Akademik Veri Yönetim Sistemi
Gıda Kimyası Kongresi, Antalya, Türkiye, 3 - 06 Mart 2022, ss.3
Validation of a Multi-Residue Analysis Method for the Detection of Pesticide Residues in Cucumber Pickles Using LC MS/MS
Cucumbers used for pickle processing are generally produced in greenhouses. Plant pests and diseases
sp read rapidly in greenhouses and lead to frequent and intense use of pesticides1
. Presence of pesticide
residues on cucumbers causes serious problems because of the high stability of pesticides at low pH
values occurring during pickle processing2
. So it is necessary to monitor the fate of pesticides during
fermentation and pickling. In this study, validation of a multi-residue analysis method was performed
for the detection of 63 different pesticides in cucumber pickles using LC MS/MS. The extraction and
cleaning up of the pesticides were conducted by using QuEChERS method3
. For validation of the multiresidue analysis, the parameters [selectivity, linearity, limit of detection (LOD), limit of quantification
(LOQ), trueness (recovery %) and precision (intra-day and inter-day)] were evaluated according to
SANTE/12682/2019 Guidelines4
. All pesticides were detected within a very short time of 11.53 minutes.
The linearities of all compounds were quite confident (R2 = from 0.991 to 0.999). The LOD and LOQ
values were between 2.48 to 5.84 (µg kg-1
) and 2.74 to 7.79 (µg kg-1
), respectively. The values were
under the lowest Maximum Residue Limit (10 µg kg-1
) for all pesticides. The recovery percentages of
compounds for two spike levels (10 and 50 µgkg-1
) were calculated between 89.09-116.89% and 85.85-
115.51%, respectively. The highest repeatability RDSr and reproducibility RDSR values were 16.48 and
19.88%, respectively. Thus, findings obtained with the evaluation of all parameters were found to be
compatible with the SANTE validation criteria, so the method was reliable, effective and easy to use for
the detection of pesticides in cucumber pickles using LC MS/MS. This study was funded as a research
project (grant number FKA-2021-531) by the collaboration of Bursa Uludag University and Perla Fruit
Company.