Detection of paralytic shellfish toxins by near‐infrared spectroscopy based on a near‐Bayesian SVM classifier with unequal misclassification costs

BACKGROUND Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace. RESULTS This paper proposes a rapid detection method for PST in mussels using near‐infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950–1700 nm for PST‐contaminated and non‐contaminated mussel samples were used to build the detection model. Near‐Bayesian support vector machines (NBSVM) with unequal misclassification costs (u‐NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non‐contaminated mussels was far less than that of PST‐contaminated mussels in practice. The u‐NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u‐NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G‐mean and accuracy reached 0.9898 and 0.9944, respectively. CONCLUSION Compared with the traditional support vector machines (SVMs) and the NBSVM, the u‐NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u‐NBSVM model can be used for rapid and non‐destructive PST detection in mussels. © 2023 Society of Chemical Industry.