Application of random forest algorithm in fingerprint of Chinese medicine: different brands of Xiasangju granules as example

Abstract

To establish a random forest algorithm for identifying and classifying different brands of Xiasangju granules, and provide effective reference for identifying multi-index complex fingerprint. HPLC method was used to collect the fingerprint of 83 batches of Xiasangju granules from different manufacturers. The classification of Xiasangju granules samples based on chromatographic fingerprints was identified by chemometric methods including principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA) and random forest analysis (RF). The superiority of the above three chemometric methods was compared. The results showed that the fingerprints of 83 batches of Xiasangju granules were established in this study. PCA could only explicate 56.52% variance contribution rate and could not completely classify the samples; PLS-DA analysis was superior to PCA, explicating 63.43% variance contribution rate and could obtain certain separation; RF could well classify the samples into 3 types, and the predication accuracy of the proposed method was 96.5%. Therefore, The results indicate that RF combined with HPLC fingerprint could effectively construct traditional Chinese medicine quality control and analysis system.