Identification of Perna viridis contaminated with diarrhetic shellfish poisoning toxins in vitro using NIRS and a discriminative non-negative representation-based classifier

文献类型： 外文期刊

第一作者： Liu, Yao

作者： Liu, Yao;Liu, Zhongyan;Qiao, Fu;Qiao, Fu;Xu, Lele;Xu, Zhen

作者机构：

关键词： Near-infrared spectroscopy; Diarrhetic shellfish poisoning; Perna viridis; Discriminative non-negative representation-based classifier; Identification

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.4； 五年影响因子：3.9 ）

ISSN： 1386-1425

年卷期： 2023 年 294 卷

页码：

收录情况： SCI

摘要： Diarrhetic shellfish poisoning (DSP) toxins are one of the most widespread marine biotoxins that affect aquaculture , human health , their detection has become crucial. In this study, near-infrared reflectance spectroscopy (NIRS) with non-destructive characteristics was used to identify DSP toxins in Perna viridis. The spectral data of the DSP toxin-contaminated and non-contaminated Perna viridis samples were acquired in the 950-1700 nm range. To solve the discrimination of spectra with crossover and overlapping, a discriminative non-negative representation-based classifier (DNRC) has been proposed. Compared with collaborative and non-negative representation-based classifiers, the DNRC model exhibited better performance in detecting DSP toxins, with a classification accuracy of 99.44 %. For a relatively small-scale sample dataset in practical applications, the performance of the DNRC model was compared with those of classical models. The DNRC model achieved the best results for both identification accuracy and F-measure, and its detection performance did not significantly decrease with decreasing sample size. The experimental results validated that a combination of NIRS and the DNRC model can facilitate rapid, convenient, and non-destructive detection of DSP toxins in Perna viridis.

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