Classification of Panax quinquefolium L. and Panax ginseng C. A. Mey. based on FTIR analysis with SVM

The support vector machine (SVM) is a new learning technique based on the statistical learning theory. In the present paper, forty Panax quinquefolium L. samples were used as experimental materials. The classification models were established using Fourier transform infrared spectra(FTIR)-SVM training method with the intention of identifying whether the Panax quinquefolium L. samples are genuine or they are just Panax ginseng C. A. Mey. samples. The thirty samples in training set were identified by the classifying models with an accurate rate of 100%, while the ten estimate samples had an accurate rate of 90%. The research result shows the feasibility of establishing the models with FTIR-SVM method to identify Panax quinquefolium L. samples and Panax ginseng C. A. Mey.