Development and Validation of a Qualitative Method for Screening Pesticide Residues in Fruits and Vegetables using UHPLC/ESI Q-Orbitrap based on a Compound Database Approach

Jian Wang, PhDCalgary LaboratoryCanadian Food Inspection Agency

Outline• Objectives• Method development

• Data acquisition

• Build Compound Database (CDB)– Retention time correction

Outlin

e – Retention time correction– Set peak detection threshold

• Method validation

• Method performance parameters and criteria

• Results• Conclusion

2

Outlin

e

UHPLC/ESI Q-Orbitrap Applications for Chemical Contaminants Analysis

Quantification Target or Non-target Screening

1 2 3

1. Target compound analysis (Quantification)

2. Target compound screen (Compound Database or MS library)

3. Non-target compound screen (Differential Analysis)

Obje

ctiv

es

3

QuantificationScreening

Obje

ctiv

es

Rationale48th CCPR Proposed Draft Guidelines on Method Performance CriteriaThe screening concept offers laboratories an effective means to extend their analytical scope to analytes which potentially have a low probability of being present in the samples. Analytes that occur more frequently should continue to be monitored using validated quantitative multi-residue methods.

Obje

ctiv

es

4

DetectabilitySoftware detects the peaks of interest without manual integration1. Build Compound Database for

target screening2. Reduce false negative rate3. Reduce false positive rate

multi-residue methods.

Obje

ctiv

es

WORKFLOW

Meth

od D

evelo

pm

ent

5

Meth

od D

evelo

pm

ent

Mass [m/z] Polarity

167.07027 Positive

167.10397 Positive

168.06552 Positive

169.06816 Positive

170.09643 Positive

172.01640 Positive

174.06802 Positive

174.09134 Positive

179.12126 Positive

180.08012 Positive

180.08012 Positive

182.12879 Positive

183.11280 Positive

184.01918 Positive

184.07666 Positive

Inclusion list

184.07666 Positive

184.13052 Positive

185.03552 Positive

185.03552 Positive

185.07094 Positive

186.12370 Positive

187.08418 Positive

187.12298 Positive

188.06820 Positive

188.11036 Positive

189.08592 Positive

189.15975 Positive

190.04334 Positive

190.12601 Positive

191.08488 Positive

191.09457 Positive

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Data

Acq

uis

itio

n Compound Database is built by injecting individual standards using Full MS/ddMS2 and “inclusion list” to obtain the product ion spectrum of a pesticide.

Sample data are

m/z 100 – 500m/z 25 × 16 = 400

m/z 500 – 900m/z 100 × 4 = 400

Sample data are acquired using Data Independent Acquisition (DIA) and, therefore, the data include all ions from HCD of a specific mass range.

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Data

Acq

uis

itio

n

Identify the most intense precursor ion, normally in its protonated

Up to five ions come out from HCD are included in CDB from high to low according to ion intensity but the first one is always a precursor ion.

Build

Com

pound D

ata

base

its protonated form

Find individual fragments from ddMS2 product ion spectrum

1

2

Intensity high to low

Build

Com

pound D

ata

base

8

In spectral mass mass correction is based on precursor ion or 214.08963

Import from Excel to TraceFinder to build an executable Compound Database

Build

Com

pound D

ata

base

10

Build

Com

pound D

ata

base

Question: Why correct retention time in CDB?

Facts:1. Software chooses the nearest peak to the retention time of individual pesticides

specified in CDB.2. Software possibly picks up a wrong peak as a result of one or more adjacent

peaks close to the compound of interest.

Corr

ect

ion

RT uncorrected

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Rete

ntion T

ime C

How to correct retention time?using 3-hydroxycarbofuran as a reference

Corr

ect

ion

Rete

ntion T

ime C

RT uncorrected

Retention time correction is critical to identify the compound of interest and to reduce false negative rate

Corr

ect

ion

RT corrected

Rete

ntion T

ime C

Fact:There are a significant number of false positives from a blank sample if data are processed by a default response threshold such as 30,000 for all pesticides.

Question: Why set peak detection threshold?ete

ctio

n T

hre

shold

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Set

Peak D

ete

ctio

n

30,000

Response threshold can be set for individual pesticidesete

ctio

n T

hre

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Set

Peak D

ete

ctio

n

Each individual

How to set peak response

threshold?

