Conventional Chromatographic Techniques and Biosensors for Mercury Speciation in Seafood: A Systematic Review

TL;DR

This paper reviews methods for detecting mercury in seafood, comparing traditional chromatography with newer biosensors.

Contribution

The study systematically compares recent advances in mercury speciation techniques in seafood from 2014 to 2025.

Findings

Hyphenated techniques like HPLC–ICP-MS and GC–ICP-MS are effective but costly and time-consuming.

Biosensors offer portability and speed but face challenges in selectivity and standardization for seafood analysis.

Abstract

Mercury (Hg) is of significant concern due to its toxicity, which strongly depends on its chemical forms, and organic mercury compounds, particularly methylmercury (MeHg), are considered the most toxic species. Therefore, mercury speciation analysis is essential for accurate exposure and risk assessment. The primary dietary source of mercury exposure for humans is food consumption, particularly seafood. Consequently, numerous studies have focused on developing analytical techniques for the identification, characterization, and quantification of Hg species in seafood. This review evaluates and compares recent developments (2014–2025) in analytical techniques for the identification and quantification of Hg species in seafood, focusing on both traditional chromatographic methods and emerging methodologies based on biosensors. Hyphenated techniques such as HPLC–ICP-MS and GC–ICP-MS have enabled significant advancements in mercury speciation analysis. Although chromatographic methods are highly effective and widely accepted due to their accuracy and sensitivity, they often require costly instrumentation, skilled operators, and lengthy analysis times. Biosensors are increasingly proposed as alternatives; however, their applicability to seafood analysis remains limited despite advantages such as portability, simplicity, and rapid response. They are still under development and face challenges in selectivity, stability, and standardization. This review provides an overview of existing methodologies, comparing their advantages and limitations, aiming to guide improvements toward optimal methods incorporating all advantageous features.

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