Succession medicinal substances of calculus bovis with infrared spectroscopy coupled with machine learning methods

doi:10.19803/j.1672-8629.20210405

Abstract

Abstract: Objective To explore a method based on machine learning for distinguishing succession medicinal substances of calculus bovis with infrared spectroscopy. Methods After being preprocessed using an autoscale method, data on infrared spectra was analyzed with unsupervised analysis methods and supervised ones respectively. With the help of a genetic algorithm, the most suitable model type and related parameters were studied. The most suitable model type was the XY-Fused network with 25(5×5) as the number of neurons and 1000 set as the epoch. The model with boundaries was trained with the batch pattern. Nine samples were selected randomly among the 43 samples as the test set. The rest were used as the calibration set. Results Accuracies of cross validation were 94.1%, 94.1%, 94.1%, respectively, and the average was 94.1%. Prediction accuracies for the test set were 100%, 83.3%, 100%, respectively, and the average was 94.4%. Conclusion The model established here can be used for distinguishing succession medicinal substances of cow-bezoar with infrared spectroscopy, and is of referential value for studies on succession medicinal substances of calculus bovis.

Key words: calculus bovis, infrared spectrum, machine learning, data analysis, identification

CLC Number:

R991
R994.11

SHI Yan, WANG Xiaowei, WEI Feng, MA Shuangcheng. Succession medicinal substances of calculus bovis with infrared spectroscopy coupled with machine learning methods[J]. Chinese Journal of Pharmacovigilance, 2023, 20(2): 140-145.

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