Facts:1. Response (peak

area) is pesticide dependent.

2. Matrix effects lead to ion suppression or enhancement.

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n T

hre

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Each individual pesticide response threshold is calculated from the peak area of 5 ppb standard and is tested at different percentage of its original area counts.

OPA: original peak area

Set

Peak D

ete

ctio

n

10% original peak area@ 5 ppb

Setting response

threshold is

ete

ctio

n T

hre

shold

30,000 default response threshold

threshold is critical to

reduce false positive

rate.

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Set

Peak D

ete

ctio

n

Experimental design for method validation

Experiment design

� Matrix: 2 groups or 10 matrices including apple, banana, grape, orange, and strawberry, and carrot, potato, tomato, broccoli, lettuce.

� Spiked at 10 or 100 µg/kg in duplicate.

� Performed on 3 different days.

alid

ation

QuEChERs

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Only 10 µg/kg, 1:1 dilution, results are presented here.

Meth

od V

alid

ation

QuEChERs� 15 g samples + 15 mL of acetonitrile/acetic

acid (99:1, v/v) + 1.5 g of sodium acetate anhydrous + 6.0 g of magnesium sulfate anhydrous

� 9 mL supernatants + 900 mg MgSO4, 150 mg C18 and 300 mg PSA

� Dilute 1:1 or concentrate 3:1 prior to UHPLC/ESI Q-Orbitrap

Screening method performance parameters and criteria• Parameters

• Retention Time and accurate mass of Precursor by Full MS Scan (RTP) for tentative positive screening.

• Retention Time and accurate mass of a Fragment Ion by DIA (RTFI) for confirmatory positive screening.

Para

mete

rs a

nd C

rite

ria

• Criteria• Retention time: ±0.2 or 0.5 min • Mass accuracy: ≤5 ppm

• An acceptable false-negative rate of ≤5%

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Meth

od P

erf

orm

ance

P

Resu

lts

ResultsAn acceptable false-negative rate of ≤ 5%

a Data by threshold of 30 K are based on ±0.2 min and Data by threshold of 10% are based on ±0.5 min sec.

Resu

lts

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RTP Manual Check: to include or exclude any peaks missed or misidentified by software

The ideal scenario

An acceptable false-negative rate of ≤5%

Resu

lts

20 samples x 3 days x 450 = 27,000 data points

~50 pesticides can’t be detected due to low sensitivity or failing on the criterion of acceptable false-negative rate of ≤5%

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Resu

lts

RTP Manual Check vs RTP (±0.2 min, ≤5 ppm)

An acceptable false-negative rate of ≤5%

Resu

lts

Tentative positive screening

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Resu

lts

RTP Manual Check vs RTP vs FI vs RTFI (±0.2 min, ≤5 ppm)

An acceptable false-negative rate of ≤5%

Resu

lts

Confirmatory positive screening

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Resu

lts

RTP: 30,000 default vs 10% threshold (±0.5 min, ≤5 ppm)

An acceptable false-negative rate of ≤5%

Resu

lts

Tentative positive screening

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Resu

lts

Final screening method: RTP vs RTFI (±0.5 min, ≤5 ppm, 10% threshold)

An acceptable false-negative rate of ≤5%

Resu

lts

Tentative positive screening and Confirmatory positive screening

25

Resu

lts

Conclusion

UHPLC/ESI Q-Orbitrap along with Compound Database (CDB) can serve as a practical approach for pesticide residues screening in fruits and vegetables.� Retention time correction is critical to reduce false negative rate

and to detect true positives.

� Appropriate response threshold is critical to reduce false positive rate.

Concl

usi

on

rate.

� Tentative positive screening: Retention Time (±0.5 min ) and accurate mass (≤5 ppm) of Precursor by Full MS Scan (RTP).

� Detect ~400 (out of 450) pesticides at 10 µg/kg.� Confirmatory positive screening: Retention Time (±0.5 min ) and

accurate mass (≤5 ppm) of a Fragment Ion by DIA (RTFI).

� Detect ~330 (out of 450) pesticides at 10 µg/kg.

Concl

usi

on

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Willis Chow, Canadian Food Inspection AgencyJames Chang, ThermoFisher Scientific Inc

Jon W Wong, US Food and Drug Administration

Acknowledgment

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