Article(id=1304414821189972211, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414798482010221, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.05.018, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761148800000, receivedDateStr=2025-10-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788926328227, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788926328227, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788926328227, creator=13701087609, updateTime=1788926328227, updator=13701087609, issue=Issue{id=1304414798482010221, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='5', pageStart='1597', pageEnd='2008', issueExtLink='null', onlineDate='null', pubDate='1773244800000', pubDateStr='2026-03-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788926322813, creator='13701087609', updateTime=1788926625459, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304416067925864795, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414798482010221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304416067925864796, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414798482010221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1798, endPage=1813, ext={EN=ArticleExt(id=1304414821647151349, articleId=1304414821189972211, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Traceability study of traditional Chinese medicine origins based machine learning, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To address the issues of strong subjectivity and low efficiency in traditional Chinese medicine origin tracing, we explore the method of Chinese medicine origin tracing based on mid-infrared spectroscopy (MIRS) combined with machine learning algorithm modeling, and provide new technical support for traditional Chinese medicine origin tracing. Methods Based on the mid-infrared spectral dataset, this study performed outlier handling, missing value imputation, first-order difference, high-variance feature selection, distance calculation and clustering analysis, and locally linear embedding (LLE) for dimensionality reduction. Subsequently, we constructed six machine learning models including Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. We then employed the Ivy Algorithm (IVYA) to optimize model parameters. A multidimensional evaluation framework was established, incorporating the area under the receiver operating characteristic (ROC) curve (AUC) alongside accuracy, recall, precision, and F1 score (F1). This framework was used to identify the optimal model for Chinese herbal medicine origin tracing. Results SVM (macro-average AUC = 0.998, F1 = 0.949, accuracy = 0.949, recall = 0.949, precision = 0.956) and ANN (macro-average AUC = 0.999, F1 = 0.940, accuracy = 0.939, recall = 0.939, precision = 0.944) demonstrated optimal performance in identifying the origins of Chinese herbal medicines. They exhibited high consistency in predicting the origins of unknown samples, with core evaluation metrics significantly outperforming RF, LightGBM, KNN, and XGBoost. The SVM model can accurately capture the functional group vibration differences in the medium chemical components of medicinal materials from different origins. ANN models simulate the multifactorial nonlinear coupling effects formed by Chinese herbal medicine origins. Both models not only achieve efficient differentiation among 11 