We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.
- Authors
Zhang, Pengjie; Liu, Bing; Mu, Xihui; Xu, Jiwei; Du, Bin; Wang, Jiang; Liu, Zhiwei; Tong, Zhaoyang
- Abstract
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.
- Subjects
MACHINE learning; PARTIAL least squares regression; RAMAN spectroscopy; RICIN; TOXINS; BACTERIAL toxins; DISCRIMINANT analysis
- Publication
Molecules, 2024, Vol 29, Issue 1, p197
- ISSN
1420-3049
- Publication type
Article
- DOI
10.3390/molecules29010197