An analytical and machine learning approach for total mercury and methylmercury determination in squid: enhancing food safety testing and traceability monitoring systems

Abstract

This study presents the first assessment of total mercury (THg) and methylmercury (MeHg) in squids (Todarodes sagittatus, L.), providing insights into contamination levels and their correlation with the geographical origin. A method based on acidic extraction of THg, re-extraction of MeHg into toluene, and back-extraction into Lcysteine, followed by direct mercury analysis, was refined through a robust multivariate optimization scheme using a fractional factorial design. This approach improved efficiency by reducing sample mass and analysis time, while ensuring high accuracy, precision, and sensitivity. The method quickly confirmed higher median MeHg levels in Mediterranean than in Atlantic squids (1.00 vs. 0.051 mg kg-1), and a higher MeHg/THg ratio in Atlantic samples (84 % vs. 72 %). Support vector machine classification based on principal component analysis scores from THg and MeHg data successfully differentiated squid samples by provenance (AUC = 1). This costeffective workflow enhances mercury monitoring while ensuring safety and traceability with minimal resource requirements.