Chemometric approach to the optimization of HS-SPME/GC–MS for the determination of multiclass pesticide residues in fruits and vegetables
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Date
2015-01-09
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Elsevier
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 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 to 500 lg/kg with correlation coefficient greater than 0.99, LOD and LOQ were found between 0.35 and 8.33 lg/kg and 1.15 and 27.76 lg/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
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Abdulra’uf, L. B., & Tan, G. H. (2015). Chemometric approach to the optimization of HS-SPME/GC–MS for the determination of multiclass pesticide residues in fruits and vegetables. Food Chemistry., 177, 267-273. doi: http://dx.doi.org/10.1016/j.foodchem.2015.01.031