Classification of Rhizoma et Radix Notoperygii by HPLC Fingerprints with the Aid of Chemometrics

Abstract

Chromatographic profiles of Rhizoma et Radix Notoperygii (RRN, “Qianghuo” in Chinese), a complex traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography with diode array detection (HPLC-DAD) at 330 nm. These data profiles were used as fingerprints to investigate quality control classification modeling of the RRN samples. In contrast to the classical methods for discrimination of TCMs, that is, just using common HPLC peaks, all chromatographic profile data were pre-processed by the correlation optimized warping method and polynomial functions; then, these data were submitted as fingerprints (variables) for classification on the basis of sample origin. Chemometrics methods used for calibration modeling and subsequent sample classification-least square support vector machine (LS-SVM), artificial neural network (ANN), and partial least square discriminant analysis (PLS-DA); all produced satisfactory calibrations as well as classification results.

Keywords:

• Classification
• Correlation optimized warping
• Fingerprints
• High performance liquid chromatography
• Radix et Radix Notoperygii

Acknowledgments

The authors are grateful for financial support from the National Natural Science Foundation of China (NSFC201065007) and the State Key Laboratory of Food Science and Technology of Nanchang University (SKLF-MB-201002 and SKLF-TS-200919).

Notes

^a Optimal parameters of the relative weight of the regression error (γ), and the kernel parameter (σ²) were 49.82 and 1.744, respectively.

^b Numbers in brackets represent the number of samples and the samples correctly predicted, respectively.

^c Parameter values: hidden layer = 8; epochs = 800; and mean square error threshold = 0.0001.

^d Four factors were used.