1. Implementation of High Resolution Mass Spectrometry
Indiana Pesticide Lab
Ping Wan
AAPCO Lab Director’s Meeting
March 7-8, 2016

2. 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

3. 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

7. 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

8. Data Dependent Acquisition (DDA)

9. 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

10. All Ion Fragmentation (AIF)

11. vDIA-Uneven Isolation DIA windows
Isolation window 25 Da
Isolation window 100 Da

12. AIF vs. DIA vs. DDA
Allidochlor
AIF
DIA
DDA

13. 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

14. 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

15. 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)

16. Matrices to Study Spinach Orange Avocado Honey Wheat flour Raisins
Hazelnuts
Tea
Ginseng root
Milk
Kidney beans
Peanut butter

17. 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

18. 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

19. Indiana’s Experience So Far
Qualitative Work
Two existing databases
Mass accuracy
Isotopic patterns
Fragmentation
Quantitative Work
Reproducibility
LOQ
Accuracy

20. 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

21. 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

22. 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%

23. 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

24. 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