CHEMOMETRIC APPROACH TO THE OPTIMIZATION OF HSSPME/GC-MS FOR THE DETERMINATION OF MULTICLASS PESTICIDE RESIDUES IN FRUITS AND VEGETABLES
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Date
2015
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FFTC-KU International Workshop on Risk Management on Agrochemical
Abstract
A Headspace Solid Phase Microextraction (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 27-4 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 – 500 µg/kg with correlation coefficient greater than 0.99; limit of detection (LOD) and limit of quantification (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 sampling time and improve analytical throughput.