MIAMI UNIVERSITY The Graduate School
Certificate for Approving the Dissertation
We hereby approve the Dissertation
of
Samar Hassan Elagamy
Candidate for the Degree
Doctor of Philosophy
______André J. Sommer, Advisor
______Neil Danielson, Chair
______Hang Ren, Reader
______Carole Dabney-Smith, Reader
______John Rakovan, Graduate School Representative
ABSTRACT
ADVANCING ATR-FTIR IMAGING INTO THE REALM OR QUANTITATIVE ANALYSIS
by
Samar H. Elagamy
This dissertation encompasses research focused on application of attenuated total internal reflection (ATR) infrared spectroscopy and imaging approaches in quantitative analysis of kidney stone components and pharmaceuticals. Chapter 1 provides a background on the attenuated total internal reflection (ATR) and other infrared microspectroscopic techniques including the factors affecting quantitative analysis using infrared microspectroscopy. It also describes the chemometric methods used for quantitative analysis. Chapter 2 presents chemical synthesis and characterization of some closely related components of kidney stones including calcium oxalate dihydrate COD, hydroxylapatite HAP, and octacalcium phosphate OCP. The described chemical synthesis methods in this chapter were simple, reproducible, and provided these components into pure form so that high quality spectra can be obtained. Chapter 3 describes the effect of sample preparation on infrared microspectroscopic analysis of renal stones The sample prepration methods in this work involve polishing the surface of cross-sectioned renal calculi using sanding discs and lapping films. This study demonstrated that polishing of kidney stones using lapping films result in greater increase in the reflectance intensity and improvement of photometric accuracy of reflectance microspectroscopy technique compared to sanding discs. Chapter 4 investigates the use of attenuated total internal reflection (ATR) with PCR models for quantitative analysis of mixed renal stones. The results of this study show that the constructed PCR models can be potentially used for quantitative analysis of stone components. Chapter 5 presents a study of the spatial resolution and detection limits of attenuated total internal reflection (ATR) infrared microspectroscopy using polymer laminates and polymeric microspheres. The results of this work prove that ATR microspectroscopy is capable of detection of structures with dimensions below the theoretical diffraction limit. It is also demonstrated that Principal component analysis (PCA) can enhance the image contrast allowing better visualization of these structures. Additionally, this chapter studies the feasibility of quantitative analysis using GRAMS/ AI and PCR models. Chapter 6 investigates the application of ATR imaging for quantitative analysis of pharmaceuticals in their dosage forms based on image analysis using PCA and Image J software. The results demonstrate the capability of ATR-FTIR imaging of obtaining information about the relative concentration of active ingredients in drug formulations.
ADVANCING ATR-FTIR IMAGING INTO THE REALM OR QUANTITATIVE ANALYSIS
A DISSERTATION
Presented to the Faculty of
Miami University in partial
fulfillment of the requirements
for the degree of
Doctor of Philosophy
Department of Chemistry and Biochemistry
by
Samar H. Elagamy
The Graduate School Miami University Oxford, Ohio
2019
Dissertation Director: André J. Sommer
©
Samar Hassan Elagamy
2019
TABLE OF CONTENTS
Chapter 1……………………………………………………………………………... 1 Introduction………………………………………………………………………….. 2 1.1 Kidney Stone Disease…………………………………………………………… 2 1.2 Infrared spectroscopy and sampling techniques…………………………………. 4 1.3 Infrared Microspectroscopy……………………………………………………… 6 1.4. Factors affecting the use of infrared microspectroscopy for quantitative analysis………………………………………………………………………………. 7 1.4.1 Diffraction……………………………………………………………………… 7 1.5 Chemometrics for quantitative analysis………………………………………….. 10 1.5.1 Beer’s Law………………………………………………………………… 10 1.5.2 Linear Least square regression LLSR………………………………………….. 10 1.5.3 Classical Least Squares (CLS) (k matrix)……………………………………… 11 1.5.4 Inverse Least-Squares Method ILS (P matrix)…………………………………. 12 1.5.5 Partial least square PLS and principal component regression PCR……………. 13 1.6. Dissertation Goals……………………………………………………………….. 15 Chapter 2: chemical synthesis and characterization of some components of kidney stones………………………………………………………………………………….. 20 2.1 Abstract…………………………………………………………………………… 21 2.2 Introduction………………………………………………………………………. 21 2.2.1 Previous research……………………………………………………………….. 23 2.3 Experimental……………………………………………………………………… 25 2.3.1 Materials………………………………………………………………………... 25 2.3.2 Synthesis of calcium oxalate dihydrate COD…………………………………... 25 2.3.3 Synthesis of Hydroxylapatite…………………………………………………... 25 2.3.4 Synthesis of Octacalcium phosphate…………………………………………… 25 2.3.5. Instrumentation………………………………………………………………. 26 2.4 Results and Discussion…………………………………………………………… 26 2.5 Conclusion………………………………………………………………………... 35 Chapter 3: Effect of sample preparation on kidney stone analysis…………………… 39 3.1 Abstract…………………………………………………………………………… 40 3.2 Introduction………………………………………………………………………. 40 3.3 Experimental……………………………………………………………………… 43 3.3.1 Materials and methods………………………………………………………….. 43 3.4 Results and Discussion…………………………………………………………… 44 3.4.1 Optimization of sample preparation procedures……………………………….. 49 3.5 Conclusion………………………………………………………………………... 55 Chapter 4: Application of Attenuated total internal reflection ATR-FTIR spectroscopy in quantitative analysis of kidney stones……………………………….. 58 4.1 Abstract…………………………………………………………………………… 59 4.2 Introduction……………………………………………………………………….. 59 4.3 Experimental……………………………………………………………………… 62 4.3.1 Materials and methods…………………………………………………………. 62
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4.4 Results and Discussion…………………………………...... 63 4.4.1 Application of PCR model into quantitative analysis of renal stone components…………………………………………………………...... 69 4.5 Conclusion…………………………………………………………...... 71 Chapter 5: Differentiating Spatial Resolution and Detection Limits in Molecular (infrared) Microspectroscopy……………………………………………………….. 75 5.1 Abstract………………………………………………………………………… 76 5.2 Introduction……………………………………………………………………… 76 5.2.1 Factors affecting the spatial resolution of infrared microspectroscopy……….. 77 5.2.1.1 Diffraction…………………………………………………………………… 77 5.2.1.2 Spectral resolution…………………………………………………………… 78 5.2.2 Measuring ATR resolution……………………………………………………. 79 5.2.3 Determination of detection limit of ATR technique…………………………... 79 5.3 Experimental…………………………………………………………………….. 81 5.3.1 Materials and methods………………………………………………………… 81 5.3.1.1. Preparation of polymer laminates…………………………………………... 81 5.3.1.2 Preparation of polymer microspheres……………………………………….. 81 5.3.1.3 Preparation of ester mixtures………………………………………………… 81 5.3.2. Instrumentation……………………………………………………………… 82 5. 4 Results and discussion…………………………………………………………... 82 5.4.1 Differentiation of spatial Resolution and detection Limits……………………. 82 5.4.1.1 ATR-FTIR Imaging of polymer laminates………………………………….. 83 5.4.1.2 ATR-FTIR Imaging of submicron particles………………………………… 86 5.4.2 Application of quantitative analysis…………………………………………… 87 5.5 Conclusion…………………………………………………………...... 98 Chapter 6: Application of ATR imaging in quantitative analysis of pharmaceuticals in their dosage form…………………………………………………………………. 102 6.1 Abstract………………………………………………………………………….. 103 6.2 Introduction…………………………………………………………...... 103 6.2.1 Application of ATR technique in pharmaceutical analysis……………………. 104 6.2.2 Image analysis using PCA and Image J……………………………………….. 106 6.3 Experimental…………………………………………………………...... 106 6.3.1 Materials and methods………………………………………………………… 106 6.3.2Instrumentation…………………………………………………………………. 107 6.4 Results and Discussion………………………………………………………… 107 6.5 Conclusion…………………………………………………………...... 115 Appendix A………………………………………………………………………….. 119 Chapter 7: Conclusion……………………………………………………………...... 120 7.1 Summary ………………………………………………………………………... 121 7.2 Future work……………………………………………………………………… 122
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LIST OF TABLES
Table 4.1: IR bands, limits of integration, figures of merit for the LLSR calibration curves……………………………………………………………………………...... 64 Table 4.2: Figures of merit for constructed PCR models…………………………… 66 Table 3.4: Prediction test of COM/CaCO3PCR model……………………………… 68 Table 4.4: Prediction test of HAP/COM PCR model………………………………... 68 Table 4.5: Comparison of the analysis of two mixed stone components using HAP/COM PCR model and Micro CT……………………………………………… 70 Table 5.1: Peak parameters of DES and DBP for Log normal GRAMS/AI model…. 91 Table 5.2: Fit statistics of Log normal GRAMS/AI model of DES/ DBP mixture of ratio (50:50 v %)…………………………………………………………………….. 91 Table 5.3: Peak parameters of DES and DBP for Gaussian GRAMS/AI model…… 94 Table 5.4: Fit statistics of Gaussian GRAMS/AI model of DES/ DBP mixture of ratio (50:50 v %)……………………………………………………………………. 94 Table 5.5: Comparison of prediction accuracy of GRAMS/AI and PCR models for assessment of concentration of different mixtures of DES/ DBP mixtures……………………………………………………………………………… 95 Table 5.6: Peak parameters of PET and PVA for Log normal GRAMS/AI model…. 96 Table 5.7: Fit statistics of Log normal GRAMS/AI model of PET/ PVA………...... 97 Table 6.1: Particle size of the active ingredients……………………………………. 109 Table 6.2: The calculated concentrations of active ingredients of Mucinex using PCA and Image J……………………………………………………………………. 111 Table6.3: The calculated concentrations of active ingredients of Excedrin using PCA and Image J……………………………………………………………………. 114
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LIST OF FIGURES
Figure 1.1: Different morphological forms of urinary stones……………………… 3 Figure 1.2: Infrared sampling techniques…………………………………………... 4 Figure1.3: On and off axis positions………………………………………………... 7 Figure1.4: Resolution criteria………………………………………………………. 9 Figure2.1: Chemical structure of a) calcium oxalate monohydrate COM and b) calcium oxalate dihydrate COD……………………………………………………. 22 Figure 2.2: Chemical structure of a) Octacalcium phosphate OCP and b) Hydroxylapatite HAP………………………………………………………………. 23 Figure 2.3: ATR spectrum of mixed phases of calcium oxalate……………………. 27 Figure2.4: Optical image of Calcium oxalate dihydrate COD crystals…………….. 28 Figure 2.5: X ray diffraction pattern of obtained Calcium oxalate dihydrate COD .. 28 Figure 2.6: ATR spectra of a) Calcium oxalate monohydrate COM and b) Calcium oxalate dihydrate COD……………………………………………………. 29 Figure 2.7: Raman spectra of a) Calcium oxalate monohydrate COM and b) Calcium oxalate dihydrate COD……………………………………………………. 29 Figure 2.8: ATR spectrum of DCPD /OCP mixture……………………………….. 30 Figure 2.9: Xray diffraction pattern of Hydroxylapatite HAP ……………………. 31 Figure 2.10: Xray diffraction pattern of Octacalcium phosphate OCP……………. 32 Figure 2.11: ATR spectra of a) Hydroxylapatite HAP and b) Octacalcium phosphate OCP ………………………………………………………………. 33 Figure 2.12: Raman spectra of a) Hydroxylapatite HAP and b) Octacalcium phosphate OCP ……………………………………………………………… 34 Figure 3.1: Diagram showing the utilized fixture for polishing the stone surface…. 43 Figure 3.2: Visible images of polished kidney stones using sanding discs of grit size a) 120 , b) 320 ,and c) 600……………………………………………………... 45 Figure 3.3: Reflectance spectra of polished uric acid stone using sanding discs of grit size a) 120, b) 320, and c) 600…………………………………………………. 45 Figure 3.4: The reflectance spectrum of uric acid kidney stone (top) ,the K-K transformation (middle),and a reference spectrum of uric acid…………………….. 46 Figure 3.5: Visible images of COM containing kidney stone polished using lapping films (Left) and sanding discs (Right)……………………………………... 47 Figure 3.6: Visible images of uric acid containing kidney stone polished using lapping films(Left) and sanding discs (Right)……………………………………… 47 Figure 3.7: Reflectance spectra of COM containing kidney stone polished using a) lapping films and b) sanding discs………………………………………………….. 48 Figure 3.8: Reflectance spectra of uric acid containing kidney stone polished using a) lapping films and b) sanding discs ……………………………………………… 48 Figure 3.9: A photograph and diagram of the polishing device…………………… 49 Figure 3.10: ATR image (Left) and Reflectance image (Right) of the same region of calcium oxalate stone cross-section……………………………………………. 51 Figure 3.11: The extracted ATR spectra from yellow (top) and pink regions (bottom)…………………………………………………………………………….. 51
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Figure 3.12: The extracted reflectance spectra from red (top) and pink regions (bottom)…………………………………………………………………………….. 52 Figure 3.13: ATR image (Left) and Reflectance image (Right) of the same region of uric acid stone cross-section…………………………………………………….. 53 Figure 3.14: The extracted ATR spectra of uric acid (top) and COM (bottom)….. 53 Figure 3.15: The extracted reflectance spectra of uric acid (top) and COM 54 overlapped with uric acid (bottom). Figure 4.1: ATR spectra of a) HAP, b) COM, and c) CaCO3……………………… 63 Figure 4.2: Calibration curves using LLSR………………………………………… 65 Figure 4.3: Size distribution of particles of a) COM and b) HAP………………... 66 Figure 4.4: ATR images of kidney stone #130692 (Left) and kidney stone# 129182 (Right).…………………………………………………………………….. 69 Figure 4. 5: The extracted averaged ATR spectra from kidney stone #130692…… 70 Figure 4.6: The extracted averaged ATR spectra from kidney stone# 129182…….. 70 Figure 5.1: ATR Sampling Volume………………………………………………… 80 Figure 5.2. Optical image of the cross-section (left) of Nylon/PVA laminate with 1.2 micrometer PVA layer, ATR-infrared image (right)…………………………… 84 Figure 5.3: Infrared spectra of Nylon (Top) and PVA (Bottom……………………. 85 Figure 5.4. Optical image of the cross-section of Nylon/PVA laminate with 0.8 micrometer PVA layer (left), ATR-infrared image (right)…………………………. 85 Figure 5.5. Optical image of the cross-section of PET/PVA laminate with 1.6 micrometer PVA layer (left), ATR-infrared image (right)…………………………. 85 Figure 5.6. Optical image of the cross-section of PET/PVA laminate with 0.8 micrometer PVA layer (left) and ATR-infrared image (right)……………………... 86 Figure 5.7: Infrared spectra of PET (Top) and PVA (Bottom)……………………... 86 Figure 5.8. Optical image (left), PCA image (middle) and regression image (right) of 1 micrometer PMMA microspheres in PVOH…………………………………... 87 Figure 5.9. Infrared spectra of PVOH (bottom), PMMA (middle) and 1 micrometer PMMA microsphere extracted from the infrared image above (top)….. 88 Figure 5.10. ATR spectra of Dibutyl phthalate DBP (top) and Diethyl sebacate DES (bottom)……………………………………………………………………….. 89 Figure 5.11: The carbonyl absorption of Dibutyl phthalate DBP (top), Diethyl sebacate DES (middle), and their mixture (bottom)……………………………… 89 Figure 5.12 : The output of band fitting of 1727cm-1 band of DES/ DBP mixture of ratio (50:50 v %)…………………………………………………………………. 92 Figure 5.13. Transmission spectra of Dibutyl phthalate DBP (top) and Diethyl sebacate DES (bottom)……………………………………………………………... 93 Figure 5.14: The carbonyl absorption of Dibutyl phthalate DBP (top), Diethyl sebacate DES (middle), and their mixture (bottom)……………………………… 93 Figure 5.15 : The output of band fitting of 1734cm-1 band of DES/ DBP mixture of ratio (50:50 v %)…………………………………………………………………. 94 Figure 5.16: The carbonyl absorption of PET (top), PVA (middle), and the PVA laminate layer of thickness 0.8 micrometers (bottom)……………………………... 96 Figure 5.17: The output of band fitting of 1723 cm-1 band of PET /PVA……….. 97 Figure 6.1: ATR spectra of a) Dextromethorphan, b) Guaifenesin………………… 108 Figure 6.2: ATR spectra of a) Acetaminophen, b) Aspirin, and c) Caffeine……….. 108
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Figure 6.3: ATR image of Mucinex tablet cross section and corresponding spectra of each score………………………………………………………………………... 110 Figure 6.4 : ATR image of Excedrin tablet cross section showing Aspirin (red) and caffeine (green)…………………………………………………………………… 112 Figure 6.5 : ATR image of Excedrin tablet cross section showing Acetaminophen (blue) and Aspirin (red)…………………………………………………………….. 112 Figure 6.6 : ATR image of Excedrin tablet cross section and corresponding spectra 113
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DEDICATION
To my lovely Family
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ACKNOWLEDGEMENTS
First, I would like to thank God Almighty for giving me the strength, ability and opportunity to achieve these academic accomplishments. I also would like to express my special appreciation and thanks to my research advisor Dr. Andy Sommer for giving me the opportunity to study at Miami university, guidance, direction, and continuous support throughout my graduate studies. Also, I would like to thank my committee members for their assistance and valuable comments that improved my academic research and contents of this dissertation. I would also like to thank Dr. James C. Williams of the Indiana University School of Medicine, (Department of Anatomy and Cell Biology) for providing our lab with the kidney stone samples, for his help in the interpretation of the results, and justification of our work from clinical perspectives.
I am also grateful to my colleagues Thaisa Wrigth, Nethmi De Alwis, Nuwantika Kumarage, Catherine Amoateng, and Jenny DeJesus for their friendship, help and cooperation. I am also thankful to my colleagues in the department of analytical chemistry, Tanta university, Egypt for their assistance and support.
My special thanks and love to my mom. Words cannot express how grateful I am for her encouragement and the many years of support during my academic studies: Your prayers for me were what made me persistent through all the difficulties in my life. I also would like to thank my sister Sally and my brother Mohamed: Thanks for always helping and supporting me. Finally, my thanks and deepest gratitude to my loving and supportive husband, Ahmed Aly: Your encouragement when the times got rough is much appreciated.
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Chapter 1
1
Introduction
1.1 Kidney Stone Disease
Kidney stone disease (urolithiasis) is a recurrent disease where insoluble mineral aggregations form and reside in the kidney. Over the last ten years, kidney stone disease has been diagnosed increasingly, and today, it affects approximately 10 % of the western countries population. Dietary habits such as high consumption of carbonated beverages and high protein and salt intake are the major causes of an increased incidence of renal stone formation however, the exact biological mechanism of urinary stone (renal calculi) formation is still not well understood.1,2 The proposed first stage in renal calculi formation is the presence of renal tissue containing finely dispersed minerals. The second stage involves development of mineral deposits in interstitial tissues, causing tissue damage and nucleation points for larger stone growth. The final stage is the growth of large renal stones that require treatment which varies depending on the location of stones in the renal system.3 In order to better understand the etiology of the disease , one must study the mechanisms at the onset of stone formation rather than studying the stone after removal.
