Metabolomics approach to growth-age discrimination in mountain-cultivated ginseng (Panax ginseng C. A. Meyer) using ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

Abstract

Mountain-cultivated ginseng is typically harvested after 10 years, while ginseng aged over 15 years is considered wild ginseng. This study aims to differentiate mountain-cultivated ginseng by age, as the fraudulent practice of selling low-aged cultivated ginseng disguised as high-aged one is damaging the market. In this study, LC-MS analyzed 98 ginseng samples, and multivariate statistical analysis identified patterns between samples to select influential components. Machine learning models were developed to identify ginseng samples of different ages. The untargeted metabolomic analysis clearly divided samples aged 4-20 years into three age groups. Twenty-two potential age-dependent biomarkers were discovered to differentiate the three sample groups. Three machine learning models were used to predict new samples, and the optimal model was selected. Some biomarkers could determine age phases according to the differentiation of mountain-cultivated ginseng samples. These biomarkers were thoroughly analyzed for variation trends. The machine learning models established using the screened biomarkers successfully predicted the age group of new samples.