origins but also exhibit complementary strengths: SVM’s globally optimal classification hyperplane excels at processing origins with markedly distinct spectral features, while ANN’s distributed representation capability better suits complex scenarios with high feature overlap. The MIRS method, integrating SVM and ANN, offers rapid, non-destructive, and high-throughput advantages for Chinese herbal medicine traceability. This provides an objective tool for large-scale origin tracing of authentic medicinal materials and delivers a practical technical pathway for the modernization of the Chinese herbal medicine industry. Conclusion MIRS combined with SVM, RF, LightGBM, KNN, XGBoost, and ANN demonstrates effectiveness for Chinese herbal medicine origin tracing. Among these, SVM and ANN emerge as preferred models for this application, offering an interdisciplinary solution. Future integration of multimodal data and deep learning techniques holds promise for enhancing model performance., authors=SHEN Ye, WANG Jisen, XIANG Qing, WANG Mingliang, WANG Lei, DENG Yao, CHEN Zhuo, LIAO Wan, LIN Wei, authorsList=SHEN Ye, WANG Jisen, XIANG Qing, WANG Mingliang, WANG Lei, DENG Yao, CHEN Zhuo, LIAO Wan, LIN Wei, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1304414821580042484, articleId=1304414821189972211, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于机器学习的中药产地溯源研究, columnId=1304140194819629763, journalTitle=中草药, columnName=数据挖掘与循证医学, runingTitle=null, highlight=null, articleAbstract=目的 针对传统中药产地溯源主观性强、效率低等问题，探索基于中红外光谱（mid-infrared spectroscopy，MIRS）结合机器学习算法模型的中药产地溯源方法，为中药产地溯源提供新的技术支撑。方法 以中红外光谱数据集为基础，对中药材的中红外光谱数据进行异常值处理、空缺值填充、一阶差分、高方差特征筛选、距离计算与分组分析及局部线性嵌入算法（locally linear embedding，LLE）等预处理，构建支持向量机（support vector machine，SVM）、随机森林（random forest，RF）、轻量级梯度提升机（light gradient boosting machine，LightGBM）、K-最近邻（K-nearest neighbor，KNN）、极端梯度提升（extreme gradient boosting，XGBoost）、人工神经网络（artificial neural network，ANN）6种机器学习模型，并利用常青藤优化算法（Ivy algorithm，IVYA）优化模型参数，构建受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under curve，AUC）与准确率、召回率、精准率、F1分数（F1 score，F1）的多维度评价指标体系，探讨适合中药产地溯源的最优模型。结果 SVM（宏平均AUC＝0.998、F1＝0.949、准确率0.949、召回率0.949、精确率0.956）与ANN（宏平均AUC＝0.999、F1＝0.940、准确率0.939、召回率0.939，精确率0.944）在中药材产地的识别上表现最优，对未知样本的产地预测结果具有较高一致性，且二者核心评价指标值显著高于RF、LightGBM、KNN、XGBoost。SVM模型可精准捕捉不同产地药材中等化学成分的官能团振动差异，ANN模型可模拟中药产地形成的多因素非线性耦合效应，2种模型不仅实现了11个产地的高效区分，还具有互补性，SVM的全局最优分类面适合处理光谱特征差异显著的产地，而ANN的分布式表征能力更适用于特征重叠度高的复杂场景。MIRS结合SVM与ANN的方法，在中药产地溯源上具有快速、无损、高通量的优势，这为道地药材的规模化产地溯源提供了客观化工具，为中药产业的现代化发展提供了可落地的技术路径。结论 MIRS结合SVM、RF、LightGBM、KNN、XGBoost、ANN用于中药产地溯源具有有效性，其中SVM与ANN可作为中药产地溯源的首选模型，可为中药产地溯源提供跨学科方案，未来可整合多模态数据及深度学习技术提升模型性能。, authors=沈晔1 , 王继森2 , 向晴1 , 王名亮1 , 汪蕾3 , 邓垚1 , 陈卓1 , 廖婉1 , 林薇1 , authorsList=沈晔, 王继森, 向晴, 王名亮, 汪蕾, 邓垚, 陈卓, 廖婉, 林薇, authorCompany=1 成都中医药大学智能医学学院, 药学院, 中药种质资源创新与高效利用实验室, 中药材标准化教育部重点实验室, 四川成都 611137; et al . Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese rice wine) by near infrared spectroscopy combined with chemometrics[J]. J Food Compos Anal , 2022, 111: 104563.Rosa roxburghii Tratt) with chemometrics variable strategy[J]. J Food Compos Anal , 2022, 105: 104225.J Optim Theory Appl , 2025, 205(1): 1.Science , 2000, 290(5500): 2323-2326.Nat Resour Res , 2023, 32(6): 2417-2438.Ieee T Knowl Data En , 2021, 34(10): 4663-4675.et al . Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm[J]. Knowl Based Syst , 2024, 295: 111850.)