Urinary stones have different mineral contents and morphologies. They are composed of multiple organic and inorganic constituents.The majority of stones are calcium based. Calcium oxalate stones account for 70%, either in monohydrate or dihydrate form, while calcium phosphate stones represent 5-20%, either in apatite or brushite form. In idiopathic calcium stone formers, the stone is comprised of a mixture of hydroxylapatite, calcium oxalate and protein.Uric acid stones are also common accounting for 10%-20% of all stones. Struvite stones (infection stones), that are composed of magnesium ammonium phosphate, occur in only 2 % of all stones . Cystine stones are rare and result from specific genetic defects (cystinuria).4 Figure1.1 shows visual images of some common kidney stones of various shapes and sizes.
2
Figure 1.1 : Different morphological forms of urinary stones.
Adapted from Michel Daudon, Arnaud Dessom, et al., Comprehensive morpho- constitutional analysis of urinary stones improves etiological diagnosis and therapeutic strategy of nephrolithiasis, Comptes Rendus Chimie,19, 11–12, (2016). (Permission granted)
The treatment of urolithiasis depends on stone composition and morphology however the stone composition is still misdiagnosed around the world due to inaccurate analysis in clinical laboratories.5,6 The current analysis of kidney stones is performed using X-ray diffraction (XRD) and spectroscopic methods such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. Micro computed tomography (MCT) and scanning electron microscopy (SEM) with energy dispersive X-ray are also useful to study the stone morphology and composition. However most of these techniques are useful for only qualitative analysis.7 Therefore, it is important to establish a reliable method for quantitative analysis of kidney stones since determining what components exist and what their concentrations are at the very onset of stone formation, help to better determine the causative factors of the disease and ultimately find a cure for the disease. In an effort to obtain an accurate stone analysis , the research reported in this dissertation will investigate the use of infrared microspectroscopy for both qualitative and quantitative analysis of stone components.
3
1.2 Infrared spectroscopy and sampling techniques
Infrared spectroscopy has long been used as a means for molecular characterization and structure identification of a wide variety of materials. It is an absorption technique that depends on quantized vibrational modes of a molecule. Absorption bands in the mid-infrared region (2.5 to 25 µm) arise from fundamental absorption bands and provide molecular fingerprint of the material being studied.8-9 There are three main infrared sampling techniques available and each technique has benefits and limitations depending on the sample to be analyzed.10 Figure 1.2 is a schematic showing the three types of infrared sampling approaches. The blue blocks in the figure represent the sample to be analyzed.
Figure 1.2: Infrared sampling techniques.
In transmission mode, the sample is analyzed by transmitting the infrared radiation directly through the sample. The light absorbed is determined by the sample thickness and typically this thickness must be on the order of ~6 µm. The solid samples are diluted with infrared inactive materials such as KBr, KCl and compressed into pellet for analysis.11 For tissue samples, thin sections are mounted on IR-transparent substrate 12 like KBr, KCl, or BaF2. The major drawbacks of this approach are that these substrates are expensive and the method is inconvenient for quantitative analysis since it requires expertise and tedious sample preparation steps to obtain the optimal thickness and integrity. Also, optical effects such scattering, diffraction, and reflection are encountered
4 when investigating biological samples with a transmission approach which are manifested as spectral artifacts leading to photometric inaccuracy. 13-16
In the trans-flection mode, a sufficiently thin slice of sample is mounted on a reflecting substrate like low-e slides which are microslides coated with a reflective material such as Tin Oxide. Infrared light passes through the sample once, reflects off the substrate passing for a second time through the sample. The radiation is attenuated by the sample twice before reaching the detector, thus the sample thickness must be less than 5 μm to maintain photometric accuracy.17-19 This approach also suffers from deleterious optical effects due to the doubled path length which affect both the qualitative and quantitative capacity of the method.
In, Attenuated Total internal Reflectance (ATR) mode, the sample is brought into intimate contact with an internal reflection element (IRE) possessing a high refractive index. When the infrared beam is directed into a medium with high refractive index, it can be totally internally reflected at the hemisphere/sample interface if the angle of reflection at the interface exceeds the “critical angle” θc. This phenomenon is called 8,20 attenuated total internal reflection (ATR). The critical angle θc can be calculated by the ratio of refractive index of the sample (ns) and IRE (nc)
nS sinC …………………………………………………..….Equation 1 nC
This internal reflectance creates an evanescent wave which extends a few micrometers beyond the crystal surface into the sample. Therefore, the sample must be in intimate contact with the IRE. The path length through the sample, which is termed the penetration depth dp depends on the refractive index of the sample and the IRE. The penetration depth is approximately 1 micrometer across the infrared spectrum and can be calculated using the following equation:
dp 1 …………………………………….Equation 2 2 n 2 2 2n sin ( s ) c nc
5
Here λ is the wavelength of the incident radiation, θ is the angle of the incidence, nc and ns are the refractive indices of the IRE and sample, respectively.
The penetration depth is independent of sample thickness which means that highly absorbing samples that can hardly be analyzed with transmission mode can be easily analyzed using ATR with little sample preparation. The reduced path length also overcomes the problem of spectral artifacts observed with transmission and trans-flection mode. Additionally, the method can be potentially used for accurate quantitative analysis due to the controlled optical path length.21-23
1.3 Infrared Microspectroscopy
Infrared microspectroscopic imaging through coupling infrared spectroscopy to a microscope combines the capability of spectroscopy for providing spectral information, with the power of visualization giving insight into molecular spatial distribution.16,24 Attenuated total internal reflection infrared imaging involves the use of a hemispherical IRE which serves as the immersion lens.25,26 The sample is placed in contact with the IRE and the IRE/sample composite is positioned at the microscope’s focus and then imaging is conducted by moving the composite off-axis using a motorized stage. Rays entering the hemisphere normal to the surface are not refracted. As the composite is moved to the left or the right , the rays no longer enter the IRE normal to the surface so that they are refracted impinging the sample at different locations.23 Figure 1.3 shows that by moving the composite off-axis, adjacent sampling sites can be addressed. When the microscope is outfitted with an array detector, large area images can be obtained in minutes to hours.
A Linear Array Detector of Perkin Elmer Spotlight microscope incorporating pure Mercury Cadmium Telluride MCT are assembled as 16 gold-wired IR detector elements. The pixel size is 30 x 30 μm on the array which results in pixel size at the sample of 6.25 x 6.25 μm area in reflectance imaging while, for ATR , the pixel size is reduced to 1.56 x 1.56 μm through the 4x immersion effect provided by the germanium crystal. This allows the acquisition of images of much smaller areas than reflectance imaging with better spatial resolution. The stage is moved in steps of 6.25 μm or 1.56 μm
6 in x and/or y direction in a raster pattern. This movement repeated until the full sampling area is completed.
On-axis position
IRE IRE IRE
Sample Sample Sample A B C
Figure1.3: On and off axis positions. First, the hemisphere/sample composite is centered (position B, on axis position). By moving the composite off-axis, the incident radiation no longer enters the IRE normal to the surface and the radiation is refracted and directed to different locations in the sample (A& C positions). The red arrows represent the infrared beam, the dashed line is the microscope’s optical axis, and the solid line represents the line normal to the hemispheres surface.
1.4 Factors affecting the use of infrared microspectroscopy for quantitative analysis
In microanalysis, there are variables that are sample structure related such as refractive index boundaries between particles, particle sizes, and shapes. Each can induce optical effects like scattering, specular reflection, and refraction which could limit effective quantitative analysis.27 Since ATR experiences a decreased penetration depth, the competing optical effects and related spectral artifacts are minimized giving the most photometrically accurate spectra compared to other sampling techniques. Microscope related variables such as diffraction can also determine the spatial resolution and detection limit of the analysis.
1.4.1 Diffraction
The spatial resolution of any microscope is defined as the minimum distance between two objects in order for them to be resolved without significant contamination
7 from its adjacent area.28 The spatial resolution is equal to the radius of the Airy disk “r” based on the Rayleigh criterion which is given as
0.61 r ………………………………………………………….…...Equation 3 n1 sin
Here again λ is the wavelength of light employed, n1 is the refractive index of the medium (n1= 1 when the measurement is conducted in the air as in transmission and reflectance modes), θ is the half angle acceptance of the objective. The typical value for sin is 0.6 for most conventional infrared microscopes therefore, the spatial resolution is calculated as λ for transmission while in reflectance and ATR modes, one half of the objective is used to direct the radiation to the sample and the other half is employed to collect the radiation, thus sin will be 0.3. Therefore, the spatial resolution is calculated as 2 λ in reflectance mode. For the ATR mode, a germanium (n = 4.0) immersion lens is employed yielding a spatial resolution of 0.5λ. Figure 1.4 illustrates the criteria for resolution. The two objects start to be resolved if they are separated by “r” (Rayleigh resolution). When d0.6 λ /sin θ, baseline resolution occurs assuming that the two source have the same intensity, however spectral contamination occurs when one source has higher extinction coefficient than the other which is highly noticeable in real samples.16,29
It is also believed that the Rayleigh criterion is an absolute value and structures smaller than this value cannot be detected. However, detection of structure below the Rayleigh criterion is possible because the source for the signal in infrared microspectroscopy, depends mainly on the absorption of light which is directly proportional to the path length of light and the extinction coefficient of the sample. In ATR measurements, the extinction coefficient determines the signal since the path length of the light through the sample is constant at a given wavelength. In the case of structures smaller than the path length, the absorption coefficient will determine the signal (vide infra), since in this case the measurement becomes a volume effect.
8
Figure1.4: Resolution criteria.
[A.J. Sommer, “Mid-infrared Transmission Microspectroscopy”, Handbook of Vibrational Spectroscopy, J.M. Chalmers and P.R. Griffiths, Ed. (Wiley, NY), p. 1371, (2002).] (Permission granted)
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1.5 Chemometrics for quantitative analysis
Chemometrics is a branch of science that derives data from measurements made on a chemical process or system through the application of mathematical and statistical methods, for the extraction of useful information for quantitative analysis.30,31 Some methods have the characteristic of being simple to understand and implement such as Linear Least square regression LLSR, Classical Least Squares (K-Matrix),and Inverse Least Squares (P-Matrix). However these simple programs may not be suitable for all possible samples. Other methods such as Partial Least Squares (PLS) and Principal Components Regression (PCR), are complex but they can handle a large variety of multicomponent samples. The following section describes some mathematics involved in these different statistical methods and their proper applications for a given analytical problem.
1.5.1 Beer’s Law
Beer’s Law is one of the most commonly applied chemometrics for quantitative analysis in any scientific field which depends on the proportionality between sample absorbance and its concentration. This method has been widely used for spectral analysis in ultraviolet, visible, and infrared regions.32 The absorbance of monochromatic radiation “A” is directly proportional to the path length “b” and the concentration “c “of the absorbing species, as shown in Equation 4
A = ε b c………………………………………………………………………...Equation 4
Where ε is extinction coefficient.
1.5.2 Linear Least square regression LLSR
Quantitation for spectroscopic analysis can be done using various mathematical techniques based on Beer’s Law. LLSR is the simplest of these methods which is based on plotting a simple regression line of absorbance versus concentration through direct measurement of band height or area of spectral bands of a set of standard samples of known concentrations. Once the calibration equations have been derived from the curve, they can be used to calculate the concentration of unknown analytes of the same
10 composition as the standards. For accurate measurements, the unknown analytes must be measured under exactly the same experimental conditions. In this case, concentrations of the components are calculated from the following equation:
Area b C ………………………………….…………………………Equation 5 a
Where “C” is the concentration of component, “a” is the slope of the calibration curve, and “b” is the intercept. By measuring the area of spectral band of the component, it is possible to determine the concentration using the equation and these coefficients.
1.5.3 Classical Least Squares (CLS) (k matrix)
This quantitation model is a full-spectrum method based on Beer’s Law. It assumes that Beer’s law model with the absorbance at each wavelength is proportional to the component concentrations.33 The model error is added to the equation to compensate for errors due to noise, instrument error, sample handling error, and many other possible variations so Beer’s Law in CLS is given by equation:
A=KC + E ……………………………………………………………….……..Equation 6
Where “K” is the path length and extinction coefficient combined into a single constant. “E” is the residual error between the least squares fit line and the actual absorbance values. The concentration of unknown samples is predicted by measuring the absorbance values of a series of different concentrations and determination of the slope of the best fit line through all the data points. For samples containing two constituents, it is necessary to set up two equations:
Aa,1 Ca + EEquation 7
b2 Cb+EEquation 8
Here A and are the absorbances at two different wavelengths, Ca and Cb are the concentrations of the two components (“a” and “b”) in the mixtures, a,1 and b2 are the extincition coeffocients for the two components at those wavelengths. Each equation is solved independently provided that the bands of the constituent are well resolved. In
11 case of spectral overlapping of two components, the absorbance at a single wavelength can be calculated as an additive function of the two constituent concentrations and their concentration can be calculated by solving the following equations simultaneously:
Aa,1 Ca + b1 Cb + EEquation 9
a,2 Ca + b2 Cb+ EEquation 10
However, solving simultaneous equations in case of more than 2 constituents or more than two wavelengths is tedious and complex process. An efficient way to solve such equations is matrix mathematics which can be done using software programs. The previous equations can be written as:
A K k C E 1 a,1 b, 2 a 1 ………………………...... Equation 11 A 2 ka, 2 kb, 2 Cb E 2 or, more simply: A=KC+ E……………………………………………...…..Equation 12
In this case, “A” represents a (2 x 1) matrix of absorbance at the two selected wavelengths, “K” is a (2 x 2) matrix of the absorptivity constants, “C” is a (2 x 1) matrix of the concentrations of the two constituents, and “E” is the (2 x 1) matrix of absorbance error.
A major disadvantage of CLS model is that the absorbance at certain wavelength is determined from the sum of all the component concentrations multiplied by their extinction coefficients thus, if the concentration of any component in the sample is omitted, the predicted absorbance will be inaccurate. Also, if the mixture is complex, or there are possible contaminants in the unknown samples that were not present in the calibration mixtures, this could limit the quantitative analysis.34-36
1.5.4 Inverse Least-Squares Method ILS (P matrix)
This method assumes that concentration is a function of absorbance at a series of given wavelengths which is completely different from Classical Least Squares where
12 absorbance at a single wavelength is measured as an additive function of the constituent concentrations.37,38 The inverse Beer’s law model is given by the equation:
C=PA+E………………………………………………………………….…Equation 13
Where “P” is the path length and extinction coefficient combined into a single constant, “E” is a matrix of concentration prediction error. In case of two constituents, the concentration is calculated using the following equations:
CaPa,1 A + Pa2 A2 + Ea…………………………………………….Equation 14
CbPb,1 A + Pb2 A 2 + Eb…………………………………………....Equation 15
Matrix mathematics can be also applied to the model when the analysis involves mixtures containing more than two constituents. The inverse expression of Beer’s law has the significant advantage that even if the concentrations of other constituents in the mixture are not known, the matrix of coefficients (P) can still be determined correctly. One disadvantage of the ILS method is that the wavelength selection can be difficult as it must correspond to the absorbance of the desired constituents. Determination of the absorbance at different wavelengths is also needed for each constituent and measurements at least one different wavelength for each additional constituent in the spectrum are necessary to accurately calibrate the ILS model. In addition, the analysis has to be limited to a small number of wavelengths and this number is limited by the number of calibration samples in the model because the precision and prediction accuracy are degraded when too many wavelengths are included in the analysis.39,36 Therefore, the improvements in precision and the full-spectrum advantages of CLS methods are not attainable with the inverse method.
1.5.5 Partial least square PLS and principal component regression PCR
The PLS and PCR algorithms are quantitative spectral decomposition techniques. They have same common features but, in PLS, the spectral decomposition and concentration prediction are slightly different. It should be noted that PCR is simply principal component analysis (PCA) followed by a regression step.40 Both PLS and PCR combine the advantages of CLS and ILS methods. They retain the full-spectrum
13 capabilities of CLS by forming a new coordinate system consisting of full-spectrum basis vectors. The advantage of ILS in performing the analysis of one chemical constituent at a time is also retained while avoiding the wavelength selection problems.
The calibration spectra for both PCR and PLS models are represented as follows
A = TB + EA ……………………………………………………………...…...Equation 16
Where “B” is the matrix of the new PLS or PCR basis set of full-spectrum vectors (loading vectors or loading spectra). “T” is the matrix of intensities (or scores) in the new coordinate system of the PLS or PCR loading vectors for the sample spectra. “EA” is the matrix of spectral residuals that are not fit by the optimal PLS or PCR model.
There is an analogy between equation 12 for CLS and the equation 16 for PLS or PCR since both equations involve the decomposition of A into the product of two smaller matrices. However, the intensities in the new coordinate system of PLS and PCR are not the concentrations as in CLS, but they are modeled and correlated to concentrations. The intensities of each of the loading vectors which are needed to construct each calibration spectrum are the scores. PLS and PCR algorithms compress the number of intensities of each spectrum in the spectral matrix A to a smaller number of intensities in the new coordinate system of the loading vectors.
The spectral intensities (T) in the new coordinate system are related to concentrations with a separate inverse least-squares analysis using the equation
c = Tv + ec ……………………………………………………………...Equation 17
Where “v” is the coefficients relating the scores to the concentrations and “T” is the matrix of scores from the PLS or PCR spectral decomposition in Equation 16.
The methods of spectral decomposition and concentration prediction are different in PLS and PCR. Both methods involve stepwise algorithms decomposing the spectral matrix into a set of vectors and scores. PCR is a two-step process; the loading vectors and scores are generated and then the scores are related to the constituent concentrations using a separate regression method. The full-spectrum loading vectors are extracted from
14 the spectra into the order of their contribution to the variance in the calibration spectra. After the determination of the first loading vector, it is removed from the raw data, and the process is repeated until the desired number of vectors has been generated. 41-43The potential problem with PCR is that the loading vectors are calculated independently of any knowledge of the constituent concentrations thus they may merely represent the most common spectral variations in the spectral data and may not be optimal for concentration prediction.
On the other hand, PLS involves one step process and does not involve separate regression step. It performs decomposition on both the spectral and concentration data simultaneously. Concentration-dependent loading vectors are calculated and the computed scores are then related to the concentrations after each loading vector is calculated. 44-47Therefore, the resulting loading vectors are directly related to the concentration of the constituents of interest but the model equations involved in PLS are significantly more complex compared with PCR.
