Journal
VIBRATIONAL SPECTROSCOPY
Volume 56, Issue 2, Pages 154-160Publisher
ELSEVIER
DOI: 10.1016/j.vibspec.2011.01.007
Keywords
Near-infrared spectroscopy; Classification; Wavelet transform; Supervised pattern recognition; Rhizoma Corydalis
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Funding
- Natural Science Foundation of China [NSFC21065007]
- State Key Laboratory of Food Science and Technology of Nanchang University [SKLF-MB201007, SKLF-TS200919]
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Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin. (C) 2011 Elsevier B.V. All rights reserved.
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