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• W2093775501 abstract "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." @default.
• W2093775501 created "2016-06-24" @default.
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• W2093775501 date "2010-06-01" @default.
• W2093775501 modified "2023-09-27" @default.
• W2093775501 title "Classification of Raw and Roasted<i>Semen Cassiae</i>Samples with the Use of Fourier Transform Infrared Fingerprints and Least Squares Support Vector Machines" @default.
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• W2093775501 doi "https://doi.org/10.1366/000370210791414362" @default.
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