1.6 Dissertation goals
The aim of dissertation is to investigate the capability of attenuated total internal reflection (ATR) technique for quantitative analysis of real samples. The starting point of this research is application of ATR for quantitation of the components of mixed renal stones. This work will determine if the method could provide a wealth of quantitative information important to kidney disease diagnosis. Also, the method will be applied for quantitative analysis of pharmaceuticals in their dosage forms. The objectives and specific aims of each chapter are:
Chapter 2 describes chemical synthesis methods of some components of kidney stones including calcium oxalate dihydrate COD, hydroxylapatite HAP, and octacalcium phosphate OCP. The described methods were optimized for the synthesis of pure phases. They are considered to be easier and more reproducible compared to those methods reported in the literature. The obtained spectra will be useful for accurate determination of stone components and construction of PCR models for future quantitative analysis.
15
Chapter 3 demonstrates the effect of sample preparation on infrared microspectroscopic analysis of renal stones by polishing their cross sections using sanding discs and lapping films. This study has demonstrated that polishing kidney stones using lapping films result in greater increase in the reflectance intensity and improvement of photometric accuracy of reflection microspectroscopy technique compared to sanding discs. This work will also demonstrate the capability of infrared microspectroscopy for studying the stone microstructure.
Chapter 4 investigates utilization of attenuated total internal reflection (ATR) in conjunction with PCR models for quantitative analysis of mixed renal stones. The constructed PCR models have good linear relationships between the actual and predicted concentrations which make the use of the method for quantitative analysis more promising. The method has been applied on kidney stones having mixed phases and the results will be compared to those obtained by micro computed tomography (Micro-CT) analysis.
Chapter 5 studies the spatial resolution and detection limits of attenuated total internal reflection (ATR) infrared microspectroscopy. It is anticipated that this investigation will provide evidence of the capability of ATR imaging of detection of structures beyond the theoretical diffraction limit including polymer laminates and submicron particles. Additionally, this chapter studied the use of principal component analysis (PCA) for enhancement of imaging contrast. Band fitting was also used to resolve the spectral overlapping that could happen in case of spectra extracted from single pixel for structures or layers of dimensions below the diffraction limit.
Chapter 6 investigates the use of ATR-FTIR imaging for quantitative analysis of pharmaceuticals in their tablet formulations based on image analysis using PCA and Image J software. This chapter represents the first attempt to apply ATR technique for quantitative analysis of active ingredients in pharmaceutical dosage form. The results show that Image J which is an open access software can provide quantitative information regarding the area of color scores composing the ATR image that are comparable to PCA software provided by Perkin Elmer.
16
References:
1. J. Cloutier, L. Villa, O. Traxer, M. Daudon, World J. Urol.; 33(2):157–169, (2015). 2. R. L. Ryall, Urol. Res. 36, 77, (2008). 3. J. C. Anderson, “Quantitative and qualitative investigations into urinarycalculi using infrared microspectroscopy” Miami. Thesis, (2007). 4. M. Daudon, A. Dessombz, V. Frochot, et.al. , C.R. Chim., 19, 11–12, 1470-1491, (2016). 5. A. E. Krambeck, et al., J. Urol, 184, 1543-1549, (2010). 6. G. P. Kasidas, C.T. Samuell, T. B. Weir, Ann. Clin. Biochem., 41, 91-97, (2004). 7. V. K. Singh, P. K. Rai, Biophys. Rev., 6(3-4):291-310, (2014). 8. P. R. Griffiths and J. A. De Haseth, Fourier Transform Infrared Spectrometry (Wiley-Interscience, 2nd ed., (2007). 9. D. A. Skoog, F. J. Holler, and T. A. Nieman, Principles of Instrumental Analysis Chapter 16, (Saunders College Publishing, Philadelphia, (2007). 10. F. M. Mirabella, Modern Techniques in Applied Molecular Spectroscopy (John Wiley & Sons, New York, (1998). 11. P. L. Feist, J. Chem. Ed, 78, 3, 351-352, (2001). 12. M. Diem, K. Papamarkakis, J. Schubert, B. Bird, M. J. Romeo, and M. Miljkovic, Appl. Spectrosc. 63, 307A, (2009). 13. H. J. Gulley-Stahl, S. B. Bledsoe, A. P. Evan, and A. J. Sommer, Appl. Spectrosc. 64, (2010). 14. P. Bassan, H. J. Byrne, F. Bonnier, J. Lee, P. Dumas, and P. Gardner, Analyst, 134, 1586, (2009). 15. M. J. Romeo and M. Diem, Vib. Spectrosc., 38, 115, (2005). 16. A. J. Sommer, Handbook of Vibrational Spectroscopy, J.M. Chalmers and P.R. Griffiths, Ed. (Wiley, NY), p. 1369, (2002). 17. J. Anderson, J. Haas, R. Arays, A. J. Sommer, and A. P. Evan, Microsc. Microanal., 12, 312-313, (2006). 18. J. C. Anderson, J. C. Williams Jr, A. P. Evan, K.W. Condon, A. J. Sommer, Urol Res. 35, 41-48, (2007).
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19. A. E. Evan, J. E. Lingeman, F. L. Coe, N. L. Miller, S. B. Bledsoe, A. J. Sommer, J. C. Williams, Y. Shao, and E. M. Worcester, Kidney Int., 74, 223-229, (2008). 20. A. J. Sommer, L. G. Tisinger, C. Marcott, and G. M. Story, Appl. Spectrosc., 55(3), 252-256, (2001). 21. B. M. Patterson, G. J. Havrilla, Appl. Spectrosc., 60(11):1256-66, (2006). 22. J. Grdadolnik, Acta Chim. Slov., 49, 631−642, (2002). 23. L. L. Lewis and A. J. Sommer, Appl. Spectrosc, 54 (2), 324-330, (2000). 24. J. E. Katon, "Infrared Microspectroscopy," in Modern Techniques in Applied Molecular Spectroscopy, Francis M. Mirabella, eds. (John Wiley & Sons, New York, p. 267, (1998). 25. L. E. Lavalle, A. J. Sommer, G. M. Story, A. E. Dowery & C. Marcott, Microsc. Microanal, 10, 1298–1299, (2004). 26. T. Nakano and S. Kawata, Scanning, 16, 368, (1994). 27. L. G. Tisinger, “Investigations in Quantitative Infrared Using Attenuated Total Reflectance”, Ph.D. Dissertation, Miami University, Oxford, OH, (2002). 28. M. H. Freeman, Optics 10th edition, p. 536 (Butterworths, Boston), (1990) 29. H. J. Gulley-Stahl, Ph.D. Dissertation, an investigation into quantitative ATR-FT- IR imaging and raman microspectroscopy of small mineral inclusions in kidney biopsies Miami University, Oxford, OH, (2010). 30. S. Matero, Chemometrics Methods in Pharmaceutical Tablet Development and Manufacturing Unit Operations, Publications of the University of Eastern Finland Dissertations in Health Sciences, (2010). 31. J. Mocak, Nova Biotechnologica et Chimica, 11, 11–25, (2012). 32. D. C. Harris, Quantitative Chemical Analysis (W.H. Freeman and Co., New York, (2007). 33. D. M. Haaland and R. G. Easterling, Appl. Spectrosc., 36, 665, (1982). 34. D. M. Haaland, R. G. Easterling and D. A. Vopicka, Appl. Spectrosc., 39, 73, (1985). 35. J. M. Harris, N. J. Dovichl, Anal. Chem., 52, 695A-706, (1980). 36. R. E. Synovec, Anal. Chem., 59, 2877-2884, (1987). 37. H. Mark, Anal. Chem., 58, 2814, (1986).
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38. J. Pawliszyn, Anal. Chem., 58, 3207-3215, (1988). 39. K. H. Norris and P. C. Williams, Cereal Chem., 62, 158, (1984). 40. D. R. Bobbitt, E. S. Yeung, Anal. Chem., 57, 271-274, (1985). 41. I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 2nd edition, (2002). 42. P. D. Wentzell, D. T. Andrews, D. C. Hamilton, K. Faber, and B. R. Kowalski, J. Chemom., 11, 4, 339–366, (1997). 43. P. M. Fredericks, J. B. Lee, P.R. Osborn and D. J. Swinkels, Appl. Spectrosc., 39, 303, (1985). 44. A. Hoskuldsson, J. Chemom., 2, 211–228, (1988). 45. M. Barker and W. Rayens, J. Chemom., 17, 3, 166–173, (2003). 46. P. H. Garthwaite, J. Am. Stat. Assoc.., 89, 122– 127, (1994). 47. D. M. Haaland and E. V. Thomas, Anal. Chem., 60, 1193-1202, (1988).
19
Chapter 2
20
Chemical Synthesis and Characterization of Some Components of Kidney Stones
2.1 Abstract
Kidney stone disease (urolithiasis) is a common and recurrent disease that may lead to renal failure. Most renal calcui consist of a mixture of organic and inorganic materials including hydroxylapatite, calcium oxalate, and proteins. The majority of calculi are composed of calcium oxalate monohydrate (COM) and hydroxylapatite (HAP). Other common components of calculi include uric acid, struvite (magnesium ammonium phosphate), calcium oxalate dihydrate (COD), brushite (calcium phosphate), and octacalcium phosphate OCP. The treatment of kidney stones is based on the determination of the exact chemical composition of stones, because different types of stones require different treatment. The aim of this chapter is to develop new methods of synthesis of some components of kidney stones including calcium oxalate dihydrate COD, hydroxylapatite HAP, and octacalcium phosphate OCP to help in accurate qualitative analysis of kidney stones.
2.2 Introduction
Urinary calculi typically consist of more than one type of mineral and even minor stone components can have important clinical significance. Understanding the etiology of the disease and planning patient treatment requires an exact determination of kidney stone composition.1-3 The purpose of this study is to chemically synthesize closely related components of kidney stones and obtain high quality infrared spectra so that these compounds can be better identified using ATR FTIR spectroscopy. These components include calcium oxalate dihydrate COD, hydroxylapatite HAP, and octacalcium phosphate OCP. The presence of these components is related to different medical conditions thus their identification can help in the optimal treatment of patients.
Calcium oxalate crystals represent approximately 86% of all renal calculi.4 They can crystalize into three main forms: monohydrate, dihydrate, and trihydrate form. Only the monohydrate form is thermodynamically stable.5 Only monohydrate and dihydrate forms are observed in renal stones (Figure 2.1). COD stones can undergo dehydration and transform into COM. COD crystals are known as weddellite and they are found in about a
21 fourth of all urinary calculi. 6-7 They grow preferentially in environments where the concentration of calcium exceeds that of oxalate and their prevalence is mainly associated with conditions of hypercalciuria while, COM stone formation results from hyperoxaluria thus it is important to differentiate between the two forms as they have different medical indications.8-10
a b
Figure 2.1: Chemical structure of a) calcium oxalate monohydrate COM and b) calcium oxalate dihydrate COD.
Hydroxylapatite is the most common form of calcium phosphate in kidney stones. It is found in approximately 80% of all stones.11 Octacalcium phosphate is a complex crystalline form that could be a precursor to apatite in many systems. Octacalcium phosphate is an unstable form of calcium phosphate which can convert to hydroxylapatite after a few hours or days. It is found in only about 1% of all calculi, and as a major component in only about one-fifth of those.12 It is also found in about a quarter of apatite 11,13 stones formed during pregnancy. Octacalcium phosphate, OCP, Ca8H2(PO4) 6 .5H2O
, has structural similarities with hydroxylapatite, HAP, Ca10(PO4)6(OH)2, but they are differing in their Ca/P molar ratios (1.33 and 1.67 for OCP and HAP, respectively) (Figure 2.2).14
22 a b
Figure 2.2: Chemical structure of a) Octacalcium phosphate OCP and b) Hydroxylapatite HAP.
2.2.1 Previous research
Different methods for synthesis of calcium oxalate dihydrate have been reported, most of which fail to form pure COD crystals. Lachance et al. has proposed a method for COD production based on hydrolysis of diethyloxalate in presence of calcium chloride solution at pH 6, 4 0C.15 The literature also reveals other methods for synthesis of COD through reaction of calcium chloride with sodium oxalate under various experimental conditions (Martin et al., 16 Wiessener et al.,17 Brown et al., 18 Grases et al., 19 and Doherty et al.20). Based on a survey of these reported methods, the formation of calcium oxalate dihydrate crystals was found to be favored by high calcium to oxalate ratio, low relative supersaturation, high citrate concentration and the presence of phosphate salt. This chapter demonstrates a new, simple, and reproducible method for pure COD formation.
There are several methods have been reported for the synthesis of Hydroxylapatite HAP include precipitation,21-24, biomimetic deposition technique,25 hydrothermal technique,26 electrodeposition,27 multiple emulsion technique28. Precipitation is the most widely used method for HAP synthesis. Bouyer et al. has synthesized HAP through the reaction between Calcium hydroxide [Ca(OH)2] and 21 orthophosphoric acid [H3PO4] .
23
10 Ca(OH)2 + 6 H3PO4 Ca10(PO4)6(OH)2 + 18 H2O………………………….(1)
This reaction was performed in a double-wall beaker and heated by water circulation. Reaction temperature was varied between 25 to 850C. Two other reactions for HAP precipitation have been reported by Santos et al.22 The first reaction was completed using ammonium phosphate [(NH4)2.HPO4] and Calcium hydroxide at 40°C. The second reaction involved Calcium hydrogen phosphate [Ca(H2PO4)2.H2O] and Calcium hydroxide at room temperature.
10 Ca(OH)2 + 6 (NH4)2.HPO4 Ca10(PO4)6(OH)2 + 6 H2O + 12 NH4OH ….(2)
7 Ca(OH)2 + 3 Ca(H2PO4)2.H2O Ca10(PO4)6(OH)2 + 15 H2O ………….…..(3)
Manuel et al. has proposed a precipitation reaction for synthesis of HAP 23 nanoparticles. The H3PO4 was added to Ca(OH)2 until Ca/P = 1.67 is maintained under stirring at room temperature and pH 10. Crystallization started after NH4OH addition. Crystal growth was allowed for 24 hours and sinteration performed at 1000°C for 1 hour. Jarcho et al. has reported another precipitation method through reaction of calcium nitrate
[Ca(NO3)2. 4H2O], with (NH4)2.HPO4 with continuous stirring at room temperature to get HAP with grain sizes < 100 nm.24 All these previous methods involve tedious and time consuming procedures. Thus, there is a need for a new, convenient, and simple method for direct precipitation of pure HAP.
There are several methods for production of OCP reported in the literature including homogeneous crystallization from a solution of calcium hydrogen phosphate at temperature of 40 to 70 0C and pH 5.7 to 7.0 and slow diffusion of calcium ions into phosphate-containing gel systems at pH 6 to 7.5. 29,30 The synthesis of octacalcium phosphate can also be done through precipitation reactions of calcium acetate and phosphate solutions under different experimental condition as reported by LeGeros, Arellano-Jime´nez et al. ,and Komlev et al. 31-33 Brown et al. has proposed another method for formation of OCP through the hydrolysis of dicalcium phosphate dihydrate 34 CaHPO4.2H2 O, DCPD.
10CaHPO4.2H2O + H2O …...Ca8 (HPO4)2 (PO4)4 ⋅ 5H2O……….……………………..(4)
24
However, these methods were inconsistent in forming exclusively OCP.
2.3 Experimental
2.3.1 Materials
Calcium chloride CaCl2, Sodium dihydrogen phosphate NaH2P04. 2H2O , Di sodium hydrogen phosphate Na2HPO4.7 H2O, Sodium chloride NaCl, Sodium citrate
Na3C6H5O7.2H2O, Sodium sulfate Na2SO4, Magnesium sulfate MgSO4.7H2O,
Ammonium chloride NH4CI, Potassium chloride KCI and Sodium oxalate Na2C204 were purchased from Fisher Scientific Company L.L.C. (Pittsburgh, PA). Calcium acetate monohydrate was purchased from Sigma Aldrich (St. Louis, MO).
2.3.2 Synthesis of calcium oxalate dihydrate COD
Calcium oxalate dihydrate is formed by adding 50 mL of 0.1 M of CaCl2 to 200 mL solution containing 0.05M NaH2P04. 2H2O, 0.08 M Na2HPO4.7 H2O, 2M NaCl, 0.02
M Na3C6H5O7.2H2O, 0.3 MNa2SO4, 0.12M MgSO4.7H2O, 0.8M NH4CI, 0.16M KCI
,and 0.01 M Na2C204. 7H2O .The solution was mixed and maintained in an ice bath for 2 hours. Then, the solution was filtered using 0.45 m nylon filter and the precipitate was allowed to dry. The crystals were excised from the filter paper and examined using an optical microscope.
2.3.3 Synthesis of Hydroxylapatite
Hydroxylapatite was synthesized by addition of 250 mL of 0.02M Calcium acetate solution to a 250 mL phosphate solution containing 0.02M NaH2P04. 2H2O and
0.02 M Na2HPO4.7 H2O. The reactant solutions are combined all at once at pH 6.6 and maintained at temperature of 600 C for 3 hours without stirring. Then, the precipitate was filtered and allowed to air dry.
2.3.4 Synthesis of Octacalcium phosphate
OCP was formed by dropwise addition of 250 mL calcium acetate solution of concentration 0.04M to 750 mL phosphate solution containing 5 mM of NaH2P04. 2H2O 0 and 5mM of Na2HPO4.7 H2O. The reaction was carried out at 70 C and starting pH of 5.
25
The solution is stirred using magnetic stirrer during the precipitation process. The precipitate was kept with mother solution for 1 hour, then filtered and air dried.
2.3.5 Instrumentation
All the formed components are stable when stored in dry conditions at room temperature. The x-ray diffraction patterns of the components were measured using a Scintag Pad X powder diffractometer using Cu K-alpha radiation and an energy discriminating Peltier cooled detector. ATR FTIR spectra were collected from 4000-400 cm-1 using a Perkin Elmer Spectrum one Fourier transform infrared spectrometer with a Ge internal reflection element (IRE) and the standard deuterium triglycine sulfate (DTGS) detector. For each component, 32 individual scans were obtained with a spectral resolution of 4 cm-1. Raman spectra were recorded with a Perkin Elmer spectrometer in the 3600 cm−1 to 100 cm−1 region using the 1064 nm wavelength excitation from Nd YAG ion laser and a power of 200 mW.
2.4 Results and discussion
A number of reported methods for calcium oxalate dihydrate COD formation were tried but mixed forms of COM and COD were obtained (Figure 2.3). The spectrum shows a broad band at 1620 cm-1 attributed to carbonyl stretch of COD and COM. The band at 850 cm-1 of OH vibration is characteristic for COM while the broad band between 3000-3500 cm -1 which is attributed to symmetric and asymmetric OH stretch of crystallized water is typical for COD. 35The intense peak at 1078 cm-1 indicates the presence of calcium phosphate reagent used in the synthesis.
