Speciation Analysis of Mercury in Seafood by LC-ICP-MS
Posters | 2025 | Shimadzu | AOACInstrumentation
ICP/MS, HPLC
IndustriesFood & Agriculture
ManufacturerShimadzu
Summary
Importance of the Topic
Mercury present in seafood represents a significant health risk, especially in its organic form such as methyl mercury. Conventional total mercury screening provides overall concentration but fails to distinguish between more and less toxic species, which may lead to incomplete risk assessments and underestimation of potential hazards in food products.Objectives and Overview
This study aimed to develop and validate a robust speciation method for mercury in seafood using liquid chromatography coupled with inductively coupled plasma mass spectrometry (LC-ICP-MS). The work evaluated both analytical performance and the effectiveness of an automatic dilution function in the autosampler to streamline calibration and ensure accurate quantification of inorganic mercury, methyl mercury, and ethyl mercury.Methodology and Instrumentation
- Sample Preparation: Following FDA Elemental Analysis Manual 4.8, tuna samples were homogenized, spiked for recovery tests, and extracted with 1% (w/v) L-cysteine-HCl solution. The mixture was heated at 60 °C for 120 minutes with intermittent mixing and then filtered through a 0.45 µm filter before analysis.
- Chromatographic Conditions: Separation of mercury species was performed on a Nexera XS inert HPLC system using the dedicated LC-ICP-MS Method Package for Mercury Speciation Analysis.
- Detection: A Shimadzu ICPMS-2040/2050 instrument coupled to the Nexera XS inert delivered element-specific detection, all controlled via LabSolutions ICPMS TRM software for automated sample injection, dilution, and data acquisition.
- Automatic Dilution: The autosampler’s automatic dilution function was tested for preparing calibration standards at low (0.2 µg/L) and mid (1 µg/L) levels to assess precision and accuracy.
Main Results and Discussion
- Species Separation: Baseline resolution of inorganic mercury, methyl mercury, and ethyl mercury at 0.5 µg/L each was achieved reproducibly.
- Linearity and Sensitivity: Calibration curves exhibited excellent linearity with correlation coefficients exceeding 0.999. The limits of detection were sufficiently low for trace-level monitoring in seafood matrices.
- Precision and Accuracy: Ten replicate analyses of a 0.2 µg/L standard determined low-level detection capability. For a 1 µg/L standard, repeated automatic dilutions yielded high accuracy and repeatability.
- Seafood Analysis: Tuna extracts analyzed via LC-ICP-MS showed spike recoveries of 102% for methyl mercury and 105% for total mercury, meeting quality criteria and demonstrating system suitability.
Benefits and Practical Applications
- Precise Species Differentiation: Enables risk-based evaluation by quantifying highly toxic methyl mercury separately from less harmful species.
- Streamlined Workflow: Single-software control and automatic dilution enhance throughput and reduce manual error.
- Versatile Implementation: Method can be applied across various seafood and food matrices for routine quality control and regulatory compliance.
Future Trends and Applications
- Advanced Separations: Adoption of ultra-high-performance LC for faster analyses and improved resolution.
- Expanded Matrices: Application to other foods, biological tissues, and environmental samples to broaden monitoring scope.
- On-Line Cleanup: Integration of automated sample preparation modules to minimize manual handling and reduce analysis time.
- High-Resolution MS Coupling: Utilization of HR-ICP-MS or hybrid mass spectrometers for speciation of emerging mercury compounds and isotopic studies.
Conclusion
The LC-ICP-MS method developed using the Shimadzu ICPMS-2040/2050 coupled to the Nexera XS inert provides a reliable and sensitive approach for mercury speciation in seafood. Excellent linearity, low detection limits, and high recovery rates confirm its suitability for accurate risk assessment and regulatory monitoring.Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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