中草药
|数据挖掘与循证医学
2026
, 57
(5) :
1798
-1813
基于机器学习的中药产地溯源研究
全屏
沈晔1 , 王继森2 , 向晴1 , 王名亮1 , 汪蕾3 , 邓垚1 , 陈卓1 , 廖婉1 , 林薇1
作者信息
1 成都中医药大学智能医学学院, 药学院, 中药种质资源创新与高效利用实验室, 中药材标准化教育部重点实验室, 四川成都 611137;
通讯作者:
廖婉
作者简介:
沈晔: 沈晔,医学信息工程专业。E-mail:3209325828@qq.com
王继森: 王继森,副主任中药师,硕士研究生,从事药品检验研究。E-mail:93230623@qq.com
Traceability study of traditional Chinese medicine origins based machine learning
SHEN Ye, WANG Jisen, XIANG Qing, WANG Mingliang, WANG Lei, DENG Yao, CHEN Zhuo, LIAO Wan, LIN Wei
Affiliations
doi: 10.7501/j.issn.0253-2670.2026.05.018
文章导航
目的 针对传统中药产地溯源主观性强、效率低等问题，探索基于中红外光谱（mid-infrared spectroscopy，MIRS）结合机器学习算法模型的中药产地溯源方法，为中药产地溯源提供新的技术支撑。方法 以中红外光谱数据集为基础，对中药材的中红外光谱数据进行异常值处理、空缺值填充、一阶差分、高方差特征筛选、距离计算与分组分析及局部线性嵌入算法（locally linear embedding，LLE）等预处理，构建支持向量机（support vector machine，SVM）、随机森林（random forest，RF）、轻量级梯度提升机（light gradient boosting machine，LightGBM）、K-最近邻（K-nearest neighbor，KNN）、极端梯度提升（extreme gradient boosting，XGBoost）、人工神经网络（artificial neural network，ANN）6种机器学习模型，并利用常青藤优化算法（Ivy algorithm，IVYA）优化模型参数，构建受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under curve，AUC）与准确率、召回率、精准率、F1分数（F1 score，F1）的多维度评价指标体系，探讨适合中药产地溯源的最优模型。结果 SVM（宏平均AUC＝0.998、F1＝0.949、准确率0.949、召回率0.949、精确率0.956）与ANN（宏平均AUC＝0.999、F1＝0.940、准确率0.939、召回率0.939，精确率0.944）在中药材产地的识别上表现最优，对未知样本的产地预测结果具有较高一致性，且二者核心评价指标值显著高于RF、LightGBM、KNN、XGBoost。SVM模型可精准捕捉不同产地药材中等化学成分的官能团振动差异，ANN模型可模拟中药产地形成的多因素非线性耦合效应，2种模型不仅实现了11个产地的高效区分，还具有互补性，SVM的全局最优分类面适合处理光谱特征差异显著的产地，而ANN的分布式表征能力更适用于特征重叠度高的复杂场景。MIRS结合SVM与ANN的方法，在中药产地溯源上具有快速、无损、高通量的优势，这为道地药材的规模化产地溯源提供了客观化工具，为中药产业的现代化发展提供了可落地的技术路径。结论 MIRS结合SVM、RF、LightGBM、KNN、XGBoost、ANN用于中药产地溯源具有有效性，其中SVM与ANN可作为中药产地溯源的首选模型，可为中药产地溯源提供跨学科方案，未来可整合多模态数据及深度学习技术提升模型性能。
中药
/
产地溯源
/
机器学习
/
支持向量机
/
人工神经网络
/
常青藤优化算法
/
中红外光谱技术
Objective To address the issues of strong subjectivity and low efficiency in traditional Chinese medicine origin tracing, we explore the method of Chinese medicine origin tracing based on mid-infrared spectroscopy (MIRS) combined with machine learning algorithm modeling, and provide new technical support for traditional Chinese medicine origin tracing. Methods Based on the mid-infrared spectral dataset, this study performed outlier handling, missing value imputation, first-order difference, high-variance feature selection, distance calculation and clustering analysis, and locally linear embedding (LLE) for dimensionality reduction. Subsequently, we constructed six machine learning models including Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. We then employed the Ivy Algorithm (IVYA) to optimize model parameters. A multidimensional evaluation framework was established, incorporating the area under the receiver operating characteristic (ROC) curve (AUC) alongside accuracy, recall, precision, and F1 score (F1). This framework was used to identify the optimal model for Chinese herbal medicine origin tracing. Results SVM (macro-average AUC = 0.998, F1 = 0.949, accuracy = 0.949, recall = 0.949, precision = 0.956) and ANN (macro-average AUC = 0.999, F1 = 0.940, accuracy = 0.939, recall = 0.939, precision = 0.944) demonstrated optimal performance in identifying the origins of Chinese herbal medicines. They exhibited high