Detection of mussels contaminated with cadmium by near‐infrared reflectance spectroscopy based on RELS‐TSVM
Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700 nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.
Authors
- Fu Qiao
- Yao Liu (ORCID: https://orcid.org/0000-0003-1008-8512)
- Zhen Xu (ORCID: https://orcid.org/0000-0002-8901-1819)
Institutions
- Lingnan Normal University (CN)
- Heilongjiang Provincial Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Journal of Food Science
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1111/1750-3841.17471
- Citations
- 3
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.71
Funders
- National Natural Science Foundation of China
- Lingnan Normal University
- Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province