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Updated: Jul 12, 2025
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with
Yao Liu1, Jianfang Xiong2, Fu Qiao2,3
1School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, China.
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.
