RESOLUTION OF HIGH PERFORMANCE LIQUID CHROMATOGRAPHIC FINGERPRINTS OF RHIZOMA CURCUMAE BY APPLICATION OF CHEMOMETRICS

Abstract

High performance liquid chromatography with diode array detector (HPLC-DAD) was employed for the chromatographic fingerprint analysis and characterization of Rhizoma Curcumae, a traditional Chinese medicine (TCM). Genetic algorithm-partial least squares (GA-PLS) were applied for feature variables selection based on the 11 common peaks. Then, two chemometrics pattern recognition methods, principal component analysis (PCA) and K-nearest neighbor (KNN), were applied to investigate the mean chromatograms of Rhizoma Curcumae samples, which were obtained from four different provinces with two different processing methods. It was found that these samples can be successfully compared and distinguished according to their origin and processing methods. The results indicated that the proposed method could be efficiently used to control the quality of Rhizoma Curcumae samples and providing a reference method for chemical pattern recognition for other TCMs.

Keywords:

• fingerprinting
• genetic algorithm-partial least squares
• high performance liquid chromatography
• pattern recognition
• processing method
• Rhizoma Curcumae

ACKNOWLEDGMENTS

The authors gratefully acknowledge the financial support of this study by the Natural Science Foundation of China (No. NSFC21065007) and the State Key Laboratory of Food Science and Technology of Nanchang University (SKLF-MB-201002 and SKLF-TS-200919).

Notes

^a Sample number of samples not accurately classified.