Study of the Underground Parts Identification and Saponins Content Prediction of Panax Notoginseng Based on FTIR Combined with Chemometrics
LI Yun1,2,3, ZHANG Ji1,2, JIN Hang1,2, WANG Yuan-zhong1,2*, ZHANG Jin-yu1,2,3*
1. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
2. Yunnan Technical Center for Quality of Chinese Materia Medica, Kunming 650200, China
3. College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
Abstract：Phenomenon of adulterated traditional Chinese medicine (TCM) are still common in TCM market today. Unscrupulous traders used fibrous root powder pretending to be main root and rhizome powder of Panax notoginseng, and such behavior has serious influence on the quality and efficacy of Panax notoginseng. In this study, we have established a rapid method to discriminate the main root, rhizome and fibrous root powder and detect saponins content of Panax notoginseng in order to provide some research bases for rapid quality assessment of Panax notoginseng. A total of 60 Fourier transform infrared (FTIR) spectra of the main root, rhizome and fibrous root powder of Panax notoginseng were collected, and ultra-high performance liquid chromatography (UPLC) was used for measuring the content of notoginsenoside R1, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd of samples. The origin data of identify model were processed by ordinate normalization and second derivative, and 2/3 of the 60 individuals were selected to form the calibration set by using Kennard-stone algorithm as well as the other 1/3 were used as validation set. Calibration set data were used to establish the discriminant model of support vector machine (SVM) and the cross-validation was used for screening optimal parameters c and g, and validation set data were used to verify the results of SVM discriminant model for external validation. The origin data used to predict saponins content were calculated by first (1D) and second derivative (2D), Savitsky-Golay smoothing with five, seven, nine, and eleven points. 2/3 of the 60 individuals were selected to form the calibration set and the rest were used as validation set. The orthogonal signal correction-partial least squares regression (OSC-PLSR) model was established by calibration set and the validation set was utilized to verify the results of the model for external validation. Results showed that, (1) with second derivative processing, the overlapped peak of FTIR spectra were efficiently separated and the resolution of the spectra has been improved. (2) The optimal parameters c and g of support vector machine calculated by cross-validation were 2.828 43 and 4.882 81×10-4 respectively and the optimal accuracy rate of calibration set was 100%. (3) The parameter of support vector machine model was set as the optimal parameter and the accuracy rate of validation set was 100%, and all samples in validation set have been identified correctly. (4) The prediction content of greatest model of notoginsenoside R1, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd was close to the content measured by UPLC. The result indicated that, FTIR combined with support vector machine could effectively identify the main root, rhizome and fibrous root powder of Panax notoginseng. OSC-PLSR could accurately predict the content of four saponins of Panax notoginseng. In summary, the FTIR spectroscopy could provide a rapid and effective method for the quality control of Panax notoginseng.
李 运，张 霁，金 航，王元忠，张金渝. FTIR结合化学计量学对三七地下部位鉴别及皂苷含量预测[J]. 光谱学与光谱分析, 2019, 39(01): 103-108.
LI Yun, ZHANG Ji, JIN Hang, WANG Yuan-zhong, ZHANG Jin-yu. Study of the Underground Parts Identification and Saponins Content Prediction of Panax Notoginseng Based on FTIR Combined with Chemometrics. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(01): 103-108.
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