Latest Analytical Requirements to meet Global Demands in Food Testing Kaushik Banerjee, PhD FNAAS FRSC National Referral Laboratory ICAR-National Research Centre for Grapes Pune 412 307, India E-mail: [email protected] SPS_161116 Challenges in residue analysis • Sample variability (matrix) - High sugar to low sugar - High fat to low fat - High to low water contents • Diverse physico-chemical properties of the target compounds Expectations • high throughput analysis • As many number of analytes as possible • Low levels (<10 ng/g ) • Balancing the performance, cost and speed of analysis SPS_161116 Residue analysis: Compliance to global Regulations Food Market Export Domestic National EUR authorities - Lex European Union eur-lex.europa.eu MHLW Japan http://www.ffcr.or.jp/zaidan/FFCRHOME.nsf/TrueMainE?O penFrameset USFDA USA National www.fda.gov/ authorities AVA Singapore & Malaysia http://www.ava.gov.sg/ SFDA Middle East http://old.sfda.gov.sa/En/Food SPS_161116 Why so much diversity? Food safety concerns are different in different countries There are differences in how food is grown, prepared and consumed across different countries or even within a country Codex standards are generally acceptable Variation in risk incidences Countries may therefore have differing standards. Method sensitivity requirements could vary with regulations SPS_161116 WHY CHEMICAL RESIDUE MONITORING IS INCREASINGLY BECOMING SO IMPORTANT IN ASIAN COUNTRIES? AAU_181016 World map of non- compliance for EU- MRL AAU_181016 EU Alert notifications- trend AAU_181016 Diversified food safety issues on the basis of which import consignments were rejected in EU countries AAU_181016 Food by hazard category_ EU RASFF data 700Major source of non-compliances 600 pathogenic microorganisms 500 mycotoxins 400 pesticide residues 300 heavy metals 200 100 0 2011 2012 2013 2014 AAU_181016 non-compliance by food product category 800 700 600 500 400 300 200 100 0 2012 2013 2014 AAU_181016 Which chemicals to monitor for Comprehensive risk assessment Chemical residues : to monitor and control Chemicals applied in agriculture and/or food processing + • Chemicals which might appear from indirect sources Contaminants : • Pesticide residues • Product specific requirements: mycotoxins, antibiotics, drug residues, POPs, etc. AAU_181016 Emphasis is on multiresidue analysis approaches • Analysis of a large number of compounds by a single method - Diverse chemistries included: non-polar to semi-polar to polar - Diverse kinds of contaminants included AAU_181016 Pesticide Residue Analysis Timeline <1960 – Schecter method by thin-layer chromatography 1960 – Publication of Rachel Carson’s Silent Spring 1963 – FDA Mills method for Organochlorines (OCs) 1975 – FDA Luke method for OCs, OPs, and others 1985 – Luke method becomes AOAC Official Method 985.22 2003 – QuEChERS approach for modern pesticides Petroleum ether (nonpolar pesticides) Waste polar pesticides Shake funnel 100 g sample + Add 100 mL pet ether When MOE was developed, only nonpolar pesticides were 200 mL ACN + + 600 mL H 2O + filter saturated NaCl important Organoch lorine and GC-ECD, nonpolar GC-FPD organoph Analysis osphorus Florisil pesticides Kuderna-Danish Cleaned Florisil cleanup extractSlide adapted from J. Wong Evaporation Blend sample Pass through Concentrate extract using with acetone C18 SPE Salt-out acetone + petroleum ether Add I.S. and GC-MS/SIM transfer to GC Analysis vials 3 x GC-MS Runs Cleanup with Concentrate extract ~350 pesticides SAX/PSA SPE Slide adapted from J. Wong QuEChERS Methods 2003 2005 2007 Anastassiades et al. Lehotay et al. Anastassiades et al. Original AOAC 2007.01 CEN 15662 10-15 g sub sample 10-15 g sub sample 10-15 g sub sample ↓ ↓ ↓ 10-15 mL 10-15 mL MeCN 10-15 mL MeCN 1% HOAc in MeCN ↓ shake ↓ shake ↓ shake 0.4 g/mL anh.MgSO 4 0.4 g/mL anh.MgSO 4 0.1 0.4 g/mL anh.MgSO 4 0.1 g/mL NaCl ••• g/mL NaCl 0.1 g/mL NaOAc 0.1g/mL Na 3Cit 2H 2O 0.05 g/mL Na Cit •••1.5H O ↓ shake ↓ shake 2 2 ↓ ↓ ↓ shake centrifuge Option: centrifuge ↓ + 50 mg centrifuge 150 mg/mL anh.MgSO 4 150 mg/mL anh.MgSO 4 C & 25 mg/mL PSA 18 50 mg/mL PSA 150 mg/mL anh.MgSO 4 7.5 mg ↓ ↓ 25 mg/mL PSA GCB ↓ shake & centrifuge shake & centrifuge shake & centrifuge Option: Scale-Up & Conc. in Toluene Slide adapted from S.J. Lehotay, USDA PERSPECTIVES & EXPECTATION Case study for pesticides • 400+ pesticides are regulated in EU • Consolidated number of regulated compounds for Codex, EU, Japan, USA, etc. : approx 600-700 • Expectation: all these chemicals should be analyzed to explore all the potential markets SPS_161116 Residue screening for food safety compliance…………. Three possibilities: • Reliable target-oriented screening when you have reference standard • Screening for suspected analytes when you do not have reference