Different calcium to oxalate molar ratios were used for COD formation trials in solution of artificial urine proposed by Grases et al. 18containing sodium chloride , sodium phosphate, sodium citrate , sodium dihydrogen sulfate , potassium chloride and magnesium sulfate . The results show that the optimum (Ca: Ox) molar ratio is 10:1. Magnesium sulfate and potassium chloride enhance COD formation by decreasing the relative supersaturation and inhibiting COM formation. The described method is considered to be quick, reproducible and suitable for production of pure COD crystals compared to the other reported methods.
26
99
96 94 92 90 88 86
%T 84 82 80 78 76 Phosphate group stretch
74 4000 3500 3000 2500 2000 1500 1000 500400 cm-1 Figure 2.3: ATR spectrum of mixed phases of calcium oxalate.
The obtained crystals are tetragonal bipyramidal in shape with average length 10 m (Figure 2.4). The formation of pure COD is confirmed by the X ray diffraction pattern which fits with PDF #75-1314 (Figure 2.5). ATR spectra and Raman spectra were also measured for the formed crystals. The principal difference between IR spectra of COM and COD is the presence of two peaks between 850 and 950 cm–1 attributed to OH deformation for COM while these two peaks do not appear for COD. Also, the two peaks at 780 and 517 cm–1 arising from C-O bending are typical of COM and important for distinguishing the two forms. Additionally, the very marked band of CO stretching is lying at 1615 cm -1 for COM and shifted to higher wavenumber 1625 cm-1 for COD .In the 3500- 3000 cm–1 region, a broad peak appears for COD, by contrast with the one for COM, which contains five weak bands arise from the symmetric and asymmetric OH stretch of crystallized water (Figure 2.6).35 Furthermore, the characteristic Raman band attributed to C-O symmetric stretch appears as singlet at 1476 cm-1 for COD and as doublet at 1464 and 1491cm -1 for COM which enables distinction between them (Figure 2.7).36
27
10
Figure2.4: Optical image of Calcium oxalate dihydrate COD crystals.
Figure 2.5: X-ray diffraction pattern of obtained Calcium oxalate dihydrate COD.
The red lines represent a reference powder diffraction pattern of COD (PDF #75-1314).
28
a 98
90 85 80 75 %T 70 65 60 55 b 48 98
90 85 80 75 %T 70 65 60 55 51 4000 3500 3000 2500 2000 1500 1000 450 cm-1
Figure 2.6: ATR spectra of a) Calcium oxalate monohydrate COM and b) Calcium oxalate dihydrate COD.
32 30 a 25 20 15
Egy 10 5
-1 27 22 b 20 18 14
Egy 12 10 6 4 2 -1 1800 1600 1400 1200 1000 800 600 450 Raman Shift / cm-1
Figure 2.7: Raman spectra of a) Calcium oxalate monohydrate COM and b) Calcium oxalate dihydrate COD.
29
In attempting to find a simple method for preparing hydroxylapatite, a reaction between calcium acetate and sodium acid phosphate was tried under various conditions of pH and temperature. Hydroxyapatite (HAP) was successfully synthesized via precipitation at pH 6.5 and temperature of 60 0C. Of all the reported methods for OCP synthesis, that by LeGeros seems to be the most appropriate. However, the method is not reproducible resulting in formation of mixture of dicalcium phosphate dihydrate DCPD and OCP (Figure 2.8). The spectrum shows typical features of OCP which are the intense –1 2- 3- bands at 1138–1010 cm corresponding to the ν3 mode of НРO4 and РO4 and medium –1 3- intensity bands at 601–560 cm for the ν4 mode of РO4 but the spectrum also contains –1 3- 23,37 bands at 529 and 574 cm of РO4 groups that are characteristic for DCPD. Thus, the method was optimized regarding temperature and pH. The results arrived at the same conclusions as other researchers that low pH, high temperature of 70 and 800C and the slow and continuous addition of the reagents solution favor the synthesis of pure OCP.
101
95
90
85
80
%T 75
70
65
60 57 4000 3500 3000 2500 2000 1500 1000 500400 cm-1
Figure 2.8: ATR spectrum of DCPD /OCP mixture.
30
The formed HAP and OCP were characterized with X-ray diffraction (XRD), Infrared spectroscopy, and Raman spectroscopy. The X-ray diffraction pattern of HAP has fit with PDF #09-0432 of synthetic HAP and with PDF #84-1998 of natural HAP (Figure2.9) which confirms the synthesis of pure HAP. The X-ray diffraction pattern of OCP also shows fit with PDF # 26-1056 (Figure2.10). The ATR spectra in Figure 2.11 show strong bands for HAP and OCP in the 1105-1000 cm-1 region which arise from asymmetric stretch (vas ) of the orthophosphate group. There are more bands in this region in the spectrum of OCP compared to HAP spectrum because of the asymmetric structure of OCP containing two HOPO3 and four PO4 groups while all the PO4 groups in HAP are equivalent (Figure 2.2).38 The band at 917 cm-1 is characteristic for OCP due to the P-(OH) stretching mode of the acid orthophosphate groups (Figure2.11). The OCP Raman spectrum also contains more peaks than the HAP. The weak band at 1010 cm−1 is the most characteristic OCP Raman band, which is absent in HAP spectrum (Figure 2.12).37
Figure 2.9: X-ray diffraction pattern of Hydroxylapatite HAP.
The blue lines represent reference powder diffraction patterns of HAP (PDF #09-0432 of synthetic HAP and with the PDF #84-1998 of natural HAP).
31
Figure 2.10: X-ray diffraction pattern of Octacalcium phosphate OCP.
The blue lines represent a reference powder diffraction pattern of OCP (PDF # 26-1056).
32
a 99 90 85 80 70 65
%T 60 50 45 40 b 34 99 90 80 70
%T 60 50 40 30 4000 3500 3000 2500 2000 1500 1000 450 cm-1
Figure 2.11: ATR spectra of a) Hydroxylapatite HAP and b) Octacalcium phosphate OCP.
33
7 7 a 6 5 4
Egy 3 2 1 -0 6.6 5.5 b 5.0 4.5 3.5
Egy 3.0 2.5 1.5 1.0 0.5 -0.1 1500 1400 1200 1012.301000 800 600 450 Raman Shift / cm-1 Figure 2.12: Raman spectra of a) Hydroxylapatite HAP and b) Octacalcium phosphate OCP.
34
2.5 Conclusion
This work represents simple and effective methods for chemical synthesis of pure calcium oxalate dihydrate COD, hydroxylapatite HAP, and octacalcium phosphate OCP compared to the other reported methods. The formation of these compounds has been confirmed by infrared, Raman spectroscopy, and X-ray diffraction measurements. Synthesis of these compounds allows determination of spectral dissimilarities between them and enables their easier identification in mixed kidney stone and complex mineral inclusions in tissues. Distinction of these phases of calcium phosphate or calcium oxalate can help in accurate diagnosis and providing the patients with stone specific treatment. In addition, the spectra of these pure compounds can be used to construct principal component regression (PCR) models for quantitative analysis of kidney stone components.
35
References
1. M .S. Parmar, BMJ, 328(7453):1420-1424, (2004).
2. P. M. Hall, Cleve Clin. J. Med.; 69(11):885-888, (2002).
3. D. A. Bushinsky, Adv. Intern. Med, 47:219-238, (2001).
4. J. C. Williams Jr, B. R. Matlaga, S. C. Kim, M. E. Jackson, A. J. Sommer, J. A. McAteer, J.E. Lingeman, A. P. Evan, J. Endourol., 20(11):885-890, (2006).
5. L. Lepage, R. Tawashi, J. Pharm. Sci., 71(9):1059-62, (1982).
6. W. Doherty, O. L. Crees, E. Senogles, Cryst. Res. Technol., 29, 4 517-524, (1994).
7. F. Grasesl, A. Millanl, and A. Conte, Urol. Res., 18:17-20, (1990).
8. J. R. Asplin, J. Lingeman, R. Kahnoski, H. Mardis, J. H. Parks, F. L. Coe, J. Urol. 159(3):664-8, (1998).
9. X. Parent, G. Boess, P. Brignon, Prog. Urol., 9(6):1051-6, (1999).
10. M. Daudon, C. A. Bader, & P. Jungers, Scanning Microsc., 7, 1081-1106, (1993)
11. M. Daudon, H. Bouzidi, & D. Bazin, Urol. Res., 38, 459-467, (2010).
12. M. Daudon, et al.,Urol. Res., 23, 319-326, (1995).
13. P. Meria, H. Hadjadj, P. Jungers, & M. Daudon, J. Urol., 183, 1412-1416, (2010).
14. R. Z. LeGeros, Calcif. Tissue Int., 37:194-197, (1985).
15. H. Lachance and R. Tawashi, Scanning Microsc., 1, 564, (1987).
16. X. Martin, L. H. Smith and P. G. Wernees, Kidney Inter., 25, 948, (1984).
17. J. H. Wieessner, G. S. Mandel, and N. S. Mandel, J. Urol., 135, 835, (1986).
36
18. P. Brown, D. Ackermann, B. Finlayson, J. Crystal Growth, 98, 3, 285-292, (1989).
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21. E. Bouyer, F. Gitzhofer, M. Boulos, J Mater Sci: Mater Med., 11: 523-531, (2000).
22. M. H. Santos, M. Oliveira, P. Souza, H. S. Mansur, W. L., Mater. Res.; 7(4): 625- 630, (2004).
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24. M. Jarcho, J. F. Kay, K. I. Gumar, R. H. Doremus, H. P. Drobeck., J.Biosci. Bioeng., 1: 79-92, (1977).
25. T. V. Thamaraiselvi, K. Prabakaran, S. Rajeswari, Trends Biomater. Artif. Org., 19(2): 81-83, (2006).
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33. V. S. Komleva, I.V. Fadeevaa, A. S. Fomina, L. I. Shvorneva, Dokl. Chem., 432, 2, 178–182, (2010).
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38
Chapter 3
39
Effect of Sample Preparation on Kidney Stone Analysis
3.1 Abstract
The qualitative and quantitative analysis of intact renal calculi is challenging because of the size and fragility of the calculi. Many modern methods of analysis destroy the structure of the stones during sample preparation procedures. Maintaining the structural integrity of the renal calculi is important for determination of chemical components of the calculi and studying the etiology of the disease. This chapter investigates the effect of sample preparation on infrared microspectroscopic analysis of renal calculi. The research presented here studies the effect of polishing the surface of cross-sectioned renal calculi using two techniques including sanding discs and lapping films. It also involves a comparison of the use of Attenuated Total Internal Reflection (ATR) infrared microspectroscopic imaging with reflectance infrared imaging technique for the analysis of microstructure of urinary stones.
3.2 Introduction
Understanding the etiology of renal calculi is linked to stone composition and the spatial arrangement of minerals and organic materials in the stone.1-2 Even with using modern spectroscopic methods for analysis of stones, existing sample preparation procedures still hinder answering important questions in elucidating the stone formation.3 Thus, there is an urgent need for proper stone analysis that maintains the structural integrity of the urinary stones. Over the past three decades, spectroscopic techniques such as infrared (IR) and Raman spectroscopy have shown great capabilities in the study of different biological samples including renal calculi. IR and Raman can identify the mineral inclusions and organic compounds in tissue samples providing a complete picture of sample composition. 4-7
Reflection spectroscopy has also been applied in analysis of kidney stones. It includes two main types: specular and diffuse reflection and each of them requires different sample properties. For specular reflectance, the surface must be smooth with surface irregularities small compared with the wavelength of the incident radiation. In that case, the spectrum will be a purely specular reflectance spectrum that can be
40 transformed into a transmission spectrum using Kramers-Kronig transformation.8 In contrast, diffuse reflectance spectroscopy requires the sample to be rough and highly scattering and individual particle sizes be similar to the radiation wavelength employed in the analysis.9 Diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) has been widely used in analysis of renal calculi however, it involves tedious sample preparation since the stones are ground to a fine powder and diluted with potassium bromide. This approach also destroys the structure of stones which prevent deposition chemistry from being obtained. 10-12
Traditional spectroscopic techniques cannot directly determine the spatial distribution of different constituents in the stone samples. To accomplish this, infrared microspectroscopic techniques can be employed which enable mapping the location of each spatially resolved component. It combines the capability of spectroscopy for identification of different components based on their chemical structure with the power of imaging, affording potential information about the microstructure and architectural arrangement of minerals and organic materials in biological tissues.13-15 This information helps to study the mechanisms of initial stone formation and interstitial mineral depositions for complex stones such as Randall’s plaques in idiopathic calcium oxalate stone formers.16 This laboratory has studied the use of infrared microspectroscopic techniques for the analysis of renal calculi for the past several years. The reflectance infrared imaging has been useful for qualitative analysis of cross-sectioned stones while attenuated total internal reflection (ATR) infrared imaging has been successfully applied for studying mineral deposits in tissue biopsies 17-20 In 2005, Anderson et al. developed an approach to study the kidney stone formation in tissues at early stages utilizing the reflectance microspectroscopic imaging technique. The data gave good qualitative information for identification of minerals but, the spectral artifacts in reflection spectra of tissues made interpretation of the spectra difficult.21 In 2007, Anderson et al. developed a simple method for qualitative analysis of the mineral layers in cross sectioned renal stones using reflectance microspectroscopic imaging technique. The investigation demonstrated that the reflectance technique is more applicable than transmission technique which requires tedious sample preparation for a thin sectioning process. They also have studied the
41 influence of specular reflectance between polished and unpolished sample spectra, and it was found that specular reflectance improved minimally with a highly polished stone cross-section surface.12 In 2010, Gulley-Stahl et al. compared Attenuated Total Internal Reflection (ATR) infrared microspectroscopic imaging technique with trans-flection infrared imaging in studying mineral inclusions in kidney biopsies. The results show that ATR spectra have minimized spectral artifacts compared to transmission and trans- flection mode.19 In the ATR method, the sample is placed in intimate contact with an internal reflection element (IRE) such as a germanium hemisphere. The infrared light evanescently penetrates the sample and is absorbed yielding the spectrum. This technique has been shown to overcome the problem of optical artifacts associated with both trans- flection and transmission modes. In addition, the IRE leads to an optical immersion effect, which increases the spatial resolution of the method by a factor of four compared to reflectance infrared imaging.18 Later, Chen et al. continued the investigation through application of ATR microspectroscopic imaging technique for the analysis of kidney stone cross-sections. The study demonstrated that ATR imaging has better spatial resolution and shows more details compared with reflectance imaging technique. Chen et al. also introduced in this study a new protocol for sample preparation of kidney stones through their embedment into spur resin followed by microtoming to yield a flat surface. The results demonstrated that the reflectance data collected from renal stones was a combination of reflectance absorption and scattering which complicates the spectral identification and prohibits the accurate diagnosis of the stones.22
Improvement of spatial resolution and photometric accuracy of reflection imaging technique is essential to facilitate qualitative analysis there by providing better images of renal stones and better spectra of their components. Thus, this chapter presents a study on the effects of surface roughness of renal stones on the spatial resolution of reflectance imaging and the quality of its spectra. This work differs from previous work performed by others in that it is performed in a manner conducive to subsequent analysis by other techniques such as Raman spectroscopy with easier preparation steps and less time, while providing smooth and flat surfaces. The sampling preparation steps were also applied for analysis of different samples of renal stones using ATR imaging. In ATR imaging of renal stones, it is necessary to ensure perfect contact of the sample with the
42
ATR crystal to maintain adequate and consistent band intensities which may impact the reproducibility and feasibility of using the technique in quantitative analysis. The observed absorbance increases as the air gap between the sample surface and the crystal is decreased, bringing more of the sample into the evanescent field. 23This can be achieved by proper sample preparation to obtain flat and uniform surfaces of renal calculi that maximizes the absorption bands and enables quantitative analysis.
3.3 Experimental
3.3.1 Materials and methods
Kidney stones were obtained from Indiana University Medical School and the stone cross sections were embedded in epoxy resin. The samples were mounted in a designed fixture to gently polish the surface of the stones (Figure 3.1). The abrasive discs utilized included 5" diameter PSA Silicon Carbide Sanding Discs of 120, 300, and 600 grit sizes (Fintech Abrasives) and circular shape Lapping films of 0.3 micrometer, 3 micrometers, and 30 micrometers(Mark V laboratory).
Figure 3.1: Diagram showing the utilized fixture for polishing the stone surface.
Samples were analyzed with a Perkin Elmer Spectrum Spotlight infrared imaging microscope with liquid nitrogen cooled HgCdTe (MCT) detector. Each pixel in the imaging array corresponds to a 6.25 x 6.25 μm2 area of sample when used in reflectance mode. In the reflectance mode, the samples were placed on a low-E slide (Kevley
43
Technologies) and the background spectra were collected first using the reflective side of the slide. The ATR imaging accessory employs a Germanium hemisphere with pixel size of 1.56 µm and imaging area of 400 x 400 µm2. The spectra were collected at 8 cm-1 spectral resolution and 16 scans were averaged per pixel.
3.4 Results and Discussion
The renal stone samples were first polished using wet silicon carbide sanding discs varying in grit size from 120-600. The grit size corresponds to the number of abrasive particles per inch of sanding discs. The lower the grit size, the rougher the outer layer of sanding discs. The results show that smoother stone surface can be achieved on using greater grit size of 600 (Figure 3.2). Figure 3.3 shows significant differences between the spectra as the surface roughness of the stone decreases with respect to reflectance intensity and signal to noise ratio. This intensity difference is due to the increased specular reflectance from the polished surface. The polished surfaces result in greater specular reflectance compared to unpolished surfaces. Also with close inspection of spectra of uric acid in Figure 3.3, the bands in the region from 3500 to 2250 cm-1 attributed to the NH are weak in Figure 3.3a but these bands are well defined in Figure 3.3 c. It also should be mentioned that using wet sanding discs does not dissolve the stone components since the majority of stones consist of calcium oxalates or calcium phosphate salts which have very low Ksp values therefore it does not interfere with the stone analysis.
44
a b c
Figure 3.2: Visible images of polished uric acid kidney stones using sanding discs of grit size a) 120, b) 320, and c) 600.
c 69 65 60 55 50 45 b 40 R 35
%T % 30 25 a 20 15 10 5 0 4000 3500 3000 2500 2000 1500 1000 580 cm-1 Figure 3.3: Reflectance spectra of polished uric acid stone using sanding discs of grit size a) 120, b) 320, and c) 600
In general, the reflectance spectra include a mixture of specular and diffuse reflection. The contribution from specular reflection is dominant in case of polished stones, thus Kramers-Kronig (K-k) transformation can be applied to transform derivative-
45 shaped bands of specular reflectance into symmetrical absorption bands which are easily interpretable and facilitate qualitative analysis of the stone components. K-K transformation enables displaying of an absorption index and/or a refractive index spectrum since both the absorption of the sample and its refractive index change with wavelength.25 Figure 3.4 shows the absorption index of polished kidney stone calculated from its specular reflectance spectrum.
