Abstract
An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.
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