title: [Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].
abstract: Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.
authors.forename: Sheng-Nan
authors.initials: SN
authors.lastname: Han
mesh_terms: D048088:Informatics / Q000379:methods*; D016018:Least-Squares Analysis; D008516:Medicine, Chinese Traditional / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D013329:Structure-Activity Relationship; D060388:Support Vector Machine