54 40 30
%T 20 10 R% -1 6 4
A 2 -0 -1 0.28 0.20 0.15 A 0.10 0.05 0.00 4000 3500 3000 2500 2000 1500 1000 580 cm-1
Figure 3.4: The reflectance spectrum of uric acid kidney stone (top), the K-K transformation (middle), and a reference spectrum of uric acid.
The lapping Films were also utilized for renal stone polishing. They are made of precision-graded minerals such as Aluminum oxide adhering with a water proof resin system to a dense polyester film. As the abrasive particle size of lapping films decreases, they can produce finer polish and by the successive use of finer lapping films, nearly mirror like surface can be obtained. The results show significant improvements in the surface roughness of cross sectioned stones on polishing with lapping films since they have smoother outer layers compared to sanding discs thus finer smoothness of stone surface can be obtained (Figures 3.5-3.6). Spectra of polished stones using lapping films and those polished with sanding discs are shown in Figures 3.7-3.8. In comparison of the reflectance spectra of COM spectra in Figure 3.7, the reflectance intensity is almost doubled and the signal to noise ratio is much higher for lapping film polished stone in
46
Figure 3.7a. Also, the bands from 3400 to 3000 cm-1 attributed to the symmetric and asymmetric OH stretch of crystallized water are easily observed in Figure 3.7a. Similarly, the reflectance intensity of spectrum of uric acid in Figure 3.8a is much higher than in Figure3.8 b. Thus, these polishing procedures result in a significant improvement of photometric accuracy of reflectance spectra.
Figure 3.5: Visible images of COM containing kidney stone polished using lapping films (Left) and sanding discs (Right).
Figure 3.6: Visible images of uric acid containing kidney stone polished using lapping films(Left) and sanding discs (Right).
47
52 a 40 R 30 % 25 %T 20 10 5 -1 31 b 25
R 20
% %T 15 10 5 0 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.7: Reflectance spectra of COM containing kidney stone polished using a) lapping films and b) sanding discs.
70 a 60 R 50 % 40
%T 30 20 10 -1 b 54 50 R 40 % 30 %T 20 10 -1 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.8: Reflectance spectra of uric acid containing kidney stone polished using a) lapping films and b) sanding discs.
48
3.4.1 Optimization of sample preparation procedures
In order to get the best results possible using both reflectance infrared imaging and ATR imaging, two requirements must be met. First, the stone surface should be as smooth as possible and second, the bottom of the stone mount and the top surface of the polished stone must be parallel to one another. These requirements were met by constructing another polishing device which consisted of a stationary stage, a polishing puck and a translation stage with a stone mounting post. A photograph and diagram of the device is shown in Figure 3.9.
Polishing puck Stationary stage Stationary stage
Stone mounting post
Translation stage
Figure 3.9: A photograph and diagram of the polishing device.
The stone was affixed to a 1 centimeter polystyrene disk using chloroform. The polystyrene disk was punched out of a petri dish. This composite was then glued to the stone mounting post using Duco cement. Lapping films applied to the puck were used to polish the stone surface. This was accomplished by raising the stone composite in 1 micrometer increments using the translation stage. The translation stage was spring loaded which kept a constant pressure between the stone and the polishing puck. The polishing puck was then moved in a figure 8 pattern to effect polishing of the stone.
49
On analysis of polished renal stones with the above device using reflectance and ATR imaging, the results show that the quality of reflectance images and the extracted spectra are further improved. However, the ATR images of the studied renal stones have better resolution compared to the reflectance images and the spectra extracted from ATR images exhibit higher photometric accuracy.
The spatial resolution of a microscope is ultimately is dictated by the Rayleigh criterion. The Rayleigh criterion is given by the following equation where ““ is the wavelength of light, “sin ” is the half angle acceptance of the objective and “n” is the refractive index of the measurement medium,” r “is the required separation between two objects in order for them to be resolved. r 0.61/n sin ……………………………………………………….…. Equation 1
Since the measurements in reflectance are conducted in air n = 1, the theoretical spatial resolution in reflection is calculated to be two wavelengths Equation 2. While, in ATR, with the Germanium as the IRE, n = 4, so the spatial resolution will be 0.5λ Equation 3.
푟 = 0.61 휆 /푁.퐴. = 0.61 휆/ 0.3 = 2휆 ………………………………………. Equation 2
푟 = 0.61 휆/ 푁.퐴. = 0.61휆/ (4∗0.3) = 0.5휆………………………………….… Equation 3
Figure 3.10 shows the reflectance image and ATR image of the same region of calcium oxalate monohydrate COM stone cross-section. Clearly, the ATR image has better spatial resolution compared to the reflectance image. The color scale in both images shows the distribution of stone components. Areas with high abundance of calcium oxalate are indicated by the pink color in ATR image and with red in the reflectance image corresponding to higher integrated band intensity; while areas with low abundance of calcium oxalate are presented in yellow color in ATR and with pink color in reflectance image. Figures 3.11-3.12 show the corresponding spectra in ATR and reflectance images. It is noticeable that the reflectance spectra of COM display improved signal to noise ratio and well defined peaks allowing straightforward identification of the stone component.
50
Figure 3.10: ATR image (Left) and Reflectance image (Right) of the same region of calcium oxalate stone cross-section.
103 90 80 70
%T 60 50 40 27 104 80 70 60 %T 50 40 30 17 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.11: The extracted ATR spectra from yellow (top) and pink regions (bottom) in ATR image of COM containing stone.
51
44 40 30 25 R 20
%T % 15 10 5 -0 27 25 20 R % 15
%T 10 5 1 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.12: The extracted reflectance spectra from red (top) and pink regions (bottom) in reflectance image of COM containing stone.
Figure 3.13 shows the reflectance image and ATR image of the same region of uric acid stone cross-section. This example illustrates the ability of infrared microspectroscopy techniques to reveal microstructure details in stone cross section and shows the improved spatial resolution of ATR technique. The red regions in ATR image represent areas of high abundance of uric acid while the blue regions indicate the areas of low abundance or absence of uric acid. The red regions in reflectance image represent uric acid composition. Both images also show thin green line and the extracted spectra indicate the presence of a thin layer of calcium oxalate monohydrate. It is noticeable that ATR image yield a highly photometric accurate spectrum of COM while the reflectance spectrum shows the characteristic bands for COM of the asymmetric C=O stretch at 1610 cm-1 and the 1315 cm-1 symmetric C=O stretch overlapped with the peaks for neighboring uric acid layers (Figures 3.14-3.15).
52
Figure 3.13: ATR image (Left) and Reflectance image (Right) of the same region of uric acid stone cross-section.
Uric acid
118 90 80 70 %T 50 40 COM 30 15 105 90 80 70
%T 60 50 40 26 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.14: The ATR spectra of uric acid (top) and COM (bottom) extracted from pink and green regions in ATR image, respectively.
53
Uric acid 46
35 R 30 20
%%T 15 10 5 -1 10 COM & uric acid 8 R 7 % 5 %T 4 3 1 0 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 3.15: The reflectance spectra of uric acid (top) and COM overlapped with uric acid (bottom) extracted from red and green regions in reflectance image, respectively.
The red lines indicate the characteristic peaks of COM.
54
3.5 Conclusion
The results of this study demonstrate that the sample preparation of kidney stones using lapping films result in greater increase in the reflectance intensity and improvement of spectral quality of reflectance technique compared to sanding discs. Polishing the surface not only enhances the quality of reflectance spectra but also improve the contact of the stone surface with the crystal in ATR technique. For the present research, both reflectance and ATR methods can be utilized in a complementary method for analysis of cross sectioned renal stones with each possessing certain advantages. Analysis using the reflectance imaging under these optimized sample preparation procedures has been shown to be simple and rapid method for identification of chemical components of the stones. While, ATR imaging provides improved spatial resolution and photometrically accurate data that allow studying of spatial distribution of stone constituents and potentially enable quantitative analysis.
55
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1. A. P. Evan, Pediatr. Nephrol., 25(5): 831–841,(2010).
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3. A. E. Krambeck, et al., J. Urol., 184, 1543-1549, (2010).
4. I. U. Rehman, Z. Movasaghi, S. Rehman, Vibrational Spectroscopy for Tissue Analysis. (Published by CRC Press, Taylor & Francis Group, LLC) p. 111-130, (2013).
5. J. R. Guerra-López, J. A. Güida, C. O. Della Védova, Urol Res., 38(5):383-90, (2010).
6. J. Tonannavar, G. Deshpande, J. Yenagi, S. B. Patil, N.A. Patil, B.G. Mulimani.,Spectrochim. Acta A Mol. Biomol. Spectrosc., 154:20-26 (2016).
7. V. Castiglione, P. Y. Sacré, E. Cavalier, P. Hubert, R. Gadisseur, E. Ziemons , PLoS One. 13(8):e0201460, (2018).
8. F. J. Bergin, Appl. Spectrosc., 43(3), 511-15. 66, (1989).
9. J. P. Blitz, Diffuse Reflectance Spectroscopy, in Modern Techniques in Applied Molecular Spectroscopy, F.M. Mirabella, Editor, Wiley-Interscience Publication: New York. P.185-219, (1998).
10. M. Berthelot, G. Cornu, M. Daudon, M. Helbert, C. Laurence, Clin. Chem., 33, 6, (1987).
11. L. Estepa, M. Daudon, Biospectrosc., 3, 5, 347-369, (1997).
12. J. C. Anderson, J. C. Williams Jr, A.P. Evan, K. W. Condon, A. J. Sommer, Urol. Res., 35:41, (2007).
13. R. Mendelsohn, E. Paschalis, and A. Boskey, J. Biomed. Opt., 4(1): 14-21, (1999).
14. S. Aparicio, B. Doty, N. Camacho, E. Paschalis, L. Spevak, R. Mendelsohn, A .Boskey , Calcif Tissue Int, 70: 422, (2002).
15. M. Daudon, Rev Med Suisse, 124(8), 445-53, (2004).
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16. I. Sethmann, G. Wendt‑Nordahl, T. Knoll, F. Enzmann, L. Simon, H. J. Kleebe, Urolithiasis, 45(3):235-248, (2017).
17. J. C. Anderson, J. C. Williams Jr., A. P. Evan, K. W. Condon, and A. J. Sommer, Urol. Res. 35, 41-48, (2007).
18. H. J. Gulley Stahel, A.J. Sommer, “Evanescent wave imaging” ed. G.Srinivasan, (Mc Graw Hill Companies, Inc, China) p. 100-102.
19. H. J. Gulley-Stahl, S. B. Bledsoe, A. P. Evan, and A. J. Sommer, Appl. Spectrosc., 64(1), 15-22, (2010).
20. A. J. Sommer, L. G. Tisinger, C. Marcott, and G. M. Story, Appl. Spectrosc., 55(3), 252-256, (2001).
21. J. C. Anderson, J. Dellomo, A. J. Sommer, A. P. Evan, and S. Bledsoe, Urol. Res. 33, 213 (2005).
22. C. Ling, J. C. Williams Jr, A. P. Evan and A. J. Sommer, Microsc. Microanal., 19 (Suppl. 2), 240-241, (2013).
23. F. Friedrich and P. G. Weidler, Appl. Spectrosc., 64(5), 500–506, (2010).
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Chapter 4
58
Application of Attenuated Total Internal Reflection ATR-FTIR Spectroscopy in Quantitative Analysis of Kidney Stones
4.1 Abstract
Urinary stones result from a variety of diseases or disorders. Identification of the stone components is important in order to provide stone-specific treatment, to prevent recurrence, and to reveal the etiology of stone development. The most common types of kidney stones are those comprised of hydroxyapatite and calcium oxalate. However, other components commonly found include carbapatite, calcium carbonate, and protein. In certain stone formers, the stone is comprised of a mixture of all components mentioned. Acquiring knowledge about the relative content of these components at the very onset of stone formation can enable the determination of causative factors of the disease. The methods for quantitative analysis of stone constituents remain limited. Thus, this study has investigated the use of ATR FTIR in quantitative analysis of renal stones using Principal Component Regression (PCR) models. The prediction accuracy of these models has been verified using synthetic mixtures of the corresponding components. The results have demonstrated that the constructed PCR models have good linear relationships between the actual and predicted concentrations and they can be potentially used for quantitative analysis.
4.2 Introduction
Urolithiasis or renal stone disease is a growing problem throughout the world in the last twenty years. It affects about 5% of women and 12% of men in the United States.1 Urinary calculi occur in a wide range of compositions from organic and inorganic materials which are varied with clinical conditions or dietary factors .2,3 Calcium oxalate is the most prevalent mineral found in about 80% of urinary stones and hydroxylapatite (HAP) is considered to be the second frequent mineral in renal stones.4-6 The etiology of stone formation is not yet well understood. Understanding the etiology requires knowledge of the stone components and their relative concentrations. Many methods have been reported for qualitative analysis of renal stones however, there is no single reported analytical method that can provide an exact quantitative analysis of stone
59 components.7-8 Urologists have discovered that in some cases, the chemical composition of mineral inclusions in kidney biopsies changes over time.9 Therefore, there is a need for quantitative analysis of these mixed mineral deposits to provide potential information to study the initial stone formation and its growth.
Beer’s Law is one of the principals of quantitative analysis for most spectroscopic based analytical techniques which states that absorbance increases linearly with sample concentration. For quantitative determination of binary mixtures based on Beer’s law, the characteristic absorption bands of mixture components should not overlap in order to build an accurate linear least square regression (LLSR) calibration curve. Another limitation of LLSR is that particle sizes, surface forces, and inconsistent noise can lead to uncertainty into the model.10 Planinšek et al. investigated the use of ATR FTIR for quantitative analysis of different binary mixtures of pharmaceutical components using LLSR .The results show non-linear correlation due to distribution of particle sizes of components and the compressibility of the particles. A positive deviation from linearity is observed for smaller particles while a negative deviation is observed for larger particles. Planinsek explained that smaller particles occupy a larger specific surface area, surrounding the larger particles, resulting in a decreased absorbance for the larger particle. Planinšek et al. concluded that similar sized components can generate calibration curve linearity, but small/large particle systems can produce nonlinearity. 11
PLSR and PCR are other efficient statistical methods used for quantitative analysis. Compared with LLSR, they have improved prediction accuracy, high multiple correlation coefficients, and a low root mean standard error (RMSE).10, 12 However, the mathematical steps involved in PLSR are considered to be more complex and inefficient compared with PCR models.13-14 The PCR algorithm attempts to establish a relationship between the spectra of each component and a set of calibration standards and the corresponding property values determined by independent means. These relations can be used for prediction of component concentrations in unknown samples. PCR models have been successfully applied in quantitative analysis of different components. 15, 16 Thus, the aim of this study is to develop a reliable method for quantitative analysis of kidney stone components using ATR-FTIR and PCR models.
60
Current analytical techniques used for the qualitative and quantitative analysis of renal stones include wet chemical analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive X-ray, and Micro computed tomography (Micro CT), and infrared spectroscopy (IR)7,17-20. The wet chemical analysis is inaccurate and results in errors in stone diagnosis. It involves dissolution of the stone followed by titration and chemical analysis.21, 22 XRD is suitable for quantification of crystallized materials. However, the stones must be ground into powder, and it cannot detect amorphous substances such as carbapatite.23 SEM with EDS have been used to study the stone morphology and for elemental analysis.24 However, it is not adequate for analysis of mineral inclusion within kidney biopsies due to poor conductivity of tissue biopsies and the possible irradiation damage.25 Micro computed tomography (micro CT) is nondestructive technique which enables high resolution imaging of the fine structure of renal stones and identification of their mineral composition based on X-ray attenuation. However, the method cannot identify mineral types in case of highly complex stones.26
IR spectroscopy is widely used for stone analysis in clinical laboratories. It is useful for identifying organic and inorganic compounds. 27The major drawback to this method is the tedious sample preparation process since the stones are ground with potassium bromide, and pressed into pellets. Moreover, the dilution factor and pellet thickness can affect sample absorbance leading to inaccurate quantitative analysis.28-29 ATR-FTIR spectroscopy has been introduced as an alternative approach for accurate qualitative and quantitative analysis of kidney stones. ATR requires little sample preparation, maintains the integrity of the stones, and gives the most photometric accurate spectra with high reproducibility due to the controlled path length, which allows quantitative analysis.30-32 It has been successfully applied for quantitative analysis of some pharmaceutical components and polymer films. 33-35
This laboratory has studied the utilization of infrared spectroscopy and imaging techniques for quantitative analysis of renal stone components. In 2009, Gulley-Stahl et al. developed a quantitative approach for analysis of stone components using ATR-FTIR and Linear Least-Square Regression (LLSR). Binary mixtures of calcium oxalate monohydrate (COM), hydroxyapatite (HAP), and calcium carbonate were prepared and
61 the calibration curves were plotted using peak areas versus weight percent of analyte to matrix, however, nonlinearity was observed at 70 % HAP/COM ratio.10 They proved that the nonlinearity results from the difference of particle sizes of HAP and COM. Later on, Chen et al. constructed similar LLSR models of these binary mixtures and compared them with PCR models. The results show good prediction accuracy of PCR models and the correlation coefficients were much improved compared to LLSR models.18 The constructed PCR models were applied to selected band regions for each component. In this study, the chemically synthesized HAP in chapter 2 was utilized to construct LLSR models and for assessment of predication ability of PCR models. Also, the constructed PCR models were applied over the entire range.
4.3 Experimental
4.3.1 Materials and methods
Kidney stone samples were obtained from Indiana University Medical School. Calcium Oxalate Monohydrate (COM) was purchased from ACROS organics and Calcium Carbonate (CaCO3) was purchased from EM Science. The original particle sizes of synthesized HAP and COM were measured on Dynamic light scattering DLS - Malvern P analytical. COM/HAP binary mixture samples ranging from 0 to 100 wt % COM or HAP (including 0, 10, 30, 50, 70, 90, 100 wt %) were mixed using Fisher Scientific Vortex Mixer to ensure homogeneity. All mixture samples were compressed into pellets of average weight of 200mg at 5.0 tons per cm2 using a SPEX industries automatic press and an 11 mm pellet die. Three pellets were pressed and scanned for each concentration. The COM/CaCO3 binary mixtures and series of test samples varied from 20 wt% to 80 wt% of each component for the two binary mixtures were prepared with the same procedures.
Infrared spectra were collected with a Harrick Split-pea interfaced to a Perkin Elmer Frontier Fourier transform infrared spectrometer using Germanium internal reflection element (IRE) and the standard deuterium triglycine sulfate (DTGS) detector. For each spectrum, 32 individual scans were obtained from 4000-400cm-1 with a spectral resolution of 4 cm-1. Integrated peak areas were calculated using Perkin Elmer Spectrum
62
5.0.1 software and plotted against the concentration (wt %). The Perkin Elmer Quant software package was used for construction of PCR models. ATR-FTIR images of kidney stone samples were collected on a Perkin Elmer Spotlight infrared microscope. The microscope is equipped with a liquid nitrogen cooled mercury cadmium telluride (MCT) 16x1 linear array detector. The imaging area was 400 x 400 µm2 with spectral resolution 8 cm-1 and pixel size 1.56 µm.
