Application of Multivariate Data Analysis to the Determination of Multiclass Pesticide Residues in Fruits and Vegetables using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry

Abstract

Design of experiment (DOE) was employed to develop a headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-MS) method for multiresidue analysis. The significance of SPME parameters was determined using Placket-Burman (P-B) design. The main effect and the interaction effect of the significant factors were also determined followed by the optimization of the significant factors using central composite design (CCD). A Minitab® statistical software was used to generate both the 27-4 Placket-Burman and the central composite design matrix. The same statistical software was also employed in the determination of the optimum level of the significant parameters using surface response optimizer and desirability surface plot. The most significant factors are: extraction temperature (90%), extraction time (80%), the pH and stirring rate (50% and 60% respectively). The optimum parameters are: Temperature, 62 °C; time, 34 min; NaCl, 10%; stirring, 350rpm; pH, 6. The figures of merit of analytical methodologies was determined using an internal standard calibration method. The linearity of the developed method ranges from 1- 500 µg/kg and correlation coefficient (R2) greater than 0.99. The average recovery was found between 74 – 115% and relative standard deviation ranges from 1.1 – 14%. The developed method was used to analyze 14 multiclass pesticide residues in two fruit (pear and grape) and two vegetable (lettuce and broccoli) samples, and the method was found to be satisfactory with LOD between 0.17 – 7.34 µg/kg and LOQ ranges from 0.55 – 24.50 µg/kg. Keyword: Design of experiment (DOE), solid phase microextraction, response surface optimizer, pesticide residues, Central composite Design