CHEMOMETRIC APPROACH TO THE OPTIMIZATION OF HSSPME/GC-MS FOR THE DETERMINATION OF MULTICLASS PESTICIDE RESIDUES IN FRUITS AND VEGETABLES

dc.contributor.authorAbdulrauf, L. B.
dc.contributor.authorTan, G. H.
dc.date.accessioned2025-10-21T13:00:53Z
dc.date.available2025-10-21T13:00:53Z
dc.date.issued2015
dc.description.abstractA 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.
dc.identifier.urihttps://ap.fftc.org.tw/article/993
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6037
dc.language.isoen
dc.publisherFFTC-KU International Workshop on Risk Management on Agrochemical
dc.titleCHEMOMETRIC APPROACH TO THE OPTIMIZATION OF HSSPME/GC-MS FOR THE DETERMINATION OF MULTICLASS PESTICIDE RESIDUES IN FRUITS AND VEGETABLES
dc.typeOther
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