Animal research on classifying the properties of traditional Chinese medicine using Raman spectroscopy combined with machine learning methods
10.3969/j.issn.1672-8467.2024.05.022
- VernacularTitle:拉曼光谱结合机器学习算法分类中药药性的动物研究
- Author:
Zi-Ren CHEN
1
;
Shuo ZHANG
;
Cong-Jian XU
Author Information
1. 复旦大学附属妇产科医院中西医结合科 上海 200011
- Keywords:
Raman spectroscopy;
machine learning;
traditional Chinese medicine;
medicine property;
mouse
- From:
Fudan University Journal of Medical Sciences
2024;51(5):795-799
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the construction and verification of the classification model for the five properties of traditional Chinese medicine:warm,cool,cold,hot,and neutral.Methods Urine samples of mice after taking Chinese medicine of different properties were selected as research objects,and Raman spectroscopy-related technology was used for detection.The obtained data set was classified into training set and test set,and the classification model was constructed using four machine learning methods:random forest,extreme gradient boosting,support vector machine,and logistic regression.The model performance was evaluated using precision,recall,F1 score,and accuracy.Results A total of 4 888 sets of spectra were collected in this study,of which 80%,totaling 3 910 sets of spectral data,were used to build the model,and the remaining 20%,totaling 978 sets of spectral data,were used to test model performance.The accuracy of the random forest model was 92%,the extreme gradient boosting model was 87%,the support vector machine model was 83%,and the logistic regression model was 75%.The Raman shifts with the highest classification weights were 872,1 012,1 108,1 190 and 1 668 cm-1.Conclusion Raman spectroscopy combined with machine learning algorithms can be used to classify the five medicinal properties of traditional Chinese medicine,among which the random forest model has the best effect.
