Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

• Published in: PloS one Vol. 12; no. 10; p. e0186664
• Main Authors: Lee, Byeong-Ju, Kim, Hye-Youn, Lim, Sa Rang, Huang, Linfang, Choi, Hyung-Kyoon
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.10.2017; Public Library of Science (PLoS)
• Subjects: Age; Analysis; Biology and Life Sciences; Cancer; Chromatography; Cultivation; Earth Sciences; Ecology and Environmental Sciences; Epigenetics; Forecasting; Fourier transforms; Ginseng; Herbal medicine; Infrared analysis; Infrared spectroscopy; Korean ginseng; Least squares method; Mathematical models; Methods; Multivariate Analysis; Multivariate statistical analysis; Panax - chemistry; Panax ginseng; Pharmaceuticals; Pharmacy; Physical Sciences; Physiological aspects; Regression analysis; Research and Analysis Methods; Scientific imaging; Spectroscopic analysis; Spectroscopy; Spectroscopy, Fourier Transform Infrared - methods; Statistical analysis; Statistical methods; Statistical prediction; Statistics; Vasoactive intestinal peptide; China; Beijing China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0186664

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.