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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25310%2F25%3A39923278" target="_blank" >RIV/00216275:25310/25:39923278 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S030881462504018X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S030881462504018X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.foodchem.2025.146766" target="_blank" >10.1016/j.foodchem.2025.146766</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Food Chemistry

ISSN

0308-8146

e-ISSN

1873-7072

Svazek periodika

496

Číslo periodika v rámci svazku

December 2025

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

146766

Kód UT WoS článku

001604748100001

EID výsledku v databázi Scopus

2-s2.0-105022210058