Ke Liu, Chun-Lu Liu, Long Wang, Ping Li, Yan Jiang, Hui-Jun Li
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Finally, separately using chemical indicators as independent variables and quality grades as dependent variables, the logistic regression model and a multi-index weighted quality comprehensive evaluation index (QCEI) were constructed to predict the quality grade of CR.</p><p><strong>Results: </strong>The results indicated that 27 batches of CR samples could be divided into three grades of I, II, and III. The gray correlation degrees and Pearson correlation coefficients between water-soluble extractives, polysaccharide, amino acid, and immunological activity all exceeded 0.8 and 0.4 (p < 0.05), respectively. Additionally, both the logistic regression model and QCEI could effectively predict the quality grade of CR, with the logistic regression model showing superior performance.</p><p><strong>Conclusion: </strong>This study is the first to establish a chemistry-biology integrated strategy for evaluating the quality grade of CR, providing a novel insight into the assessment of TCM quality grade.</p>","PeriodicalId":20095,"journal":{"name":"Phytochemical Analysis","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Grading Evaluation of Changii Radix Based on a Biology-Related Chemical Indicator Prediction Model.\",\"authors\":\"Ke Liu, Chun-Lu Liu, Long Wang, Ping Li, Yan Jiang, Hui-Jun Li\",\"doi\":\"10.1002/pca.3524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The quality of traditional Chinese medicine (TCM) is a prerequisite for clinical efficacy. However, the existing quality evaluation methods are not strongly correlated with efficacy, and they are unable to adequately reflect the quality grade of Changii Radix (CR).</p><p><strong>Objectives: </strong>In this study, a biology-related chemical indicator quality grading prediction model was developed to predict the quality grade of CR.</p><p><strong>Materials and methods: </strong>Firstly, the quality grade of CR was pre-classified based on immunological activity. Subsequently, one-way analysis of variance, gray correlation analysis, and Pearson correlation analysis were employed to identify the chemical indicators associated with immunological activity. Finally, separately using chemical indicators as independent variables and quality grades as dependent variables, the logistic regression model and a multi-index weighted quality comprehensive evaluation index (QCEI) were constructed to predict the quality grade of CR.</p><p><strong>Results: </strong>The results indicated that 27 batches of CR samples could be divided into three grades of I, II, and III. The gray correlation degrees and Pearson correlation coefficients between water-soluble extractives, polysaccharide, amino acid, and immunological activity all exceeded 0.8 and 0.4 (p < 0.05), respectively. Additionally, both the logistic regression model and QCEI could effectively predict the quality grade of CR, with the logistic regression model showing superior performance.</p><p><strong>Conclusion: </strong>This study is the first to establish a chemistry-biology integrated strategy for evaluating the quality grade of CR, providing a novel insight into the assessment of TCM quality grade.</p>\",\"PeriodicalId\":20095,\"journal\":{\"name\":\"Phytochemical Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Phytochemical Analysis\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/pca.3524\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phytochemical Analysis","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/pca.3524","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Quality Grading Evaluation of Changii Radix Based on a Biology-Related Chemical Indicator Prediction Model.
Introduction: The quality of traditional Chinese medicine (TCM) is a prerequisite for clinical efficacy. However, the existing quality evaluation methods are not strongly correlated with efficacy, and they are unable to adequately reflect the quality grade of Changii Radix (CR).
Objectives: In this study, a biology-related chemical indicator quality grading prediction model was developed to predict the quality grade of CR.
Materials and methods: Firstly, the quality grade of CR was pre-classified based on immunological activity. Subsequently, one-way analysis of variance, gray correlation analysis, and Pearson correlation analysis were employed to identify the chemical indicators associated with immunological activity. Finally, separately using chemical indicators as independent variables and quality grades as dependent variables, the logistic regression model and a multi-index weighted quality comprehensive evaluation index (QCEI) were constructed to predict the quality grade of CR.
Results: The results indicated that 27 batches of CR samples could be divided into three grades of I, II, and III. The gray correlation degrees and Pearson correlation coefficients between water-soluble extractives, polysaccharide, amino acid, and immunological activity all exceeded 0.8 and 0.4 (p < 0.05), respectively. Additionally, both the logistic regression model and QCEI could effectively predict the quality grade of CR, with the logistic regression model showing superior performance.
Conclusion: This study is the first to establish a chemistry-biology integrated strategy for evaluating the quality grade of CR, providing a novel insight into the assessment of TCM quality grade.
期刊介绍：
Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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