Human beings are up against serious health hazards when ingesting toxins-contaminated shellfish.It is badly required to identify shellfish contaminated by toxins.Near-infrared spectroscopy and a class-specific residual constraint nonnegative representation classification (CRNRC) were applied to recognize toxins-contaminated mussels rapidly.The changes in the tissue of mussels contaminated with diarrhea shellfish toxins (DST) could be reflected in the near-infrared spectral curves.The CRNRC model was used to classify healthy and DST-contaminated mussel samples with the preprocessed near-infrared spectra of mussels as input.Class-specific residual terms and collaborative representation were introduced into the CRNRC model to relate the coding with classification.The coding vectors of collaborative representation were shown for CRNRC.The optimal parameters affecting the performance of the CRNRC model were determined through experiments.The experimental results showed that CRNRC model was superior to collaborative representation classification, and non-negative representation classification (NRC) for the evaluation indexes of average accuracy, F-measure, and 1-specificity.The study indicated that NIRS combined with the CRNRC could distinguish DST-contaminated mussel samples, which had the advantages of intelligence, non-destruction accuracy, and without chemical reagents.The detection method of CRNRC with NIRS would be extended to detect other seafood products, for example, testing the level of nuclear contamination in seafood products, which could ensure human beings ingest healthy seafood products.
QIAO Fu
,
LIU Zhongyan
,
LIU Yao
. A rapid detection method of toxins-contaminated mussels based on near-infrared spectroscopy combined with collaborative representation[J]. Food and Fermentation Industries, 2024
, 50(12)
: 292
-298
.
DOI: 10.13995/j.cnki.11-1802/ts.037804
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