standard • Screening for unknowns SPS_161116 Sample preparation- generic or selective SPS_161116 Identification of analytes and confirming results • Chromatography: RT in the extract should correspond to that of the matrix matched calibration standard, within ±0.1 min for both GC/LC. • Mass spectrometry: Analyte specific mass spectra, isotope patterns, signals for selected ions. – Identification using selected ions : selected ions must be specific for the analyte in the matrix being analysed and in the relevant concentration range. – XIC should have similar ion ratio, RT compared to cal. standards. – Different types and modes of mass spectrometric detectors provide different degrees of selectivity and specificity. For more details DG SANTE /11945/2015 (Table 4). SPS_161116 MS detector is essential for GC or LC analysis SPS_161116 Targeted analysis: Triple quad Screening: GC-HRMS and LC-HRMS SPS_161116 MS/MS for unique identification of co-eluting compounds Cyperazine Pentachloroaniline Pretilachlor Desmetryn 2,4-DDD Aldrin Dimethochlor Oxadiazon Many compounds may co-elute in multiresidue analysis. Co-eluted signals can be singularly identified based on unique MRM transitions. SPS_161116 MS/MS optimization Determines and optimizes MRM transitions automatically for new compounds 3 steps operation for the MRM Optimization tool 3. Register CE value for best sensitivity and its MRM transition m/z values Intensity CE(V) 1. Set up CID collision energy (CE) parameters 2 .Automatic detection of optimal CE voltage SPS_161116 Final MRM method- set automatically SPS_161116 Analytical quality control in residue analysis Guidance document on analytical quality control and method validation procedures for pesticides residues analysis in food and feed. SANTE/11945/2015 Supersedes SANCO/12571/2013 Implemented by 01/01/2016 SPS_161116 2nd Edition May 2015 September 24, 2015 SPS_161116 Method validation criteria SANTE/11945/2015 Parameters What/how Criterion Sensitivity/Line Linearity check from five levels Residuals>20% < arity ±20% Matrix effect Comparison of response from solvent standards and ±20% matrix matched standards Limit of Lowest spike level meeting the method performance ≤ MRL quantitation criteria for accuracy and precision Specificity Response in reagent blank and blank control samples < 30% of Identification criteria Reporting level Truness Average recovery for spike levels tested 70-120% (Accuracy) Precison (RSD r) Repeatability RSDr for spiked samples ≤ 20% Precison Within laboratory reproducibility, derived from ongoing ≤ 20% (RSD wR ) method validation/ verification Robustness Average recovery and RSDwR, derived from on-going method validation / verification SPS_161116 LOD & LOQ • LOD : The lowest concentration of an analyte that can be detected, not quantified. • LOQ : The lowest concentration of an analyte in a sample that can be determined with Peak B LOQ acceptable precision and accuracy under stated operational conditions Peak A • Expressed as a concentrationLOD at a specified signal:noise ratio Baseline noise SPS_161116 LCL S/N= 10 SPS_161116 Fenamidone in grapes at LOQ Solvent standard Grape spike Name of chemical % Recovery at LOQ LOQ = 1 ng/g (RSD) (n=8) Fenamidone 98 % (8.5 % ) SPS_161116 Identification requirements for different MS technology SPS_161116 Two MRM transitions - “confirmatory analysis” Azinphos methyl Phosmet 318 >160 318 >160 ? Azinphos methyl Phosmet 318 >132 318 > 77 SPS_161116 Linuron in Coriander by LC-MS/MS Linuron ? Linuron Sol. Std. in Coriander Ion ratio: 1.8 Ion ratio: 2.4 SPS_161116 Accuracy Expresses the CLOSENESS of agreement BETWEEN the value, which is accepted either as a conventional TRUE VALUE or an accepted REFERENCE VALUE and the VALUE FOUND i.e. individual observation or mean of measurements The closeness of test results to the true value obtained by the method (trueness). – Established across the range Recommended Data minimum 3 concentration levels in specified range (6 replicate for each level. (e.g. 3 concentrations/6 replicates each). % Recovery =(Cal. conc.- ctrl sample) x 100/Spiked conc . Reported as % recovery of known added amount or difference between the mean and true value, with confidence intervals SPS_161116 Precision The precision (VARIABILITY) of an analytical procedure is usually expressed as the standard deviation (S), variance (S 2), or coefficient of variation (= relative standard deviation, R.S.D.) of a series of measurements. The confidence interval should be reported for each type of precision investigated. – Repeatability , a measure of variability under the same operating conditions over a short interval (intra-assay precision). Minimum of 9 determinations covering specified range – Intermediate precision, a measure of within-laboratory variations