Marine toxins in seafood: Recent updates on sample pretreatment and determination techniques

Bian, Yu; Zhang, Yuan; Feng, Xue-song; Gao, Hui-yuan

doi:10.1016/j.foodchem.2023.137995

Cited by 6 publications

(1 citation statement)

References 109 publications

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“…Detecting targets at early stages is the primary measure for prevention, emphasizing the need to devise efficient detection techniques. Traditional toxin detection methods, such as mass spectrometry, capillary electrophoresis, and biological methods, are both time- and labor-intensive and fail to meet the immediacy and convenience needs of online screening [ 4 , 5 , 6 ]. Raman spectroscopy is a valuable technique for researching molecular structures.…”

Section: Introductionmentioning

confidence: 99%

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

Zhang,

Liu,

et al. 2023

Molecules

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Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

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Section: Introductionmentioning

confidence: 99%