4.4 Results and discussion
Since most kidney stones contain more than one component, the choice was made to use different component mixture powders for the generation of LLSR calibration curves. One of the most common combinations is COM/HAP. Spectra of pure COM,
HAP, and CaCO3 are shown in Figure 4.1. The selected bands for LLSR quantitative analysis include the 1615 cm-1 band for COM, the 1015 cm-1 band for HAP, and the 870 -1 -1 cm band for CaCO3. The COM band at 1615 cm is attributed to asymmetric C=O -1 3- stretch, the HAP absorption band at 1015 cm corresponds to asymmetric ν3 PO4 -1 stretch, and the 870 cm band of CaCO3 arises from Ca-O out of plane bending vibration.36-37
0.5 0.4 a 0.3
A 0.2 0.1 -0.0 0.32 0.25 b 0.20
A 0.15 0.10 0.05 0.00 0.32 c 0.25 0.20
A 0.15 0.10 0.05 0.00 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 4.1: ATR spectra of a) HAP, b) COM, and c) CaCO3.
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LLSR calibration curves were plotted as integrated band intensities versus the concentration. The limits of integration for the selected bands and analytical figures of merit for the calibration curves are listed in Table 4.1. Four sets of calibration curves are shown in Figure 4.2. The data shows improvement of linearity compared with previous investigations. The correlation coefficients were in the range of 0.9519-0.9922. The reason for improved linearity is that the synthesized HAP particle size is comparable to COM resulting in homogenous mixture. The size distribution of HAP and COM particles is shown in Figure 4.3. The average particle sizes of HAP and COM are 1.6m and 1.2m respectively.
Mixture Component/band Limit of Slope Y- Correlation integration (abs/wt%) intercept Coefficient
(abs) (R2)
COM/ HAP COM/1615cm-1 1749-1410 cm-1 0.302 1.63 0.9908
HAP/1015cm-1 1124-971cm-1 0.27 0.08 0.9953
-1 -1 COM/CaCO3 COM/1615cm 1749-1410 cm 0.28 0.50 0.9988
-1 -1 CaCO3/870cm 892-743cm 0.03 0.08 0.9976
Table 4.1: IR bands, limits of integration, and figures of merit for the LLSR calibration curves.
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Peak area
%wt
Peak area Peak area
%wt %wt
Figure 4.2: Calibration curves using LLSR.
65
Figure 4.3: Size distribution of particles of a) COM and b) HAP.
The Principal Component Regression (PCR) method was constructed and applied to the whole spectral range with data points collected from each wavelength. The results show large values for % Variance (R squared) as listed in Table 4.2.
Model Property % Variance Std Error of Estimate ESS
HAP/ COM HAP 99.8 1.19
COM 100.00 0.015
COM/CaCO3 COM 100.00 0.013
CaCO3 98.87 2.17
Table 4.2: Figures of merit for constructed PCR models.
The % variance for the primary principal components in PCA model is not calculated as in LLSR. In LLSR, % Variance is equal to 1 minus the ratio of sum of squared regression
66 and the sum of squared total while, % Variance in PCA can be calculated as in equation
2 n c a c a 1. % Variance i i i i 100% ……………. Equation 1 2 2 2 2 nci ci nai ai
Where ci is the ith score in the primary PC, and ai is the corresponding absorbance.
Standard error of estimate ESS was calculated as in the following Equation:
k 1 a a 2 j j i j ij,est ………………………………………… Equation 2 n k 1
Where n is the number of data points in the model, k is the number of PC component in the model, aij is the actual absorbance for the ith score in the jth PC, aij,est is the estimated absorbance for the ith score in the jth PC, and 휆푗is the weight of the jth PC.
The predictive ability of the PCR models was assessed using test samples of known concentration.
The prediction accuracy was calculated according to Equation
Cact. C pred %prediction accuracy 100%……………………………..Equation 3 Cact
Where Cact. is the actual concentration of the analyte, and Cpred. is the predicted concentration using the PCR model. The prediction results were listed in Table 4.3-4.4.
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Actual Predicted %Prediction Actual Predicted %Prediction Conc. of Conc. of accuracy Conc. Conc. of accuracy
CaCO3 CaCO3 of COM COM
20* 19.01 4.94 80 80.98 1.23
40 39.26 1.84 60 60.73 1.23
60 58.18 3.02 40 41.81 4.54
80 81.40 1.75 20 18.59 7.00
Table 4.3: Prediction test of COM/CaCO3PCR model. *Three pellets were prepared for each mixture with average weight of 200mg
Actual Predicted %Prediction Actual Predicted %Prediction Conc. of Conc. of accuracy Conc. of Conc. of accuracy HAP HAP COM COM
20* 19.01 4.93 80 80.98 1.23
40 40.59 1.48 60 59.40 0.98
60 59.89 0.18 40 40.10 0.27
80 78.88 1.39 20 21.11 5.57
Table 4.4: Prediction test of HAP/COM PCR model.
* Three pellets were prepared for each mixture with average weight of 200mg.
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4.4.1 Application of PCR model into quantitative analysis of renal stone components
The validated PCR model of HAP/COM mixture was applied for quantitative analysis of kidney stone components. Figure 4.4 shows the ATR images of two cross sectioned kidney stones. The average spectra over the entire image reveal the presence of hydroxylapatite HAP and calcium oxalate monohydrate COM (Figures 4.5-4.6). The results of PCR prediction were compared to micro computed tomography (micro CT) analysis of the same kidney stones (Table 4.5). Micro computed tomography (micro CT) was chosen for comparison with ATR technique since the method allows nondestructive analysis of intact stones that enables further analysis with other techniques. Also, it can provide quantitative information about the mineral contents based on X-ray attenuation. It is noticeable that the results of both types of analysis were comparable. Thus, ATR technique in conjunction with PCR models can be potentially used for quantitative analysis of mixed stone components. This quantitative information could help the pathologists to study the initial mechanism of stone formation and their subsequent growth.
Figure 4.4: ATR images of kidney stone #130692 (Left) and kidney stone# 129182 (Right).
69
94
90
85
80
75
70
%T
65
60
55
49 4000 3500 3000 2500 2000 1500 1000 748 cm-1 Figure 4.5: The extracted averaged ATR spectra from kidney stone #130692.
104 100
90
80
70
%T 60
50
40
30
23 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 4.6: The extracted averaged ATR spectra from kidney stone# 129182.
Kidney stones Micro CT PCR
#130692 %COM 70 74.71
%HAP 30 25.28
#129182 %COM 89 87.12
%HAP 11 12.86
Table 4.5: Comparison of the analysis of two mixed stone components using HAP/COM PCR model and Micro CT.
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4.5 Conclusion
Combination of ATR-FTIR and PCR model was successfully applied for quantitative analysis for mixed renal stones. The results were compared to micro computed tomography (micro CT) analysis. This approach is considered to be reliable and suitable for routine use in clinical laboratories. Moreover, it requires minimum sample preparation and the PCR models show good prediction accuracy and enable accurate quantitative analysis of stone components. Furthermore, HAP /COM PCR model can be useful for quantitative analysis of Randall’s plaque components in idiopathic calcium stone formers which is mainly composed of mixed inclusions of COM and HAP. This could help in studying the mechanism of the plaque formation on renal papilla and determination of the etiology of the disease. The method can be also applied for analysis of complex stones of ternary and quaternary components.
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9. A. P. Evan, J. E. Lingeman, F. L. Coe, E. M. Worcester, Kidney Int.,69, 1313, (2006).
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11. O. Planinsek, D. Planinsek, A. Zega, M. Breznik, and S. Srcic, Int. J. Pharm.,319, 13, (2006).
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16. C. Y. Goh, W. V. Bronswijk, C. Priddis, Appl. Spectrosc., 62(6), 640-648, (2008)
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17. C. Ling, Capabilities, Limitations and Applications of ATR-FTIR Imaging" Miami Ph.D. Thesis, (2014).
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19. B. Keshavarzi, N. Y. Ashayeri, F. Moore, Minerals, 6, 131, (2016).
20. R. Gilad, J. C. Williams Jr., K. D. Usman, R. Holland, J. Nephrol., 30, 1,135–140, (2017).
21. S. Charafi1, M. Mbarki, A. Costa-Bauza, R. M. Prieto, Int. J Nephrol. Urol., 2 (3): 469-475, (2010).
22. G. P. Kasidas, C.T. Samuell & T. B. Weir, Ann. Clin. Biochem., 41, 91-97, (2004).
23. D. C. Nambiar and V. M. Shinde, Inorg. Anal. Chem., 68(8), 2277-2279, (1995).
24. J. Cloutier, L. Villa, O.Traxer, & M. Daudon , World J. Urol., 33(2), 157-69, (2014).
25. V. Uvarov, I. Popov, N. Shapur, T. Abdin, O. N. Gofrit, D. Pode, M. Duvdevani, Environ. Geochem. Health, 33, 613-622, (2011).
26. C. A. Zarse, J. A. McAteer, A. J. Sommer, BMC Urol.; 4(1):15, (2004).
27. M. Daudon, D. Bazin, C. R. Chim. , 19, 11–12, 1416-1423, (2016).
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Chapter 5
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Differentiating Spatial Resolution and Detection Limits in Molecular (infrared) Microspectroscopy*
5.1 Abstract
Infrared ATR microspectroscopy has become a powerful analytical tool employed in a wide variety of scientific disciplines. The spatial resolution of attenuated total internal reflection (ATR) imaging with germanium (n = 4.0) crystal is maximized by a factor of four compared to reflectance measurements or by a factor of two for transmission measurements. There is often some confusion about the inter-relationship between the spectral and spatial resolution and how they affect the imaging of a particular sample. Thus, the purpose of this work is to study the spectral and spatial resolution and to differentiate the spatial resolution and detection limits of ATR technique and infrared microspectroscopy in general. The study of spectral and spatial resolution was performed using two models. The first model is a one dimensional system using polymer laminates. The polymer laminates included a relatively thick Nylon 6/6 substrate while the second laminate used a polyethylene terephthalate (PET) substrate. Both substrates were coated with a thin layer of polyvinyl acetate (PVA). The second model is a two dimensional system using poly methyl methacrylate (PMMA) microspheres embedded in poly vinyl alcohol (PVOH) films. The feasibility of quantitative analysis was also investigated in this study using GRAMS/ AI and PCR models.
5.2 Introduction
Infrared microspectroscopy has been applied for the analysis of biological samples for several years. Today, infrared microspectroscopy enables researchers to investigate not only bulk tissues, but also individual cells and cellular components. 1-5
*A brief of this chapter has been published in Volume 24, Supplement 51 (Proceedings of Microscopy & Microanalysis) in Microscopy and Microanalysis Journal, (2018),pp:1396-1397.
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The attenuated total internal reflection (ATR) technique has significant advantages over other conventional infrared microspectroscopic sampling techniques (reflection and transmission) in terms of little required sample preparation and improved spatial resolution.6 Therefore, there has been a growing interest of utilizing (ATR) infrared technique for biomedical applications and cellular investigations in recent years. 7-10 In ATR, the sample is immersed in a medium of high refractive index such as a germanium internal reflection element (IRE). Therefore, the diffraction-limited spot size at the microscope’s focus point is decreased by factor equal to the refractive index of the crystal. This reduction improves the spatial resolution and enables more information and fine details to be obtained on spatial domains below the wavelength of light employed.11- 12 However, the spatial resolution could limit the spectral purity of the spectra extracted from a single spot particularly when imaging cellular components or highly thin laminate layers that are smaller than the wavelength of the infrared radiation.13
5.2.1 Factors affecting the spatial resolution of infrared microspectroscopy
5.2.1.1 Diffraction
The spatial resolution of any optical microscope is fundamentally limited by the diffraction of light. When monochromatic radiation of a point source passes through a circular aperture, the light tends to spread out giving series of concentric rings of decreasing intensity at the beam focus (Airy disk or diffraction pattern). The net effect of diffraction is to blur and deteriorate the resolution of detected images 14 .This diffraction limit can be quantified by Rayleigh criterion thus, the resolution of images can be calculated by
푟 = 0.61 휆/푁.퐴. …………………………………………………………..Equation 1
Where “r” is the radius of the diffraction pattern, N.A. is the convergence angle of the beam or the numerical aperture, defined as: 푁. 퐴. = 푛1 sin 휃 where n1 is the refractive index of the medium in which the sample is immersed and θ is the incident angle of the radiation.
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Two objects start to be resolved if they are separated by” r” 15-16. The ATR imaging accessory utilizes a high-refractive index material (Germanium) (n1 = 4.0) for conducting the measurements, this has the effect of improving the spatial resolution and reducing the effects of diffraction of infrared microspectroscopy by a considerable factor of 4 12. One major problem is that new practitioners to the field and those outside the field view the diffraction limit value as an absolute beyond which structures smaller than this dimension cannot be observed. The source for the signal in infrared microspectroscopy is the absorption of light whose magnitude is proportional to the path length of light through the sample and, more importantly, the extinction coefficient of the sample.17 In an ATR measurement the path length is constant at a given wavelength so the extinction coefficient is most important. Detection of features smaller than the Rayleigh criterion is possible provided the spectral resolution of the measurement is sufficient. The capabilities of ATR-FTIR imaging beyond diffraction limit have been studied in this laboratory. In 2010, Gulley-Stahl et al. demonstrated using ATR-FTIR imaging to detect submicron particles down to 0.6 µm in diameter 18.
5.2.1.2 Spectral resolution
ATR microspectroscopy affords the analyst the ability to detect smaller areas of the sample with pixel resolution of 1.56 m. This capability arises from the high refractive index of the germanium hemispherical IRE (n=4) which increases the magnification approximately fourfold compared to reflection imaging 12. It is believed that it is impossible to detect features in the sample which are smaller than the detector element size but in fact the pixel resolution has no indication of the inherent spatial resolution of the method. When the sample size is smaller than the pixel size, the spectral purity will be degraded with overlap from the surrounding area, but the sample can still be detected because the signal arises from the light absorption of chemical species in the sample as mentioned earlier. To achieve spectral purity and hence better interpretation of the sample spectra, the sample should be of size at least 3 times the pixel resolution of the system, thus each pixel will consist of a pure spectrum 19-20.
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5.2.2 Measuring ATR resolution
Spatial resolution for conventional imaging systems is typically estimated using the standard Airforce target (a blade or chrome on glass substrate). However, this method is inappropriate for measuring the inherent spatial resolution of the microscope since the sample has high contrast edges.20 For ATR spatial resolution measurement; the material must be in intimate contact with IRE and have a mid-IR absorption. An alternative method employed for evaluation of the spatial resolution is to obtain the edge response function using cross sectioned polymeric films with a sharply defined edge brought to the focus of the microscope. The resolution of the microscope is measured by monitoring the intensity of certain absorption as function of position. The width of the edge response signal is equivalent to 2X the resolution of the microscope. 21-22 In 2001, Tisinger et al. measured the spatial resolution of ATR microscope with focal plane array detector using a cross-sectioned photographic film consisting of a polyvinyl acetate (PVA) layer and dye layer of gelatin. The edge functions were constructed using the peak height for the gelatin’s amide I absorption at 1650 cm-1 and PVA’s carbonyl absorption at 1740 cm-1 plotted against displacement from the origin of the hemisphere. The interface between the two layers of the film was used to generate the edge function from which the spatial resolution could be evaluated. The determined spatial resolution was close to the diffraction limit of the microscope (~4µm for light of 5.8 μm wavelength). 12In 2010, Gulley-Stahl et al. determined the spatial resolution of a Perkin Elmer Spotlight 300 ATR microscope as 3 μm (6 micormeter wavelength) through a plot of point spread function (PSF) or the distribution of the focused beam for the annular aperture present in a reflecting objective. 21
5.2.3 Determination of detection limit of ATR technique
Previous investigations have shown that ATR-FTIR microspectroscopic imaging has several advantages over other IR sampling method, including an improved spatial and volumetric resolution as well as elimination of deleterious spectral competing processes. In order to promote this technique into biomedical applications, it is necessary to explore the ultimate detection limit of this technique. Submicron particles were used for determination of the detection limit of ATR technique. In this case the ability to detect
79 these particles is dependent more so on a volume effect rather than path length since the diameter of the spheres is less than the penetration depth of the evanescent wave. The ATR sampling volume can be estimated as a cone, with the bottom diameter of beam width and height of 3 times penetration depth (actual penetration depth), as shown in Figure 5.1. Assuming one particle is included in this cone and detected, the volume of the bead “v” is equal to 4/3 πr3, where “r” is the radius of the particle. The sampling volume can be calculated by:
2 1 d V 3d p …………………………………………………Equation 2 3 2
Where d is the beam width at the microscope focus and dp is the penetration depth.
Figure 5.1: ATR Sampling Volume.
Gulley-Stahl et al. demonstrated the ability of ATR imaging to detect submicron particles including 1 µm polymethyl methacrylate (PMMA) particles corresponds to 28 parts-per- thousand (ppt) a detection limit and 0.6 µm particles of polystyrene embedded on PVOH polyvinyl alcohol substrate which corresponds detection limit of 10 ppt.18 In 2014, Chen et al. continued the investigation and achieved detection limit of approximately 130 ppm corresponding to 0.1 μm PMMA spherical bead.13 This work differs from previous investigations in that principal component analysis (PCA) and Compare Correlation were utilized to better visualize the microsphere.
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5.3 Experimental
5.3.1 Materials and methods
5.3.1.1. Preparation of polymer laminates
Nylon 6/6 film of thickness 0.2 mm and polyethylene terephthalate (PET) film of thickness 0.1 mm were purchased from CS Hyde Company, Inc. ( Lakevilla,IL). Poly vinyl acetate PVA was obtained from Sigma Aldrich Chemical Company, Inc. PVA solutions (2 mM) were prepared in acetone and a dye was added to the solution such that its thickness could be measured optically. The polymer laminates were prepared by coating Nylon 6/6 substrate and PET substrate with a thin layer of polyvinyl acetate (PVA). Then, the laminate stripe was embedded in epoxy resin. Specimen blocks were allowed to cure at room temperature overnight. The dried samples were cross sectioned using a Reichert Ultracut S Ultramicrotome, Leica with a speed of 3 mm/second. Glass knives were prepared using LKB Bromma 7800 knifemaker.
