 F 163 78 H.:
194
K.: S.: T 158, M.A.: B.: Nezhad S.: Z.: 90 A.: 97, .: S.Y S.: F M.: Z.: M.:183 .: A.: .: M.S.: F R.: F Jahani 140 Heidari Hokmi Honary Hojati Hejazi Hoggo Hormozi IranifamM.: Izakian Jaberi Imani-Nabiyyi Jalali-Heravi Khajvand Keshavarz Khani Kazemian Khanmohammadi Jalili- Kamyabi Jamehbozorghi Jouyban Jamalian Kalantar Kashanian Jamshidi 135, Khayamian Kariminia Kazemi Karami Kariminia 153, 152, 71 70, 151, 74 69, 126 51 150, S.K.: 191 54, 67 116 14, M.R.: 65 77 112 E.: 72, 72 129 Z.: H.: 157 69 A.: 106 Darzi 195 M.: B.: 64, 76, Z.: 15, 111 H.: 15, 66, M. 117 73 68 68 125 75 N.: 156 M.: 77 103, 85 B.:
16, 195 176, A.: N.: 8 M.: 122 B.: .: .: .: F 30 K.: H.: Nejad- .: .: A.S.: F T A.: B.: A.: F M.: F M.: S.: 176, Shabani Ghiasi Gholivand Ghorashi Ghorbani Ghorbani-Kolhor Ghorbannezhad Ghorbanzad´e Ghowsi Ghowsi Gobadian Golabi Golchoubian Goliaei Golmohammadi Goodarzi Gorji Goudarzi 155, Hadiloo Hadjmohammadi Haji Hajinia Hajilari Hamidi Hamidi Hasani Hashemi Hassani Hassanzadeh Heidari Heidari 198 POSTER POSTER 180 199 113, 65, 178 64, 74 127 61, 57,190 161, A.: 141 174 107 124 62 27, 21 .: 52, 109 56, P 175 .: 119 P N.: 131, E.: 190 99 H.: 35 41 122 d 182 31 197 M.: 118 Sh.: 97, 55, .: 122,144 92 123 F
121 33 155 S.: 83, .: 131 B.: Far M.: F H.: S.: S.M.: E.: M.: M.: N.: B.: Solari nasab S.M.: A.H.M.: N.: A.: H.: M.R.: A.: R. Sh.: eshti Saadati Sabzi Sadeghi sadeghi Safavi Salavati Saligheh sadeghi Sajjadi Sajjadifar Salamati Salimi Salimpour Saleh Samadi Samnejad Samadi-Maybodi Sarlak Sangi Sarrafi Sehatnia Seidi Shamsipur Ser Shafiee Shahbazikhah Shaker Shamseddin Shams ShamsdinS.E.: 100, 108, 173 173 99, 172, 107, 172, 98, 171, 106, 171, 97, 115 170, 106 105, 171 170, 168 96, 169 30 114, .: 156, 123 A.: F 103, 95, 170, 143, 76 104, 111 N.: H.: 112 189 42 77 R.: 147 150,151,152,153 193 65 E.: 143, .: 94, Sh.: H.: 23 24 Jadid Z.: 112 F 116 H.: 32 M.: 142, 103, H.: G.H.: 165, 113 A.M.: M.: 4, .: 98 154 .: eganeh d F H.: A.: H.: P 142, .: Y .: M.R.: .: M.: F F B.: F A.: S.: 102, 110 oozi etedal ooz ouzi ouzi-Pesian chehbaf ouzi Niazi 101, 109, Nikbakht Nikoofar Nimr Nor Nor Nor Parastar Par Peyman Pir Pourbasheer Pour Pourmorad Rabbani Rafatpanah Raofie Rashidi Rashidi-Nodeh Rezaei Rezaei Rezai-Bina Riahi Rohani Rohbakhsh Roshanfekr Rostami Rounaghi 89 191,192,193 92 22, 166 90 76 63 62 M.:
91 M.: 168, 106 167 87 189
R.: M.: 191 M.: 21, 111 89,185,186 30, A.H.: S.: 163 87 H.: 118 105 141 G.R.: 103, 166, 165, 136 128 N.: 22, D.: 26, S.: A.A.: 91 157 88 96, 92, 93 108 A.: M.: 110 A.:
A.R.: Sh.: Sarafi
5, R.: Sh.: M.: 137 M.: Sh.: R.: S.: Robati H.: M.: M.: .: O.L.: A.: fari P Mirzaabdollahi Mirzaabdollahi Mirzaei Miran-Beigi Miran-Beigi Moazeni Mohadesi Mohammadalizade Mohammadhosseini Mohammadian Mohammadnejad Mohammadi Mohammadzadeh Mohammadzadeh Mousavi Moradi Moradi Moradi Mokhtari Motiee Motahar Moradi Moohsen Montazeri Mozaf Naseri Najafi Nazari Nezhadali Nekoei Nemati Nematollahi 183 70,145 58, 182, 132 62 K.: 57, 181, 21, 49 .: 56, E.:184 130 V 94 43, 31, 133 55, 53 A.: J.: 180 .: A.: 79 F G.R.: .: 100 121 131 F 33 96 M.: 50, .: M.R.: Farahani nein Kermanshahi G.: Y S.: B.: Shahabadi K.: A.R.: dast e- ei ei ar atankhah osough amini amini asari egane-faal aghoobi azdanipour ekdeli V V V Y Y Y Y Zandkarimi Zar Zamani Y Y Y Zar Zar Zeinali Zolghar 195,196 197 132 179 177 159
111 139 176, 60, 26 141 .: 129 49 162 .: F 99 T 125 175 138 81 .: 129 127, 29, 178, T S.M.: 181 133 128, 25 J.: 98, 128, 21 K.: 158, 130 M.R.: M.H.: M.H.: M.: 89 190 27, 28, 83, 174, e .: 133 Bonuti A.: F 39 .H.: M.:
95, 126 N.: M.: .:128, T J.: H.: A.: 8 F R.: H.: Hashtjin M.R.: H.: .: M.S.: S.: chi S.: V S.: A.A.: ouraddin allipour abaraki adayon ajali alebi ashkhourian avakkoli avakoli avakoli avallali ehrani eymouri Shariati-Rad Shariati-Rad Shariatmanesh Shariatmanesh Shariatpanahi Sharifi Sharifi Shayanfar Shayeste Sheibani Shekar Shemirani Shishehbor Soheyli-Azaz Sohrabi Soldouzi Soleymani Soleimanian Soltani Sor T T T T T T T T T T T V 200 POSTER POSTER 201 202 POSTER