食品与发酵工业2024，Vol.50Issue(12)：292-298,7.DOI:10.13995/j.cnki.11-1802/ts.037804
基于近红外光谱与协同表示方法的贻贝毒素快速检测
A rapid detection method of toxins-contaminated mussels based on near-infrared spectroscopy combined with collaborative representation
摘要
Abstract
Human beings are up against serious health hazards when ingesting toxins-contaminated shellfish.It is badly required to i-dentify shellfish contaminated by toxins.Near-infrared spectroscopy and a class-specific residual constraint nonnegative representation clas-sification(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 collab-orative 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 ex-periments.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-de-struction 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.关键词
腹泻贝类毒素/近红外光谱/贻贝/类别相关残差约束/非负表示Key words
diarrheal shellfish toxins/near infrared spectroscopy/mussels/class-specific residual constraint/non-negative representa-tion引用本文复制引用
乔付,刘忠艳,刘瑶..基于近红外光谱与协同表示方法的贻贝毒素快速检测[J].食品与发酵工业,2024,50(12):292-298,7.基金项目
国家自然科学基金项目(62005109) （62005109）
岭南师范学院红树林研究所开放项目(PYXM04) （PYXM04）
