Comprehensive multi-residue analysis of 87 pesticides in Egyptian food commodities: Method validation and dietary...

A comprehensive multi-residue analytical method was developed and validated for the simultaneous determination of 87 pesticides in diverse food matrices using a modified QuEChERS extraction protocol coupled with gas chromatography-tandem mass spectrometry (GC-MS/MS). Method validation according to SANTE/11312/2021 guidelines demonstrated excellent analytical performance, achieving a recovery range of 70-120% at three fortification levels (0.01, 0.05, and 0.1 mg/kg). Relative standard deviations for intraday and interday precision remained below 21% for all validated compounds. The method achieved limits of quantification ranging from 0.0005 to 0.01 mg/kg. The practical applicability was demonstrated through an extensive survey of 591 real food samples comprising eight commodities (apple, chamomile, caraway, dried mint, fennel, green beans, hibiscus, and rice), which revealed widespread pesticide contamination, with 325 samples (54.9%) containing detectable residues. Thirty-eight different pesticide compounds were identified, with chlorpyrifos, cypermethrin, and profenofos being the most frequently detected. Dried herbs showed the highest contamination rates (63.1% for mint, 64.2% for hibiscus), with some samples containing up to eight different pesticides simultaneously. Human health risk assessment through Target Hazard Quotient (THQ) calculations identified several pesticide-commodity combinations within the moderate concern range (THQ > 0.1), particularly chlorpyrifos in rice (THQ = 0.286) and profenofos in dried mint (THQ = 0.098). These values, while below the action threshold of unity, indicate meaningful contributions to chronic pesticide exposure warranting enhanced regulatory monitoring and targeted intervention strategies. The environmental sustainability assessment yielded an AGREE score of 0.60, reflecting reasonable green analytical performance despite the inherent challenges of trace-level multi-residue analysis.