5.3.1.2 Preparation of polymer microspheres
The poly methyl methacrylate (PMMA) microspheres in suspension were purchased from Phosphorex, Inc. (Hopkinton, MA). Polyvinyl alcohol PVOH was obtained from Aldrich Chemical Company, Inc. PVOH/H2O (5 % w/v) solution was prepared at 80 °C. An EC101DT Digital Photo Resist Spinner from Headway Research, Inc. was used to spin coat the PVOH film onto a low-e slide (Kevley Technologies, Chesterland, OH). The 1inch x 1inch substrate was mounted on the spinner and 2mL of PVOH solution was pipetted on the center of the substrate at 2500 RMP. Then, 10 μL of diluted PMMA suspension was pipetted onto the film substrate.
5.3.1.3 Preparation of ester mixtures
Diethyl sebacate DES and Dibutyl phthalate DBP were purchased from Sigma Aldrich Chemical Company, Inc. Binary mixtures of esters were prepared ranging from 0 to 100% DES or DBP (including 0, 10, 30, 50, 70, 90, 100 v%). The total volumes for all standards were 200 L. The samples were mixed in a Fisher Scientific plate shaker at ~60
81 rpm for 30 minutes to ensure proper mixing. Series of test mixtures varied from 20 v% to 80 v% of each component of DES/DBP were prepared with the same protocol.
5.3.2 Instrumentation
ATR spectra were collected with a Harrick Split-pea interfaced to a Perkin Elmer Frontier Fourier transform infrared spectrometer using Germanium internal reflection element (IRE) and the standard deuterium triglycine sulfate (DTGS) detector. For each spectrum, 32 individual scans were obtained from 4000-400cm-1 with a spectral resolution of 4 cm-1. The Perkin Elmer Quant software package was used for construction PCR models. Band fitting of the peaks was performed using GRAMS/ AI (7.02) software. Infrared images were obtained using a Perkin Elmer Spectrum Spotlight infrared imaging microscope equipped with Germanium hemisphere and a liquid nitrogen cooled HgCdTe linear array (MCT) detector. The imaging area was 400 x 400 µm2 with spectral resolution 8cm-1 and pixel size 1.56 µm. Principal Components Analysis (PCA) was used to enhances the IR image contrast and extract the spectra.
5.4 Results and discussion
5.4.1 Differentiation of spatial Resolution and detection Limits
The spatial resolution and detection limit of ATR microspectroscopy were studied using one-dimensional structure (polymer laminates) and two-dimensional structures (polymeric microspheres). The investigated polymer laminates and microspheres have layers or microsphere with dimensions less than that dictated by the Rayleigh criterion. The results confirmed that it is possible to detect structures below the diffraction limit and the detection of still smaller features is dependent on the selectivity of the method and the strength of the extinction coefficients. A principal component analysis (PCA) was conducted on all recorded infrared images to enhance the detection of laminate layers and microspheres. PCA is one of the most useful chemometric techniques that have been used to provide image contrast. This statistical multivariate method sorts the image spectra into an independent set of sub spectra (principal components) from which the image spectra can be reconstructed. The amounts of the principal components (scores) in the original image spectra are determined at each pixel, and the resulting score images are
82 useful in enhancing image contrast allowing detection of subtle features in the image. PCA can distinguish physical and morphological details in the sample, as well as chemical differences by reducing the number of variables and making the principal components more outstanding23. A compare correlation is another regression analysis used to enhance the image contrast especially in case of trace materials. Since it reveals the areas where certain material is most prevalent provided its reference spectrum is known.
5.4.1.1 ATR-FTIR Imaging of polymer laminates
The studied polymer laminates include PVA/Nylon and PVA/ PET laminates. Based on the carbonyl absorption of the laminate components at 1700 wavenumbers (~5.9 micrometers) the theoretical spatial resolution at this wavelength would be 3 micrometers. Thus the novice or new practitioners to infrared microspectroscopy would say that features smaller than 3 micrometers could not be detected. However, this is not the case. Figure 5.2 illustrates optical and infrared images of the PVA/Nylon laminate with PVA layer of 1.2 micrometers in thickness. The Nylon layer appears as red, PVA as blue and the epoxy resin as green. The PVA layer is clearly detected. The absorption strength (extinction coefficient) of PVA relative to Nylon is approximately the same. Figure 5.3 illustrates the infrared absorption spectra of Nylon and PVA. A comparison of the spectra shows that that they are significantly different where the nylon carbonyl absorption is located at 1650 cm-1 while the carbonyl absorption for PVA occurs at 1733 cm-1. As such the selectivity for a given layer is high. Figure 5.4 shows similar images in which the PVA layer is 0.8 m in thickness. The PVA layer is again clearly detected but is approaching a limit.
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Figure 5.2: Optical image of the cross-section (left) of Nylon/PVA laminate with 1.2 micrometer PVA layer, ATR-infrared image (right).
0.28 0.24 0.22 0.20 0.16
A 0.14 0.12 0.08 0.06 0.04 0.02 0.49 0.40 0.35 0.30 0.25 A 0.20 0.15 0.10 0.05 -0.00 -0.02 4000 3500 3000 2500 2000 1500 1000 748 cm-1 Figure 5.3: Infrared spectra of Nylon (Top) and PVA (Bottom).
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0.8 micrometers
Figure 5.4: Optical image of the cross-section of Nylon/PVA laminate with 0.8 micrometer PVA layer (left), ATR-infrared image (right).
Figures 5.5 and 5.6 show similar results but in this case the laminates are composed of PVA on PET. The PET layer is colored red with PVA blue and epoxy green. The absorption strength of PVA relative to PET is approximately 1.5. A comparison of the spectra illustrated in Figure 5.7 shows that they exhibit very similar absorption features. The ATR images allow one to detect both the 1.6 micrometer thick layer as well as the 0.8 micrometer thick layer. However, the use of PCA allows better selectivity for those cases in which the spectra of the individual layers are more similar.
Figure 5.5: Optical image of the cross-section of PET/PVA laminate with 1.6 micrometer PVA layer (left), ATR-infrared image (right).
85
Figure 5.6: Optical image of the cross-section of PET/PVA laminate with 0.8 micrometer PVA layer (left) and ATR-infrared image (right).
0.58 0.50 0.45 0.40 0.30
A 0.25 0.20 0.10 0.05 0.00 -0.05 0.49 0.40 0.35 0.30 0.25 A 0.20 0.15 0.10 0.05 -0.00 -0.02 4000 3500 3000 2500 2000 1500 1000 748 cm-1 Figure 5.7: Infrared spectra of PET (Top) and PVA (Bottom).
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5.4.1.2 ATR-FTIR Imaging of submicron particles
The capability of ATR-FTIR Imaging for detecting structures of dimensions smaller than the theoretical spatial resolution was also proved using a 2D system consisting of 1 micrometer diameter PMMA microspheres embedded in a film of PVOH. The PCA analysis and Compare Correlation were applied to better visualize these microsphere. Figure 5.8 illustrates an optical image, an infrared PCA score image and a regression infrared image of the area sampled. The box within each image relates the area sampled between the optical and infrared images. Both the PCA and the Compare Correlation regression analysis are effective in detecting the microspheres.
10
Figure 5.8: Optical image (left), PCA image (middle) and regression image (right) of 1 micrometer PMMA microspheres in PVOH. Figure 5.9 illustrates reference infrared spectra of PVOH, PMMA and that of a 1 micrometer sphere extracted from the above infrared image. The spectrum extracted from the image exhibits absorptions characteristic mostly for PVOH but the feature located at 1725 cm-1 is the carbonyl absorption of PMMA due to the higher extinction coefficient of PMMA than that of PVOH at this wavelength and the selectivity of infrared microspectroscopy. Based on the noise in the spectrum and the peak height of this feature a detection limit for PMMA in PVOH can be determined. This detection limit corresponds to a PMMA microsphere of diameter 0.1 micrometers. As is seen in the current example, beyond the diffraction limit, spectra originating from a single pixel will be mixtures of the sample of interest and the surrounding matrix.
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PMMA signature
Figure 5.9: Infrared spectra of PVOH (bottom), PMMA (middle) and 1 micrometer PMMA microsphere extracted from the infrared image above (top).
5.4.2 Application of quantitative analysis
In infrared microspectroscopy, spectral purity could be degraded beyond the diffraction limit and spectral absorptions that lie in close proximity to each other will overlap to form unresolved broad peaks. Diethyl sebacate (DES) and Dibutyl phthalate (DBP) ester mixtures were employed to investigate the use of available softwares to resolve overlapped peaks and determine the percentage composition of each in a given spectrum (Figure 5.10). The carbonyl stretching bands of DES and DBP are located at 1733 cm -1 and 17 22 cm-1 respectively while that for the (50:50 V%) mixture of the two esters consist of one broad band at 1727 cm-1 (Figure 5.11 ).
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0.29 0.24 0.22 0.18 0.16
A 0.14 0.10 0.08 0.06 0.03 0.27 0.24 0.22 0.18 0.16
A 0.14 0.12 0.08 0.06 0.04 0.02 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 5.10: ATR spectra of Dibutyl phthalate DBP (top) and Diethyl sebacate DES (bottom).
0.29 0.20
A 0.15 0.10 0.03 0.27 0.20 0.15 A 0.10 0.05 0.02 0.25 0.20 0.15 A 0.10 0.05 0.03 2000 1900 1800 1727.111700 1600 1500 cm-1
Figure 5.11: The carbonyl absorption of Dibutyl phthalate DBP (top), Diethyl sebacate DES (middle), and a (50:50 V%) mixture (bottom).
In order to determine integrated peak intensity of each ester in the mixture, GRAMS/AI Peak Fitting was applied. Peak fitting requires the analyst to know the expected number of peaks, an approximate full width at half maxima (FWHM), and approximate location for the peaks of interest 24-26. Then, the program uses different
89 algorithms to estimate the peak height and areas required to produce the final spectrum. The goodness-of-fit is described as follows:
2 n Actual Calculated i i RMSnoise Χ 2 i0 …………………………………Equation 3 n f
The Actual and Calculated values are the measured and calculated data, respectively. The RMS noise is the estimated Root Mean Squared noise in the Actual data over the fitted region. The variable n is the number of data points in the fitted region and f is the total number of variables from all the peak and baseline functions.
There are two main functions for peak fitting based on the peak shape: Gaussian for symmetric peaks and Log normal function for tailed peaks .Since, the ATR spectrum of the ester mixture show asymmetrical carbonyl band, log normal function peak fitting was applied. Log normal function peak fitting requires to provide half width ratio (asymmetry index) (B/A) as well as peak width and location.27,28 Figure 5.12 shows the output of band fitting of 1727cm-1 band of DES/ DBP mixture of ratio (50:50 v %). The average integrated peak areas for DES and DBP were 2.9 and 3.1 respectively. The concentration ratio of mixture components was calculated using the following equation:
Conc.a Aa As,b ……………………………………………………...Equation 4 Conc.b Ab As,a
Where, A is the peak area of “a “and “b” in the mixture and As is the peak area of standard solution of “a “and “b”.
The provided peak parameters and fit results are presented in Table 5.1 and 5.2. The results demonstrate that the model shows good correlation coefficient R2 and small value of goodness of fit, χ 2 which indicate that the mathematically predicted peaks are in good agreement with the actual spectrum.
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Peaks Center (cm-1) FWHM (cm-1) Half width ratio Area % Conc.
DES 1733 21 0.8 2.9 51.5
DBP 1722 27 0.7 3.1 48.5
Table 5.1: Peak parameters of DES and DBP for Log normal GRAMS/AI model.
Reduced Chi2 45769
RMS Noise: 0.000029
Correlation R2: 0.99
Standard Error: 0.006
Table 5.2: Fit statistics of Log normal GRAMS/AI model of DES/ DBP mixture of ratio (50:50 v %).
91
Original Trace + Fitted Trace + Residual + Peaks + Baseline + 2nd Derivative
.25
.2
.15
Absorbance
.1
.05
File: 50 sebacate 50 phthalate.SPC 1850 1800 1750 1700 1650 Wavenumber (cm-1)
Figure 5.12 : The output of band fitting of 1727cm-1 band of DES/ DBP mixture of ratio (50:50 v %).
Transmission spectra were also collected for the esters binary mixtures (Figure 5.13). It is noticeable that the carbonyl bands of the esters are more symmetric compared to ATR spectra however, observation of the spectrum of the mixture shows that the carbonyl absorption also gives rise to a broad peak located at 1734cm-1 (Figure 5.14). Thus, Gaussian curve-fitting was applied to this peak to determine the integrated area of each ester (Figure 5.15). The parameters of the model and the results of band fitting are listed in Table 5.3 and 5.4.
92
0.7 0.6 0.5 0.4
A 0.3 0.2 0.1
-0.0 0.55 0.45 0.40 0.35
A 0.25 0.20 0.15 0.10 -0.00 -0.03 4000 3500 3000 2500 2000 1500 1000 750 cm-1 Figure 5.13: Transmission spectra of Dibutyl phthalate DBP (top) and Diethyl sebacate DES (bottom).
0.7
0.4
A 0.2 -0.0 0.5
0.3 A 0.2 0.1 -0.0 1.1
0.6 A 0.4 0.2 -0.1 2000 1900 1800 1733.811700 1600 1500 cm-1
Figure 5.14: The carbonyl absorption of Dibutyl phthalate DBP (top), Diethyl sebacate DES (middle), and their mixture (bottom).
93
Original Trace + Fitted Trace + Residual + Peaks + Baseline + 2nd Derivative
.25
.2
.15
.1
Absorbance
.05
0
File: final s sebacate 50.SPC 1800 1750 1700 1650 Wavenumber (cm-1)
Figure 5.15 : The output of band fitting of 1734cm-1 band of DES/ DBP mixture of ratio (50:50 v %).
Peaks Center(cm-1) FWHM(cm-1) Area %Conc.
DES 1737.12 17 3.14 54.5
DPS 1727.87 23 3.29 45.5
Table 5.3: Peak parameters of DES and DBP for Gaussian GRAMS/AI model.
Reduced Chi2 18760.73
RMS Noise: 0.000043
Correlation R2: 0.99
Standard Error: 0.005
Table 5.4: Fit statistics of Gaussian GRAMS/AI model of DES/ DBP mixture of ratio (50:50 v %).
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The Principal Component Regression (PCR) model was constructed using ATR spectra of the esters .The prediction assessment of the model was compared to the GRAMS/AI peak fitting (Table 5.5). The results show that GRAMS/AI peak fitting has better prediction accuracy than PCR model and the prediction accuracy of both GRAMS/AI peak fitting and PCR is degraded in case of low concentrations.
Actual Prediction Prediction Actual Prediction Prediction conc.of accuracy accuracy conc.of accuracy accuracy DBP(%) (GRMS/AI DES(%) (GRMS/AI (PCR (PCR model) model) model) model)
10 11.86 12.28 90 1.08 1.43
20 10.07 7.35 80 2.2 1.7
40 4.6 7.7 60 1.19 1.98
50 3.2 4.75 50 3.4 3.93
60 1.78 2.7 40 4.01 8.24
80 1.03 0.93 20 7.1 10.5
Table 5.5: Comparison of prediction accuracy of GRAMS/AI and PCR models for assessment of concentration of different mixtures of DES/ DBP mixtures.
Thus, GRAMS/AI peak fitting can afford a valuable estimation of integrated peak intensity of specific bands in complex and unresolved spectra and it can be effectively applied in infrared microspectroscopy for band fitting in case of spectra extracted from single pixel or spectra from structures or layers of spatial domains below the diffraction limit. GRAMS/AI peak fitting with log normal function was successfully applied to resolve the spectra of the studied polymer laminate of PET /PVA (Figure 5.6). Since the
95
PVA layer (0.8 micrometer) is less than the pixel size of the system (1.56 micrometer), the spectrum extracted from PVA layer is contaminated with spectral features from the neighboring layer PET. GRAMS/AI peak fitting was employed to resolve the overlapped carbonyl band at 1722 cm-1 (Figure 5.16). The peak parameters and fit statistics of PET and PVA model are listed in Table 5.6-5.7. The results of the model show that the percentage of spectral overlapping from PET layer is 15% (Figure 5.17).
0.6 0.4
A 0.2
-0.1 0.5
0.3 A 0.2 0.1 -0.0 0.5 0.3
A 0.2 0.1 -0.0 2000 1900 1800 1722.541700 1600 1500 cm-1
Figure 5.16: The carbonyl absorption of PET (top), PVA (middle), and the PVA laminate layer of thickness 0.8 micrometers (bottom).
Peaks Center (cm-1) FWHM (cm-1) Half width ratio Area %Conc.
PET 1712 28 0.7 2 15
PVA 1730 32 0.73 5.7 85
Table 5.6: Peak parameters of PET and PVA for Log normal GRAMS/AI model.
96
2 Reduced Chi 46311.5
RMS Noise: 0.000092
2 Correlation R : 0.99
Standard Error: 0.006
Table 5.7: Fit statistics of Log normal GRAMS/AI model of PET/ PVA.
Original Trace + Fitted Trace + Residual + Peaks + Baseline + 2nd Derivative
.2
.15
.1
Absorbance
.05
0
File: interface image.SPC 1850 1800 1750 1700 1650 Wavenumber (cm-1)
Figure 5.17: The output of band fitting of 1722 cm-1 band of PET/PVA.
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5.5 Conclusion
This study demonstrates an investigation of spatial resolution and detection limits of ATR microspectroscopy. In this investigation; it was demonstrated that ATR microspectroscopy can detect structures beyond the theoretical diffraction limit. GRAMS/AI peak fitting is considered to be a reliable methodology to resolve and quantify specific peaks in case of overlapped peaks extracted for images of structures beyond the Rayleigh criterion. Principal component analysis PCA and compare correlation function have been also demonstrated to enhance the image contrast allowing better visualization of sub visible particulates. The detection limit of the method was estimated to be 0.1 μm PMMA microsphere thus; ATR imaging approach can be potentially introduced for future application in protein therapeutics analysis, cytological examination in clinical laboratories, environmental management and forensic science.
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References
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3. A. J. Sommer, L. G. Tisinger, C. Marott, and G. M. Story, Appl. Spectrosc., 55(3), 252-256, (2001).
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6. H. J. Gulley-Stahl, S. B. Bledsoe, A. P. Evan, and A. J. Sommer, Appl. Spectrosc., 64(1), 15-22, (2010).
7. K. L. Andrew Chan, S. G. Kazarian, Chem. Soc. Rev., 7, 45(7):1850-1864, (2016).
8. T. P. Wrobel, K. M. Marzec, K. Majzner, K. Kochan, M. Bartus, S. Chlopicki, M. Baranska., Analyst. 21; 137(18):4135-9, (2012).
9. B. Balázs, G. Farkas, O. Berkesi, R. Gyulai, et al., J. Microchemical, 117 , 183- 186, (2014).
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11. H. J. Gulley-Stahl, S. B. Bledsoe A. P. Evan & A. J. Sommer, Appl. Spectrosc., 64, 15-22, (2010).
