Fast Detection of Diarrhetic Shellfish Poisoning Toxins in Mussels Using NIR Spectroscopy and Improved Twin Support Vector Machines
文献类型： 外文期刊
作者： Liu, Yao 1 ; Qiao, Fu 2 ; Xu, Lele 3 ; Wang, Runtao 1 ; Jiang, Wei 2 ; Xu, Zhen 4 ;
作者机构： 1.Lingnan Normal Univ, Sch Elect & Elect Engn, Zhanjiang, Peoples R China
2.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang, Peoples R China
3.Lingnan Normal Univ, Sch Life Sci & Technol, Zhanjiang, Peoples R China
4.Heilongjiang Acad Agr Sci, Sci & Technol Extens Dept, Harbin, Peoples R China
关键词： near-infrared spectroscopy; diarrhetic shellfish poisoning toxins; mussels (Mytilidae); waveband selection; twin support vector machines (TWSVM)
期刊名称：FRONTIERS IN MARINE SCIENCE （ 影响因子：5.247； 五年影响因子：5.72 ）
ISSN：
年卷期： 2022 年 9 卷
页码：
收录情况： SCI
摘要： Diarrhetic shellfish poisoning (DSP) toxins are potent marine biotoxins. It can cause a severe gastrointestinal illness by the consumption of mussels contaminated by DSP toxins. New methods for effectively and rapidly detecting DSP toxins-contaminated mussels are required. In this study, we used near-infrared (NIR) reflection spectroscopy combined with pattern recognition methods to detect DSP toxins. In the range of 950-1700 nm, the spectral data of healthy mussels and DSP toxins-contaminated mussels were acquired. To select optimal waveband subsets, a waveband selection algorithm with a Gaussian membership function based on fuzzy rough set theory was applied. Considering that detecting DSP toxins-contaminated mussels from healthy mussels was an imbalanced classification problem, an improved approach of twin support vector machines (TWSVM) was explored, which is based on a centered kernel alignment. The influences of parameters of the waveband selection algorithm and regularization hyperparameters of the improved TWSVM (ITWSVM) on the performance of models were analyzed. Compared to conventional SVM, TWSVM, and other state-of-the-art algorithms (such as multi-layer perceptron, extreme gradient boosting and adaptive boosting), our proposed model exhibited better performance in detecting DSP toxins and was little affected by the imbalance ratio. For the proposed model, the F-measure reached 0.9886, and detection accuracy reached 98.83%. We explored the physical basis for the detection model by analyzing the relationship between the occurrence of overtone and combination bands and selected wavebands. This study supports NIR spectroscopy as an innovative, rapid, and convenient analytical method to detect DSP toxins in mussels.
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