Application progress on chemical pattern recognition in quality control of Chinese materia medica / 中草药

ABSTRACT

Chemometrics is a new cross discipline based on computer and modern technology. It has been widely used in the research of Chinese materia medica (CMM) identification, qualitative characterization, quality control, and group-effect relationship, especially in quality control and evaluation of CMM. In this paper, the application and progress of chemical pattern recognition methods in chemometrics for quality control of CMM in recent years are reviewed. Two unsupervised pattern recognition methods (cluster analysis and principal component analysis) and four supervised pattern recognition methods (soft independent modeling of class analogy, partial least-squares discriminant analysis, support vector machine, and artificial neural network) are described. This paper reviews application of chemical pattern recognition in quality control of CMM from different aspects, including growing areas, herbal origin, processing, identification of the authenticity, etc.