Improving the monitoring of multi-class pesticides in baby foods using QuEChERS-UHPLC-Q-TOF with automated identification based on MS/MS similarity algorithms - Archive ouverte HAL Access content directly
Journal Articles Food Chemistry Year : 2022

Improving the monitoring of multi-class pesticides in baby foods using QuEChERS-UHPLC-Q-TOF with automated identification based on MS/MS similarity algorithms

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Abstract

A screening method was developed for the multi-residue analysis of pesticides in baby foods using QuEChERS and UHPLC-Q-TOF. For sample preparation, the two-buffered versions of QuEChERS and different purification procedures were studied. False negatives and false positives were determined using different thresholds mentioned in the literature on the retention time and accurate mass measurement detection criteria. To reach unequivocal identification, the fragmentation spectra of the pesticides were used. The information-dependant-acquisition (IDA) mode was optimized with a precursor-inclusion list (PIL) to limit the loss of MS/MS data. Then, the experimental fragmentation spectra were compared to those included in a homemade library, by assessing different MS/MS algorithms and similarity scores. The optimised method was validated according to SANTE/11312/2021 guidelines. 95% and 73% ofthe pesticides presented a screening detection limit (SDL) and a limit of identification (LOI) ≤ 0.1 mg.kg-1. One plasticizer was found in the investigated samples by a suspect-screening approach.
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anses-03706205 , version 1 (27-06-2022)

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Yassine Makni, Thierno Diallo, Thierry Guérin, Julien Parinet. Improving the monitoring of multi-class pesticides in baby foods using QuEChERS-UHPLC-Q-TOF with automated identification based on MS/MS similarity algorithms. Food Chemistry, 2022, pp.133573. ⟨10.1016/j.foodchem.2022.133573⟩. ⟨anses-03706205⟩
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