Multivariate optimization of high-pressure supercritical carbon dioxide extraction technique for analysis of selected pesticides in green coffee beans

Abstract

Residues of pesticides require efficient and selective sample preparation, due to their existence in trace levels and the complex nature of food matrices. The objective of this study was to use a multivariate experimental design for the determination of multi-residue pesticides in the green coffee beans utilizing supercritical carbon dioxide (sc-CO₂) as extraction solvent. In this study, an extraction method based on sc-CO₂ as a solvent, at high pressure using Al₂O₃ as a masking agent for caffeine removal, was developed for the determination of pesticide residues in green coffee beans. A Box-Behnken experimental design was utilized to optimize extraction parameters, including pressure (200-800 bar), temperature (40-70 ℃), and volumes of sc-CO₂ (10-40 mL). The optimum extraction conditions were found to be 588 bar, 46 °C, and 40 mL. The method's performance was evaluated by extracting 5 µg g^-1 spiked green coffee bean samples, resulting in good linearity (R² ≥ 0.995); repeatability (3.7-10.3%), and reproducibility (1.6-8.4%). Limit of detection (LODs), Limit of quantification (LOQs), and recoveries were in the ranges 0.02-0.05 µg g^-1, 0.08-0.13 µg g^-1, and 86-97%, respectively. The developed method could be used as an alternative method for the determination of α-hexachlorohexane (α-HCH), lindane, b-hexachlorohexane (b-HCH), and malathion in green coffee beans.

KEY WORDS: sc-CO₂, GC-MS, Green coffee bean, Multivariate optimization, Pesticide residues

Bull. Chem. Soc. Ethiop. 2026, 40(1), 183-197.                                                      

DOI: https://dx.doi.org/10.4314/bcse.v40i1.15