文献类型： 外文期刊
作者： Zhang, Yanying 1 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
2.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Peoples R China
关键词： Machine learning; Medicinal plant; Multi-source data; Data fusion; Application
期刊名称：JOURNAL OF PHARMACEUTICAL ANALYSIS （ 影响因子：8.8； 五年影响因子：7.2 ）
ISSN： 2095-1779
年卷期： 2023 年 13 卷 12 期
页码：
收录情况： SCI
摘要： In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of Xi'an Jiaotong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- 相关文献
作者其他论文 更多>>
-
Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Gastrodia elata f. glauca; Fourier transform infrared spectroscopy; Deep learning; Data driven version of soft independent; modeling of class analogy
-
ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy
作者：Li, Xiaokun;Zeng, Pan;Wu, Xunxun;Yang, Xintong;Liu, Peizhong;Diao, Yong;Lin, Jingcang;Liu, Peizhong;Wang, Yuanzhong
关键词：Antioxidant activity; FT-IR; Gentian; Deep Learning; Chemometrics
-
Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves
作者：Zhang, Yanying;Zhang, Yanying;Zhu, Xinyan;Wang, Yuanzhong
关键词：Machine learning; Eucommia ulmoides leaves; Geographical traceability; Content prediction; Quality evaluation
-
ATR-FTIR Spectroscopy Preprocessing Technique Selection for Identification of Geographical Origins of Gastrodia elata Blume
作者：Liu, Hong;Li, Jieqing;Liu, Hong;Wang, Yuanzhong;Liu, Honggao
关键词：ATR-FTIR spectroscopy; data preprocessing; DD-SIMCA; Gastrodia elata Blume; GBM; PLS-DA; SVM
-
The method based on ATR-FTIR spectroscopy combined with feature variable selection for the boletus species and origins identification
作者：Ji, Zhiyi;Li, Jieqing;Ji, Zhiyi;Wang, Yuanzhong;Liu, Honggao
关键词：feature variable selection; food safety; mid-infrared spectroscopy; species identification; traceability; wild boletus
-
Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics
作者：Zheng, Chuanmao;Li, Jieqing;Zheng, Chuanmao;Wang, Yuanzhong;Liu, Honggao
关键词：Boletus bainiugan; HS-SPME-GC-MS; VOCs; 2DCOS; Chemometrics; Quality estimation
-
A fast method for predicting adenosine content in porcini mushrooms using Fourier transform near-infrared spectroscopy combined with regression model
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Fourier transform near-infrared spectroscopy; Porcini mushrooms; Adenosine; Partial least squares regression
