Transcript Implementation of High Resolution Mass

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
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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
Ion
Source
Quadrupole
Collision Cell
HRM
HRMS
S
DDA
one precursor at a time
Best specificity
DIA
HRMS
multiple times with small isolation windows
Better specificity
AIF
HRMS
ion fragmentation of all precursors
No specificity
DDA = Data-dependent Acquisition, DIA = Data Independent Acquisition,
AIF = All Ion Fragmentation
Modified from Zhu et al., Anal. Chem. 2014, 86, 1202-1209
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: 10.1021/jf505049a, Publication Date (Web): December 22, 2014
J. Agric. Food Chem., 2014, 62 (42), pp 10375–1039, DOI: 10.1021/jf503778c
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All Ion Fragmentation (AIF)
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vDIA-Uneven Isolation DIA windows
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150
175
200
225
250
275
300
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Isolation window 100 Da
Isolation window 25 Da
125
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325
350
375
400
425
450
475
500
600
700
800
900
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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
1. Compound database
MS scan: 1 precursor ion
MS/MS: 2 product ions
δM ≤ 5 ppm
2. Library Search
Identification of Imidacloprid in Tea (Tea Sample 27)
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Tea Sample #27
Imidacloprid: 4.3 ppb
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Reference (buffered ACN:H2O)
Imidacloprid: 100 ng/mL
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* Compound database
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
1. Spinach
2. Orange
3. Avocado
4. Honey
5. Wheat flour
6. Raisins
7. Hazelnuts
8. Tea
9. Ginseng root
10. Milk
11. Kidney beans
12. Peanut butter
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
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Commodity
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Domestic and Global Cooperation
Laboratories contribute to a “global” database for screening pesticides
CFIA
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OME
FDA-ORA 
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FDA-CFSAN
CDFA State ofIN
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CAIQ
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NRCG
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LANAGRO
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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
SIDFC
 TFDA
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
I.P.
Mass Tolerance
Match
5 ppm
90%
Standard:
Analyte
WMR0255
No. of
Fragments
2
5.00 ppb Std.
50.0 ppb Std.
500 ppb Std.
R.T.
Status
Comment
Status
Acephate
Carbaryl
Dicrotophos
Dimethoate
Dimethomorph
Isocarbophos
Methamidophos
Mevinphos
Monocrotophos
Omethoate
1.1
8.5
4.3
5.7
9.6
10.0
1.1
6.0
1.1
1.1
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
1 Frag.
No Frag./L.S.
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Found
Temephos
14.4
Found
Found
Found
Trichlorfon
Vamidothion
4.9
4.9
Not Found
Found
Not Found
Found
Found
Found
1 Frag.; No
L.S.
Comment
Status
Comment
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Reproducibility
Imazamox
Low CCV
R.T.
Peak Area
High CCV
R.T.
Peak Area
CCV-L1
3.03
101771
CCV-H1
3.03
10737366
CCV-L2
CCV-L3
CCV-L4
CCV-L5
CCV-L6
CCV-L7
CCV-L8
CCV-L9
CCV-L10
RSD
Pass/Fail
3.02
3.02
3.02
3.02
3.04
3.03
3.03
3.03
3.02
0.23%
Pass
100466
107588
108578
103971
103427
105392
107094
100233
104899
2.80%
Pass
CCV-H2
CCV-H3
CCV-H4
CCV-H5
CCV-H6
CCV-H7
CCV-H8
CCV-H9
CCV-H10
RSD
Pass/Fail
3.03
3.03
3.02
3.02
3.03
3.02
3.02
3.02
3.02
0.17%
Pass
10637981
10519845
10743784
10642929
10613505
10516714
10481208
10538277
10645192
0.86%
Pass
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LOQ
Imazamox
Peak Area
RSD
Pass/Fail
Conc. (ppb)
Cal Block 1
Cal Block 2
Cal Block 3
0.0500
2129
3963
4350
34.1%
Fail
0.100
11133
12728
10155
11.5%
Pass
0.500
49376
50063
52139
2.8%
Pass
1.00
104365
108473
105861
2.0%
Pass
5.00
537985
539257
521561
1.9%
Pass
10.0
1078647
1049724
1054309
1.5%
Pass
50.0
5347131
5336217
5319427
0.3%
Pass
100
10829725
10653720
10501632
1.5%
Pass
500
54725563
54040598
53816979
0.9%
Pass
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WI Check Sample
Performance
Feb. 2015 Soil Study
Analyte
Run Mean
(ppb)
Program Data (ppb)
Pass/Fail
Prog. Mean
Program
S.D.
Mean - 2
S.D.
Mean + 2
S.D.
267
182
75.4
31.2
333
Pass
456
371
79.3
212
530
Pass
Triclopyr
446
385
132
121
649
Pass
MCPP
656
527
95.0
337
717
Pass
Imazamox
MCPA
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Future Work
• 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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