12. A. J. Sommer, L. Tisinger, C. Marcott, and G. M. Story, Appl. Spectrosc., 55, 252-256, (2001).
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13. C. Ling “Capabilities, Limitations and Applications of ATR-FTIR Imaging” Miami Ph.D. Thesis, (2014)
14. A. J. Sommer and J. E. Katon, Appl. Spectorsc., 45(10), 1633-1640, (1991).
15. A. J. Sommer, “Handbook of Vibrational Spectroscopy, J.M. Chalmers and P.R. Griffiths, Ed. (Wiley, NY), p. 1369- 1371, (2002).
16. C. Ling, A. J. Sommer, Microsc. Microanal.; 21(3):626-36, (2015).
17. L. G. Tisinger, Dissertation: Investigations in quantitative infrared using attenuated total reflectance: Doctorate dissertation, (2002).
18. H. J. Gulley-Stahl, Chapter 4, “An Investigation into Quantitative ATR-FTIR imaging and Raman Microspectroscopy of Small Mineral Inclusions in Kidney Biopsies” Thesis, (2010).
19. A. Canas, R. Carter, R. Hoult, J. Sellors, S. Williams, Spatial resolution in mid-IR ATR imaging: measurement and meaning. FACCS Conference, FL, (2006).
20. ‘Spatial Resolution in FT-IR ATR Imaging’ Perkin Elmer Technical Note # 007641_03 (2006).
21. H. J. Gulley-Stahl, A. J. Sommer,“Evanescent Wave Imaging” in Vibrational Spectroscopic Imaging for Biomedical Applications, Srinivasan, G. (Ed.), chapter 4, p. 110–111, McGraw Hill Professional, (2010).
22. J. Sellors, Application Notes of “ATR Imaging of Laminates”, Perkin Elmer Life and Analytical Sciences, Seer Green, UK.
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25. J. C. Anderson, “Quantitative and qualitative investigations into urinarycalculi using infrared microspectroscopy” Miami. Thesis, (2007).
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26. K. Axe, M. Vejgarden, and P. Persson, J. Colloid Interface Sci., 294(1): p. 31-37, (2006).
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Chapter 6
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Application of ATR Imaging in Quantitative Analysis of Pharmaceuticals in Their Dosage Form
6.1 Abstract
Recently, Attenuated Total Reflectance Fourier Transform Infrared ATR-FTIR imaging has many distinct applications in the field of pharmaceuticals including detection of counterfeit drugs, study of dissolution of pharmaceutical formulations, and drug release. In this work the feasibility of ATR-FTIR technique for quantitative analysis of pharmaceuticals was investigated on two different tablet formulations. The quantitation of active ingredients in this study is based on image analysis using Principal Component analysis PCA and Image J software. The main advantage of this method is that it enables fast determination of the components in mixtures without need of prior separation steps. The results demonstrate the capability of ATR-FTIR imaging for use as a quick and simple tool to get information about the relative concentration of each active ingredient in drug formulations.
6.2 Introduction
Infrared spectroscopy has long been used as a means to elucidate molecular structural information from a wide variety of materials. 1,2 While the technique is primarily used as a qualitative method for identification, it can under controlled conditions, be employed for quantitative analysis. One of the most important control parameters is the path length of light through the sample. For solid samples the path length should be no greater than 6 micrometers otherwise the absorption of light is too great rendering the method useless for quantitative purposes.3 This thickness dictates extended sample preparation which requires expertise. In addition, many samples due to their structure or fragility can affect the results of the analysis or prevent a sample from being properly prepared.
A solution to this problem is attenuated total internal reflection (ATR) infrared analysis where the sample is placed in contact with a high index infrared transmitting crystal (IRE). The path length of light through the sample is dependent on the refractive index of the IRE and that of the sample. In most cases the path length, or the penetration
103 depth, is typically less than 1 micrometer. The reduced path length means that highly absorbing samples can be analyzed and since the method is a contact method, little if any sample preparation is required.4 When coupled to a microscope an additional benefit results in that the IRE serves as an immersion lens. This lens significantly improves the spatial resolution of the measurements.5 ATR-FTIR imaging has dramatically changed the manner in which molecular imaging is conducted and has been extremely useful in the study of molecular spatial distributions important for disease detection, pharmaceutical formulations and industrial processing. Because the control of path length is less problematic with this method, it can be potentially used for quantitative analysis. 6-9
6.2.1 Application of ATR technique in pharmaceutical analysis
There has been a growing interest in chemical imaging of pharmaceuticals as a means of integrating the chemical selectivity of vibrational spectroscopic methods with the power of imaging to attain information about ingredients distributions in different dosage forms and counterfeit product detection. Chemical imaging techniques employed for this purpose include near infrared (NIR) imaging, mid-IR diffuse reflectance, and Raman mapping.10-16 However, each technique has some limitations that hinder its use as comprehensive method for characterization of both excipients and active ingredients. NIR imaging has been limited because spectra involve broad overtone and combination bands, which make them difficult for interpretation and identification of active ingredients and excipients in complex tablet matrixes.17 Diffuse reflectance imaging has problems of high signal attenuation and uncontrolled specular reflectance contributions in the image.18 The limitations of Raman mapping include sample overheating, potential fluorescence, limited spatial resolution , small sample areas to reduce long data acquisition times ,and insensitivity to weak Raman or Raman inactive pharmaceutical excipients.19, 20 The ATR imaging technique overcomes the major limitations of other imaging techniques. It has improved spatial resolution compared with NIR and diffuse reflectance imaging, and provides higher sensitivity and faster acquisition times for large image areas compared to Raman mapping.21, 22
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Several studies have employed ATR technique for pharmaceutical analysis. Chan et al. investigated the use of diamond ATR accessory and micro-ATR imaging for imaging of pharmaceutical tablets.23Their results have shown that macro ATR imaging accessory is useful to study the overall distribution of different ingredients in tablet matrix while the micro-ATR imaging technique enables closer look at the tablet with detection of low-concentration ingredients down to 0.5%. Ricci et al. used ATR imaging to distinguish authentic and counterfeit artesunate anti-malarial tablets.24 Lanzarotta et al. evaluated the capabilities of the micro ATR imaging and macro ATR imaging in analysis of counterfeit pharmaceutical tablets. The results demonstrated that both techniques complement each other where micro ATR imaging provides more information about tablet formulation, while a macro ATR approach is more effective for screening of counterfeits.25
ATR FTIR has also been used to study drug release and dissolution. Hanh et al. investigated the use of ATR- FTIR spectroscopy to study the release of drug particles (ketoconazole) from ointment formulations and determine the diffusion coefficient of the drug. The formulation is placed on the ATR crystal and the acceptor membrane on the top of the ointment. The decrease of the drug content was quantified by monitoring the spectral changes using multivariate analysis.26 Weerd et al. designed a special compaction cell for tablets in such a way allowing introduction of water flow once the tablet was compacted to combine imaging approach with measurement of dissolution of the drug. They studied the dissolution of Nicotinamide tablets using partial least squares (PLS) calibration for quantitative analysis of the dissolved drug as function of time. The total amount of dissolved drug obtained from imaging data has been compared with the obtained drug concentration using UV detection of effluent cell. The results of the FTIR imaging were comparable to the results of the conventional dissolution test. 27 This chapter represents the first attempt to apply ATR imaging approach for quantitative analysis of active ingredients in solid dosage form using Principal Component analysis PCA and Image J software. A comparison of these two software packages was conducted to determine if Image J could be used in an undergraduate laboratory to extract quantitative information from dried precipitates.
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6.2.2 Image analysis using PCA and Image J
Principal Component Analysis PCA is a chemometric methodology that has been used in image recognition and compression by reducing the number of variables in a large data set within the microspectroscopic images which are comprised of thousands of pixels, wavelengths and corresponding intensities. 28,29PCA algorithms produce false color images by classification of the image spectra into an independent set of sub spectra (principal components or scores) from which the image can be reconstructed providing compositional information within the field of view. Each score has distinct color; the area of each color can provide information about the distribution of the components and their relative concentrations.
Image J is an image processing program developed by the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation. It is designed for providing quantitative data from scientific multidimensional images. 30-32 It has many successful applications in protein quantification, cells or bacterial colonies counting, and nanoparticle size distribution analysis.33-38The colored ATR images can be analyzed using Image J giving information about the area of each color in the image which can help in estimating the relative concentration of each corresponding component. The aim of this chapter is to investigate the capability of ATR imaging of quantitative analysis of active ingredients in pharmaceutical tablets through image analysis using Principal Component Analysis (PCA) algorithms and NIH Image J.
6.3 Experimental
6.3.1 Materials and methods
Dextromethorphan was purchased from Fisher Scientific Company L.L.C. (Pittsburgh, PA). Guaifenesin, acetaminophen, aspirin, and caffeine were purchased from Sigma Aldrich Chemical Company, Inc. (St. Louis, MO).
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6.3.2 Instrumentation
Infrared spectra of active ingredients were collected with a Harrick Split-pea interfaced to a Perkin Elmer Frontier Fourier transform infrared spectrometer using Germanium internal reflection element (IRE) and the standard deuterium triglycine sulfate (DTGS) detector. For each spectrum, 32 individual scans were obtained from 4000-400cm-1 with a spectral resolution of 4 cm-1. Tablet cross sections were polished using lapping film and then placed directly onto IRE and ATR images were collected on a Perkin Elmer Spotlight infrared microscope. The microscope was equipped with a liquid nitrogen cooled mercury cadmium telluride (MCT) 16x1 linear array detector. The imaging area was 400 x 400 µm2 with spectral resolution 8cm-1 and pixel size 1.56 µm. Perkin Elmer Spectrum IMAGE.7.1 and NIH Image J software were employed for image analysis.
6.4 Results and discussion
The study was applied on two tablet formulations. The active ingredients of the first tablet (Mucinex) are Dextromethorphan HBr 60 mg and Guaifenesin 1200 mg while the other formulation (Excedrin) contains Aspirin 250 mg , Acetaminophen 250 mg, and Caffeine 65 mg. ATR FTIR spectra of all the active ingredients were collected (Figures 6.1-6.2). Since there is a big difference in the particle size of the active ingredients, it will be difficult to get homogenous physical mixtures of these components to apply PCR models for prediction of the concentration of these components in tablet dosage form (Table 6.1).
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95 92 90 88 86 %T 84 82 80 78 76 97 92 88 86 82 %T 80 76 74 70 68 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 6.1: ATR spectra of a) Dextromethorphan and b) Guaifenesin.
96 90 85 80 %T 75 70 64 96 90 80
%T 70 60
47 97 90 80
%T 70 60 57 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 6.2: ATR spectra of a) Acetaminophen, b) Aspirin, and c) Caffeine.
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Component Particle size m
Dextromethorphan 15
Guaifenesin 45
Acetaminophen 30
Aspirin 155
Caffeine 8
Table 6.1: Particle size of the active ingredients.
Therefore, the image analysis of cross sectioned tablets was applied using PCA and Image J. The ATR image of Mucinex tablet has three main scores: score 1 red represents Guaifenesin, score 2 blue represents Dextromethorphan HBr, and score 3 green represents starch (excipient) (Figure 6.3). Using PCA, the area of each color can be determined. Assuming that the tablet formulation is homogenous and the cross section is representative to the whole tablet, the approximate concentration of each component can be calculated using the following equation.
The concentration in mg = The %area x mass of the tablet………………….....Equation 1
The mass of Mucinex tablet is 1500 mg while that for Excedrin is 670 mg
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106 100 95 90
%T 85 80 75 105 100 90 80
%T 70 60 54 109 90 80 70 %T 60 50 40 34 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 6.3: ATR image of Mucinex tablet cross section and corresponding spectra of each score Dextromethorphan (blue), Guaifenesin (red), and starch (green).
Similarly, the ATR image of the cross sectioned tablet was also analyzed using Image J. Image J exclusively select the color score of interest through adjusting Hue value which
110 is one of color properties that defines a particular color ( Appendix A). The area ratio of each color and the corresponding concentration are summarized in Table 6.2.
Component PCA Image J
Average* Conc. mg % Average Conc. % % Area Deviation *% Area mg Deviation from from labeled labeled value value
Guaifenesin(1200mg) 71 1065 11.25 73.8 1107 7.75
Dextromethorphan(60mg) 3.5 52.5 12.5 4.4 66 10
Table 6.2: The calculated concentrations of active ingredients of Mucinex using PCA and
Image J. *Average of analysis of 3 ATR images.
The results show that PCA and image J can be used to determine the relative concentration of active ingredients in pharmaceutical tablets. The % deviation was calculated according to the following equation:
Labeled concentration - Calculated concentration %Deviation 100…….Equation 2 Labeled concentration
Since, the current design employed in this laboratory limits the imaging area to 400 x 400 µm2, some collected images for Excedrin tablet did not contain all the active ingredients due to the distribution of the ingredients throughout the tablet (Figures 6.4- 6.5). This requires multiple imaging of tablet cross sections to get an image with all active ingredients and to verify accurate concentration gradients across the image. Figure 6.6 shows an ATR image containing the active ingredients of Excedrin where red represents Aspirin, cyan for Caffeine, blue for Acetaminophen, and green represents
111 starch (excipient). The image was analyzed using PCA and Image J. The area of each color was used to determine the concentration of the components according to equation 1 (Table 6.3).
Figure 6.4: ATR image of Excedrin tablet cross section showing Aspirin (red) and caffeine (green).
Figure 6.5: ATR image of Excedrin tablet cross section showing Acetaminophen (blue) and Aspirin (red).
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104
80
%T 60 39 108 100 80
%T 60
35 107 100 90 %T 80
67 112 100 80
%T 60 38 4000 3500 3000 2500 2000 1500 1000 748 cm-1
Figure 6.6 : ATR image of Excedrin tablet cross section and corresponding spectra Aspirin (red), Caffeine (cyan), Acetaminophen(dark blue), and starch (green).
113 component PCA Image J
Average* Conc. %Deviation Average Conc. %Deviation % Area mg from *% Area mg from labeled labeled value value Aspirin(250 mg) 33 221.1 11.56 34.5 231.15 7.54
Acetaminophen(250 mg) 40 268 7.2 34.9 233.83 6.4
Caffeine(65 mg) 10 67 11.66 9 60.3 7.23
Table 6.3: The calculated concentrations of active ingredients of Excedrin using PCA and Image J. *Average of analysis of 3 ATR images.
The results obtained show that ATR-FTIR imaging can be applied to obtain information about active ingredient concentration through image analysis of their cross sectioned tablets but the % deviations from the labeled value are relatively high in the analysis of both tablets due to the limited imaging area and variation of concentration gradients or distribution of components in the image. However, Patterson et al. demonstrated that a 2500 x 2500 µm2 image area can be obtained with the use of a larger 12.5 mm diameter IRE which can help to improve the accuracy of the method.39 Also, it should be mentioned that it is necessary to ensure intimate contact of the sample with the IRE to provide accurate and reproducible data which requires preparing of cross sectioned tablets with even and uniform surfaces. Once the tablet is pressed against the crystal, it is contaminated, therefore proper crystal cleaning is required prior each measurement. However, crystal cleaning is relatively fast unless the tablet particles have adhesive properties. Despite these limitations, the technique is considered to become a simple and quick tool to determine the relative concentration of active ingredients in pharmaceutical tablets.
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6.5 Conclusion
In conclusion, this work investigates the use of ATR-FTIR imaging for concentration determination of active ingredients in tablet formulations. This study provides a new approach for quantitative analysis through image analysis using PCA and image J. The method is simple, relatively easy to use, and most importantly it involves little sample preparation. The method does not need any separation or extraction steps prior to determination procedure. In addition it can be applicable to different types of solid samples as it depends on determination of the area of the color scores of different components composing the sample. Some limitations of this approach can be avoided on using large crystal diameter or using macro ATR imaging to get larger and representative images of the sample.
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Appendix A
Steps for using Image J for area measurement
1-Select File → Open from the menu bar to open a stored image file.
2- Select Analyze → Set scale from the menu bar to set the unit of length as pixel.
3- Choose parameters to be measured (Area) via Analyze → Set Measurements.
4-Select the desired color in the image via Image → Adjust → Color threshold and use the slider to adjust [Hue-Saturation – Brightness] until the required color is all selected.
5-In the color threshold window click select, the desired area will be selected in the image.
6-For measuring area, select Analyze → Measure from the menu bar. The measured area will be displayed in a data window.
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Chapter 7
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Conclusion
7.1 Summary
Several aspects of kidney stone analysis have been addressed in this dissertation. Chemical synthesis and characterization of common phases of calcium oxalate and calcium phosphate have been presented. The developed methods were optimized for the production of pure phases. The synthesis of these components helps to better differentiate between these phases and improves identification of stone subtypes. Sample preparation of renal stone cross sections and stone fragments has been presented using polishing discs and lapping films. The polishing steps lead to a significant improvement in reflectance measurements of the stones. It was also demonstrated in this research that infrared microspectroscopy can reveal important details about the microstructure of the stones and the distribution of stone components. Both ATR and reflectance imaging can be utilized in a complementary fashion for accurate qualitative stone analysis. The ATR technique has several benefits over reflectance and transmission modes. One of these advantages is that the path length associated with an ATR measurement is principally based on the refractive index of the sample and the IRE. Thus, the main goal of this research was to extend the method in quantitative analysis of real samples. Quantitative analysis of mixed stones has been successfully performed on cross-sectioned renal stones using PCR models. The quantitative information for stone components is important for studying the etiology of stone formation and the causative factors of the disease. This research has also demonstrated that ATR microspectroscopy provides improved spatial resolution and the results show the capability of detection of structures whose dimensions are smaller than the diffraction limit. Additionally, this work has demonstrated that it is possible to study a spectrum extracted from a single pixel using band fitting. The method also has been successfully applied for quantitative analysis of pharmaceuticals based on analysis of the colored ATR images using PCA and Image J. This portion of the study shows that Image J and PCA give comparable results for estimation of the percentage area of color scores which are then used for determination of the concentration of the active ingredients.
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7.2 Future work
This dissertation demonstrates the capability of ATR microspectroscopy for quantitative analysis of mixed stones of binary components. The application of this method will be extended to quantify mineral inclusions in tissue biopsies or complex stones of ternary and quaternary components. Such quantitative information can potentially help urologists to study the mechanism of stone formation at any stage of its subsequent growth. ATR FTIR imaging also provides the satisfactory spatial resolution that help in visualization of the localized mineral/tissue interface and understanding the mechanism of interstitial mineral deposition. Additionally, ATR imaging is capable to visualize submicron particles with diameter up to 0.1m. Thus the method can be introduced to clinical laboratories for early disease diagnosis and protein therapeutics analysis. Future research will also involve the analysis of single cells using ATR imaging. In addition, the approach for application of ATR imaging for quantitative analysis of pharmaceuticals based on image analysis using Image J and PCA can be applicable to different types of solid samples. Ultimately, it is the goal of this laboratory and its collaborators to incorporate infrared microspectroscopic methods in clinical laboratories to be used as reliable tools for the analysis of kidney stone and other biological samples.
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