An ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-Tof MS)-based metabolomic technique was applied for metabolite profiling of 60 Panax ginseng samples aged from 1 to 6 years. Multivariate statistical methods such as principal component analysis and hierarchical clustering analysis were used to compare the derived patterns among the samples. The data set was subsequently applied to various metabolite selection methods for sophisticated classification with the optimal number of metabolites. The results showed variations in accuracy among the classification methods for the samples of different ages, especially for those aged 4, 5, and 6 years. This proposed analytical method coupled with multivariate analysis is fast, accurate, and reliable for discriminating the cultivation ages of P. ginseng samples and is a potential tool to standardize quality control in the P. ginseng industry.

The age of the ginseng plant has been considered as an important criterion to determine the quality of this species. For age differentiation and structure interpretation of age-dependent key constituents of Panax ginseng, hairy root (fine root) extracts aged from four to six years were analyzed using a nontargeted approach with ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Various classification methods were used to determine an optimal method to best describe ginseng age by selecting influential metabolites of different ages. Through the metabolite selection process, several age-dependent key constituents having the potential to be biomarkers were determined, and their structures were identified according to tandem mass spectrometry and accurate mass spectrometry by comparing them with an in-house ginsenoside library and with literature data. This proposed method applied to the hairy roots of P. ginseng showed an improved efficiency of age differentiation when compared to previous results on the main roots and increases the possibility of the identification of key metabolites that can be used as biomarker candidates for quality assurance in ginseng.

Fingerprinting analysis of fresh ginseng according to root age was performed using 1H-NMR spectroscopy and multivariate analysis techniques. Various peaks were detected in the aliphatic (0-3 ppm), sugar (3-6 ppm), and aromatic (6-9 ppm) regions of the 1H-NMR spectra of the water extracts of fresh ginseng root. The use of principal components (PCs) analysis (PCA) for metabolomic profiling allowed the large 1H-NMR data set obtained for various metabolites to be reduced to PC1, PC2, and PC3. Two dimensional score plots showed clear separations with these three components at different roots ages, and explained 89.6% of the total variance. Canonical discriminant analysis identified the ginseng roots at various ages from the NMR results with over 89.9% discrimination accuracy. These results indicate that the combination of 1H-NMR and PCA provides a very promising tool for the authentication and quality control of fresh ginseng roots at different ages.

Korean ginseng (Panax ginseng C.A. Meyer) is one of the most popular medicinal herbs used in Asia, including Korea and China. In the present study lipid profiling of two officially registered cultivars (P. ginseng 'Chunpoong' and P. ginseng 'Yunpoong') was performed at different cultivation ages (5 and 6 years) and on different parts (tap roots, lateral roots, and rhizomes) using nano-electrospray ionization-mass spectrometry (nanoESI-MS). In total, 30 compounds including galactolipids, phospholipids, triacylglycerols, and ginsenosides were identified. Among them, triacylglycerol 54:6 (18:2/18:2/18:2), phosphatidylglycerol 34:3 (16:0/18:3), monogalactosyldiacylglycerol 36:4 (18:2/18:2), phosphatidic acid species 36:4 (18:2/18:2), and 34:1 (16:0/18:1) were selected as biomarkers to discriminate cultivars, cultivation ages, and parts. In addition, an unknown P. ginseng sample was successfully predicted by applying validated partial least squares projection to latent structures regression models. This is the first study regarding the identification of intact lipid species from P. ginseng and to predict cultivars, cultivation ages, and parts of P. ginseng using nanoESI-MS-based lipidomic profiling with a multivariate statistical analysis.

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