Fast Detection of Diarrhetic Shellfish Poisoning Toxins in Mussels Using NIR Spectroscopy and Improved Twin Support Vector Machines

Diarrhetic shellfish poisoning (DSP) toxins are potent marine biotoxins. It can cause a severe gastrointestinal illness by the consumption of mussels contaminated DSP toxins. New methods for effectively and rapidly detecting toxins-contaminated required. In this study, we used near-infrared (NIR) reflection spectroscopy combined with pattern recognition to detect range 950-1700 nm, spectral data healthy were acquired. To select optimal waveband subsets, selection algorithm Gaussian membership function based on fuzzy rough set theory was applied. Considering that from an imbalanced classification problem, improved approach twin support vector machines (TWSVM) explored, which is centered kernel alignment. The influences parameters regularization hyperparameters TWSVM (ITWSVM) performance models analyzed. Compared conventional SVM, TWSVM, other state-of-the-art algorithms (such as multi-layer perceptron, extreme gradient boosting adaptive boosting), our proposed model exhibited better in little affected imbalance ratio. For model, F-measure reached 0.9886, detection accuracy 98.83%. We explored physical basis analyzing relationship between occurrence overtone combination bands selected wavebands. This study supports NIR innovative, rapid, convenient analytical method mussels.