ABSTRACT
Objective: The isotopic ratios of light elements (C/N/H/O) in Panax japonicus from six producing areas were determined with isotope ratio mass spectrometry.Methods: Three methods, including linear discrimination (LD), gaussian kernel support vector machine (SVM), and the back-propagation learning algorithm of pattern recognition based on neural network toolbox (BPN) were employed to establish a model for P. japonicus geographical origin discrimination. Results: The results showed that stable isotope carbon (δ 13C) had obvious regional characteristics, which will be used to effectively distinguish the origin of P. japonicus. The methods of LD and BPN could classify the geographical origin of P. japonicus from six producing areas, both of which showed that the accuracy rates were 100% using training dataset. Conclusion: Therefore, the stable isotope technique combined with LD and BPN method can effectively trace the origin of P. japonicus.