Coupling of GC-MS/MS to Principal Component Analysis for Assessment of Matrix Effect: Efficient Determination of Ultra-Low Levels of Pesticide Residues in Some Functional Foods

• Published in: Food analytical methods 2019-12, Vol.12 (12), p.2870-2885
• Main Authors: Shendy, Amr H., Eltanany, Basma M., Al-Ghobashy, Medhat A., Gadalla, Sohair A., Mamdouh, Wael, Lotfy, Hayam M.
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Clusters; Dilution; Food; Food safety; Food Science; Functional foods & nutraceuticals; Microbiology; PCB; Pesticide residues; Pesticides; Polychlorinated biphenyls; Principal components analysis; Product safety; Residues; Safety; Selectivity; Sensitivity analysis; Test procedures
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-019-01643-z

Functional foods provide nutritional and health benefits, yet they could be contaminated with residues like pesticides and polychlorobiphenyls. These residues affect the safety, quality, and consequently the commercial value of functional foods. Therefore, the validity and efficiency of residue determination methods constitute a major analytical concern. Reduction of matrix effect (ME) has always been the golden key for guaranteed sensitivity, selectivity, and high throughput analysis. This study aims for accurate determination and streamlined quantification of 200 pesticide residues in 16 matrices. Hence, QuEChERS protocol coupled to GC-MS/MS was then employed and separations were obtained in 25 min. Dilution of the final extracts of fresh and herbal samples was carried out to achieve an acceptable balance between sensitivity and peak characteristics. Dilution factors of 1x and 5x were selected for fresh and herbal samples, respectively. Principal component analysis (PCA) was then independently applied on the digitally exported total ion chromatograms (TICs) of the studied matrices and the calculated ME%. PCA score/loading plots of TICs demonstrated the key matrix constituents that influenced the obtained trends. Similarly, three main clusters were obtained after PCA of ME% indicating a dependent relationship between matrix type and the obtained effects. Out of the obtained three clusters, an appropriate representative matrix-matched calibration (R-MMC) was selected for ME compensation. Based on the EU validation guidelines, the proposed protocol was validated at 2 and 10 μg Kg −1 with acceptable method performance. Four proficiency testing (PT) and commercial samples were successfully analyzed. The proposed protocol would help laboratories to increase sample processing capacity and to ensure the safety of functional food products. This work should serve in setting standards that warranty the quality/safety of functional foods by national regulatory authorities.