In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra-high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter-propagation artificial neural network (CP-ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS-DA, SVMDA and CP-ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS-DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.
Keywords: Cynanchi Auriculati radix; Polygoni Multiflori radix; UHPLC fingerprints; adulteration; pattern recognition.
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