Multi-wavelength HPLC fingerprint similarity metric for cold-hot nature identification of Chinese herbal medicines

Cold-hot nature theory is the core basic theory of the nature of Chinese herbal medicines (CHMs). It is found that the material basis of cold-hot nature is CHM ingredients. In view of this, our group proposed a scientific hypothesis that “CHMs with similar nature should have similar material basis”. To demonstrate this hypothesis, we developed a novel multi-wavelength high performance liquid chromatography (HPLC) fingerprint similarity metric scheme for cold-hot nature identification. We explored a multi-wavelength distance metric learning model to compute the similarity of CHM ingredients, and developed an improved k-nearest neighbor algorithm based on multi-wavelength HPLC fusion (KMHF) to predict cold-hot nature of CHMs. Firstly, multi-wavelength HPLC fingerprints were used to extract the characteristic information of CHM ingredients. Secondly, we defined the similarity of CHM ingredients as semantic relevance and fingerprint similarity. We studied a multi-wavelength distance metric to measure the similarity of CHM ingredients. The learned distance metric could discover complementary characteristics of different wavelength HPLC through an optimization algorithm. Finally, an improved multi-wavelength k-nearest neighbor algorithm KMHF was proposed to analyze the relationship between cold-hot nature and CHM ingredients. Numerous experiments were designed to test the feasibility of the proposed KMHF algorithm. Experimental results indicate that the performance of our KMHF algorithm outperforms that of the compared algorithms. Experimental results demonstrate that the hypothesis that CHMs with similar cold-hot nature have similar material basis. The KMHF model is evaluated to be feasible for nature identification.