consistency in predicting the origins of unknown samples, with core evaluation metrics significantly outperforming RF, LightGBM, KNN, and XGBoost. The SVM model can accurately capture the functional group vibration differences in the medium chemical components of medicinal materials from different origins. ANN models simulate the multifactorial nonlinear coupling effects formed by Chinese herbal medicine origins. Both models not only achieve efficient differentiation among 11 origins but also exhibit complementary strengths: SVM’s globally optimal classification hyperplane excels at processing origins with markedly distinct spectral features, while ANN’s distributed representation capability better suits complex scenarios with high feature overlap. The MIRS method, integrating SVM and ANN, offers rapid, non-destructive, and high-throughput advantages for Chinese herbal medicine traceability. This provides an objective tool for large-scale origin tracing of authentic medicinal materials and delivers a practical technical pathway for the modernization of the Chinese herbal medicine industry. Conclusion MIRS combined with SVM, RF, LightGBM, KNN, XGBoost, and ANN demonstrates effectiveness for Chinese herbal medicine origin tracing. Among these, SVM and ANN emerge as preferred models for this application, offering an interdisciplinary solution. Future integration of multimodal data and deep learning techniques holds promise for enhancing model performance.
traditional Chinese medicine
/
origin traceability
/
machine learning
/
support vector machine
/
artificial neural network
/
Ivy algorithm
/
mid-infrared spectroscopic technique
沈晔, 王继森, 向晴, 王名亮, 汪蕾, 邓垚, 陈卓, 廖婉, 林薇.
基于机器学习的中药产地溯源研究.
中草药,
2026
, 57
(5)
: 1798
-1813
.
DOI: 10.7501/j.issn.0253-2670.2026.05.018
SHEN Ye, WANG Jisen, XIANG Qing, WANG Mingliang, WANG Lei, DENG Yao, CHEN Zhuo, LIAO Wan, LIN Wei.
Traceability study of traditional Chinese medicine origins based machine learning[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(5)
: 1798
-1813
.
DOI: 10.7501/j.issn.0253-2670.2026.05.018
四川省自然科学基金 (2024NSFSC2109); 国家自然科学基金面上项目 (82574868); 成都市科学技术局技术创新研发项目 (2024-YF05-01999-SN); 教育部代谢性心血管疾病医药基础研究创新中心开放课题重点项目 (xnykdxcxzx-2025-05); 四川省中医药管理局面上项目(25MSZX079）
参考文献
引证文献
赵露颖, 施梦瑶, 张巧艳, 等. 道地药材品质特征及形成机制研究进展[J]. 中草药, 2022, 53(21): 6931-6947.et al . Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese rice wine) by near infrared spectroscopy combined with chemometrics[J]. J Food Compos Anal , 2022, 111: 104563.Rosa roxburghii Tratt) with chemometrics variable strategy[J]. J Food Compos Anal , 2022, 105: 104225.J Optim Theory Appl , 2025, 205(1): 1.Science , 2000, 290(5500): 2323-2326.Nat Resour Res , 2023, 32(6): 2417-2438.Ieee T Knowl Data En , 2021, 34(10): 4663-4675.et al . Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm[J]. Knowl Based Syst , 2024, 295: 111850.
2026年第57卷第5期
PDF下载
28
9
引用本文
BibTeX
文章信息
doi: 10.7501/j.issn.0253-2670.2026.05.018
接收时间：2025-10-23
首发时间：2026-09-09
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.05.018
引用本文
BibTeX
2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
28
9
关闭全屏
北京市海淀区学院南路86号
010-62199257（仅加盟咨询）
qkjq@cast.org.cn
