X U Ningning, Yan Ganming, X U Fengjie, Deng Linfeng, Qiao Xinjiang, L U Changzheng, Cheng Shaomin
{"title":"根据近红外光谱与化学计量学相结合的方法,确定莫瑶()的地理产地和加工技术。","authors":"X U Ningning, Yan Ganming, X U Fengjie, Deng Linfeng, Qiao Xinjiang, L U Changzheng, Cheng Shaomin","doi":"10.19852/j.cnki.jtcm.20240308.002","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the quality of Moyao (<i>Myrrh</i>) in the identification of the geographical origin and processing of the products.</p><p><strong>Methods: </strong>Raw Moyao (<i>Myrrh</i>) and two kinds of Moyao (<i>Myrrh</i>) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (<i>Myrrh</i>) samples, and their discriminative performance was evaluated.</p><p><strong>Results: </strong>Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (<i>Myrrh</i>) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.</p><p><strong>Conclusions: </strong>NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (<i>Myrrh</i>) and can also provide a reference for evaluations of its quality and the clinical use.</p>","PeriodicalId":94119,"journal":{"name":"Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan","volume":"44 3","pages":"505-514"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11077148/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identifying the geographical origin and processing technology of Moyao () on the basis of near-infrared spectroscopy combined with chemometrics.\",\"authors\":\"X U Ningning, Yan Ganming, X U Fengjie, Deng Linfeng, Qiao Xinjiang, L U Changzheng, Cheng Shaomin\",\"doi\":\"10.19852/j.cnki.jtcm.20240308.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To evaluate the quality of Moyao (<i>Myrrh</i>) in the identification of the geographical origin and processing of the products.</p><p><strong>Methods: </strong>Raw Moyao (<i>Myrrh</i>) and two kinds of Moyao (<i>Myrrh</i>) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (<i>Myrrh</i>) samples, and their discriminative performance was evaluated.</p><p><strong>Results: </strong>Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. 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The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.</p><p><strong>Conclusions: </strong>NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (<i>Myrrh</i>) and can also provide a reference for evaluations of its quality and the clinical use.</p>\",\"PeriodicalId\":94119,\"journal\":{\"name\":\"Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan\",\"volume\":\"44 3\",\"pages\":\"505-514\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11077148/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.19852/j.cnki.jtcm.20240308.002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.19852/j.cnki.jtcm.20240308.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的:评估没药的质量:在确定产品的地理来源和加工过程中评估没药的质量:使用近红外光谱(NIR)和化学计量学技术对来自三个国家的未加工的没药和用醋加工的两种没药进行鉴定。主成分分析(PCA)用于降低数据的维度,并使不同类别样品的聚类可视化。采用经典的化学计量学算法(PLS-DA)和两种机器学习算法(K-近邻(KNN)和支持向量机)对没药样品的近红外光谱进行了分类分析,并对其鉴别性能进行了评估:根据各模型的准确度、精确度、召回率和 F1 值,结果表明经典化学计量学算法和机器学习算法都取得了积极的成果。在所有化学计量分析中,经标准正态变异或多元散射校正结合 KNN 预处理的没药近红外光谱的地理产地鉴别准确率最高,KNN 方法在一阶导数预处理后建立的加工工艺鉴别准确率最好。地理产地判别和加工技术判别的最佳准确率分别为 0.9853 和 0.9706:近红外光谱与化学计量学技术相结合可作为追踪没药产地和加工工艺的重要工具,也可为没药质量评价和临床使用提供参考。
Identifying the geographical origin and processing technology of Moyao () on the basis of near-infrared spectroscopy combined with chemometrics.
Objective: To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products.
Methods: Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated.
Results: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.
Conclusions: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.