Classification of raw and roasted Semen Cassiae samples with the use of Fourier transform infrared fingerprints and least squares support vector machines.
Raw and roasted Semen Cassiae seeds, a complex traditional Chinese medicine (TCM), are used as examples to research and develop a method of classification analysis based on measurements of Fourier transform infrared (FT-IR) spectral fingerprints. Eighty samples of the TCM were measured in the mid-infrared range, 400-2000 cm(-1) (KBr pellets), and the complex overlapping spectra were submitted for interpretation to a principal component analysis least squares support vector machine (PC-LS-SVM), kernel principal component analysis least squares support vector machine (KPC-LS-SVM), and radial basis function artificial neural networks (RBF-ANN). The LS-SVM models were developed with an RBF kernel function and a grid search technique. Training models were constructed with the use of raw and first-derivative spectra and these were then verified by another data set containing both raw and roasted spectral objects. It was demonstrated that the first-derivative data set produced the best separation of the spectral objects. In general, satisfactory analytical performance was obtained with the PC-LS-SVM, KPC-LS-SVM, and RBF-ANN training models and with the classification of the verification spectral objects. With regard to chemometrics modeling, the performance of KPC-LS-SVM was somewhat more economical than that of the PC-LS-SVM model. It would appear that the latter relatively simple model would be sufficient for application to most small to medium sized FT-IR fingerprint data sets, but with larger matrices the more complex models, such as the RBF-ANN and KPC-LS-SVM, may be more advantageous on a computational basis.
Lai
Yanhua
Y
State Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, China.
Ni
Yongnian
Y
Kokot
Serge
S
D001185:Artificial Intelligence; D004365:Drugs, Chinese Herbal / Q000737:chemistry*; D007887:Fabaceae / Q000737:chemistry*; D016018:Least-Squares Analysis; D012639:Seeds / Q000737:chemistry*; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods
D004365:Drugs, Chinese Herbal