[Study on application of Fourier transformation near-infrared spectroscopy analysis with support vector machine (SVM)].
Support Vector Machine (SVM) is a method for the research on identifying two types of problem. It is the latest branch in the statistics study theories, and the identification model has a strict mathematics foundation. In this paper, the basic principle and method of SVM are not only introduced, but also applied to chemometrics. One hundred and three rhubarb samples were used as experimental materials. The identification models were established with near-infrared spectroscopy and SVM training method with the intention of identifying whether the rhubarb samples are true or false. The thirty-three samples in training set were identified by the identifying models with the accurate rate of 100%, while seventy estimate samples had an accurate rate of 96.77%. The research result provided the method of identifying the traditional Chinese medicine rhubarb quickly. So, it shows the feasibility of establishing the models with near-infrared spectroscopy and SVM method to identify biological samples. This paper introduced the theme of SVM training method in order to beget the attention of the research members who deal with chemometrics.
Zhang
Lu-Da
LD
College of Science, China Agricultural University, Beijing 100094, China.
Su
Shi-Guang
SG
Wang
Lai-Sheng
LS
Li
Jun-Hui
JH
Yang
Li-Ming
LM
D000465:Algorithms; D004365:Drugs, Chinese Herbal / Q000032:analysis / Q000737:chemistry; D005240:Feasibility Studies; D005583:Fourier Analysis; D011786:Quality Control; D015203:Reproducibility of Results; D012250:Rheum / Q000737:chemistry; D012984:Software; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
D004365:Drugs, Chinese Herbal