Chemometric approach to the optimization of HS-SPME/GC-MS for the determination of multiclass pesticide residues in fruits and vegetables

Abstract

An HS-SPME method was developed using multivariate experimental designs, which was conducted in two stages. The significance of each factor was estimated using the Plackett-Burman (P-B) design, for the identification of significant factors, followed by the optimization of the significant factors using central composite design (CCD). The multivariate experiment involved the use of Minitab® statistical software for the generation of a 2(7-4) P-B design and CCD matrices. The method performance evaluated with internal standard calibration method produced good analytical figures of merit with linearity ranging from 1 to 500 μg/kg with correlation coefficient greater than 0.99, LOD and LOQ were found between 0.35 and 8.33 μg/kg and 1.15 and 27.76 μg/kg respectively. The average recovery was between 73% and 118% with relative standard deviation (RSD=1.5-14%) for all the investigated pesticides. The multivariate method helps to reduce optimization time and improve analytical throughput.

Keywords: Central composite design; Fruits and vegetables; GC–MS; Pesticide residues; Plackett–Burma design; SPME.