Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs.
J Sci Food Agric
; 104(4): 1984-1991, 2024 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-37899531
ABSTRACT
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.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Bivalves
/
Máquina de Vetores de Suporte
Limite:
Animals
/
Humans
Idioma:
En
Revista:
J Sci Food Agric
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China