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Implementation of High Resolution Mass Spectrometry
Indiana Pesticide Lab Ping Wan AAPCO Lab Director’s Meeting March 7-8, 2016
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OUTLINE Acquisition of the QE Overall Observations of the QE
MS/MS Acquisition Experiments (DDA, AIF, DIA) Proposed AOAC collaborative Study Spectral Libraries and Compound Databases How does the QE Compare to the Triple Quad LC/MS/MS Future Plans OUTLINE
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Instrument Evaluation
Three Q-tofs plus the Q-orbitrap (Company A, T, S and W) Unknown Pesticides identification Sensitivity ~10 ppb unknown Sub 1 ppb quant. Reproducibility Linear Dynamic Range Ion suppression experiment Software user-friendliness Software efficiency unknown identification workflow Reporting software Service/support Cost < $500K
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MS/MS Acquisition Experiments
HRMS DDA DIA AIF Ion Source Quadrupole Collision Cell DDA = Data-dependent Acquisition, DIA = Data Independent Acquisition, AIF = All Ion Fragmentation one precursor at a time Best specificity multiple times with small isolation windows Better specificity ion fragmentation of all precursors No specificity Modified from Zhu et al., Anal. Chem. 2014, 86, Courtesy of Jon Wong, FDA
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Data Dependent Acquisition (DDA)
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Published Work based on UHPLC-DDA-HRMS
J. Agric. Food Chem., DOI: /jf505049a, Publication Date (Web): December 22, 2014 J. Agric. Food Chem., 2014, 62 (42), pp 10375–1039, DOI: /jf503778c
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All Ion Fragmentation (AIF)
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vDIA-Uneven Isolation DIA windows
Isolation window 25 Da Isolation window 100 Da
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AIF vs. DIA vs. DDA Allidochlor AIF DIA DDA
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Pesticide Screening with Identification
using Compound Database and Mass Spectral Libraries Compound database MS scan: 1 precursor ion MS/MS: 2 product ions δM ≤ 5 ppm Library Search
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Identification of Imidacloprid in Tea (Tea Sample 27)
* * * Compound database Tea Sample #27 Imidacloprid: 4.3 ppb * * Reference (buffered ACN:H2O) Imidacloprid: 100 ng/mL
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AOAC Multi-laboratory Study (Jon Wong, FDA)
Increase FERN Labs existing capabilities for screening residues and contaminants FERN Laboratories will have immediate Q-Exactive Screening Methods Laboratories will be able to screen for pesticides in different matrices under optimized experimental conditions Laboratories will gain access to ~1000 pesticide standards, MS/MS spectral libraries, and compound database (labs will also gain access to mycotoxin and veterinary drug libraries and databases as well) Excellent platform to work with- can be expanded to other chemical residues and chemical contaminants Harmonized methods and cooperation between laboratories (internationally)
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Matrices to Study Spinach Orange Avocado Honey Wheat flour Raisins
Hazelnuts Tea Ginseng root Milk Kidney beans Peanut butter
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Domestic and Global Cooperation
Laboratories contribute to a “global” database for screening pesticides CFIA OME FDA-ORA CAIQ CDFA FDA-CFSAN State of IN SIDFC TFDA NRCG LANAGRO California Department of Food and Agriculture (CDFA) Canadian Food Inspection Agency (CFIA) Chinese Academy of Inspection and Quarantine (CAIQ) National Research Centre for Grapes (NRCG) Ontario Ministry of the Environment (OME) Shanghai Institute for Food and Drug Control (SIFDC) Taiwan Food and Drug Administration (TFDA) FDA-ORA PNWRL
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FDA/DHHS, College Park, MD
Jon Wong FDA/DHHS, College Park, MD Paul Yang Ontario Ministry of the Environment, Etobicoke, ON Canada Jian Wang Canadian Food Inspection Agency, Calgary AB Canada Chia-Ding Liao Taiwan Food and Drug Administration, Taipei, Taiwan Zhengwei Jia Shanghai Institute for Food and Drug Control, People’s Republic of China Kaushik Banerjee National Research Centre for Grapes, Pune, India James S. Chang ThermoScientific, San Jose, CA USA Greg Mercer and Randy Self FDA/ORA- Pacific Northwest Regional Laboratory, Seattle WA Roland Carlson California Department of Food and Agriculture, Sacramento CA
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Indiana’s Experience So Far
Qualitative Work Two existing databases Mass accuracy Isotopic patterns Fragmentation Quantitative Work Reproducibility LOQ Accuracy
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Passing Criteria Mass Tolerance I.P. Match No. of Fragments 5 ppm 90% 2 Standard: WMR-0255 5.00 ppb Std. 50.0 ppb Std. 500 ppb Std. Analyte R.T. Status Comment Acephate 1.1 Found 1 Frag. Carbaryl 8.5 No Frag./L.S. Dicrotophos 4.3 Dimethoate 5.7 Dimethomorph 9.6 Isocarbophos 10.0 Methamidophos Mevinphos 6.0 Monocrotophos Omethoate Temephos 14.4 1 Frag.; No L.S. Trichlorfon 4.9 Not Found Vamidothion
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Reproducibility Imazamox Low CCV R.T. Peak Area High CCV CCV-L1 3.03
101771 CCV-H1 CCV-L2 3.02 100466 CCV-H2 CCV-L3 107588 CCV-H3 CCV-L4 108578 CCV-H4 CCV-L5 103971 CCV-H5 CCV-L6 3.04 103427 CCV-H6 CCV-L7 105392 CCV-H7 CCV-L8 107094 CCV-H8 CCV-L9 100233 CCV-H9 CCV-L10 104899 CCV-H10 RSD 0.23% 2.80% 0.17% 0.86% Pass/Fail Pass
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LOQ Imazamox Peak Area RSD Pass/Fail Conc. (ppb) Cal Block 1
0.0500 2129 3963 4350 34.1% Fail 0.100 11133 12728 10155 11.5% Pass 0.500 49376 50063 52139 2.8% 1.00 104365 108473 105861 2.0% 5.00 537985 539257 521561 1.9% 10.0 1.5% 50.0 0.3% 100 500 0.9%
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WI Check Sample Performance
Feb Soil Study Analyte Run Mean (ppb) Program Data (ppb) Pass/Fail Prog. Mean Program S.D. Mean - 2 S.D. Mean + 2 S.D. Imazamox 267 182 75.4 31.2 333 Pass MCPA 456 371 79.3 212 530 Triclopyr 446 385 132 121 649 MCPP 656 527 95.0 337 717
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Future Work Expanding the existing databases in both pos and neg modes
Expanding the existing databases in both pos and neg modes Qualitative screening with identification using DIA and AIF Environmental Matrices: determine screening detection limits for pesticides in water, soil and various vegetation
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