Quality comparision of Flos Lonicerae Japonicae by several dissimilarity methods

Computing similarity is extremely important in modern scientific research such as computational biology and data mining. In this paper, we put forward the concept of quantitative dissimilarity (QDS) and define a series of formulations to measure how similar or dissimilar between two objects are in quality and content for their n-dimentional properties, which is based on a simple mathematical model. Through establishing the HPLC fingerprints (HPLC-FPs) of Flos Lonicerae Japonicae (FLJ) to obtain n-dimentional vectors, in which each peak area serves as an element, the authentical quality control of FLJ were successfully implemented by several QDSs. The presented dissimilarity methods can accurately and quantitatively assess all objects differences in n-dimentional properties.