An analytical and machine learning approach for total mercury and methylmercury determination in squid: enhancing food safety testing and traceability monitoring systems
Year: 2025
Type: article
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 L-cysteine, 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 cost-effective workflow enhances mercury monitoring while ensuring safety and traceability with minimal resource requirements. • Mercury determination for seafood safety assessment and traceability monitoring. • Rapid quantification of MeHg and THg achieved via direct mercury analysis. • Optimized analysis with reduced sample mass and reagent use while ensuring accuracy. • Support vector machine can distinguish squid origins based on MeHg and THg patterns.
Source: Food Chemistry
Institutions: University of Pardubice, University of Parma
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: hybrid
Estimated APC paid: $4,930
Award: G4905973702
OpenAlex ID: W4415327296