Optimization of a LC-MS/MS Method for the detection of pesticides currently registered for Sweet Cherry, Antalya, Turkey, 3 - 06 March 2022, pp.2
Turkey is one of the major producers of sweet cherry and ranks in the first place for worldwide cherry export1. To increase the yield and quality of cherry fruits, insecticides and fungicides have been widely used to control pests and diseases of cherry trees. Since pesticides may cause adverse effects on human health, their residues in fruits should be monitored regularly using a confidential detection method2. Regular monitoring of pesticide residues is an important task for the sustainability of international sweet cherry trade. This study shows the validation data obtained from multi-residue analysis of 24 pesticides registered for sweet cherry using LC-MS/MS. The extraction and cleaning up of the pesticides were conducted by using QuEChERS method3. The validation parameters of all pesticides detected in LCMS/ MS method was quite confident in line with the SANTE/12682/2019 Guideline4. All pesticides were detected within a very short time of 8.92 minutes and no interference was observed. Two specific product ions were used for confirmation analysis based on Commission Decision 2002/657/EC.4 Good linearity for all compounds was obtained (R2 = from 0.997 to 0.999). The recovery percentages of the pesticides for two spike levels (10 and 50 μgkg-1) were calculated between 73.68-110.45% and 92.93-116.50%, respectively. The LOD and LOQ values at low and high concentration range were between 5.16-6.43 (μg kg-1) and 5.53-9.76 (μg kg-1). The values were found appropriate for pesticides with the lowest Maximum Residue Limit [acequinocyl (10 μg kg-1), isopyrazam (10 μg kg-1) and malathion (20 μg kg-1)]. The highest repeatability RDSr and reproducibility RDSR values did not exceed 20% (12.64 and 14.03%, respectively). Results of the current study showed that, this method is fast, reliable and easy to use for the detection of pesticides in cherry fruits using LC MS/MS. This study was funded (grant no.FKA-2021-320) by the collaboration of Bursa Uludag University and Perla Fruit Company.