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  • Open Access

    ARTICLE

    Identification of Secondary Metabolites of Lycium ruthenicum Murray by UPLC-QTOF/MS and Network Pharmacology of Its Anti-Inflammatory Properties

    Chen Chen#,*, Chunli Li#, Tengfei Li, Qianhong Li, Luyao Li, Fengqin Liu

    Phyton-International Journal of Experimental Botany, Vol.94, No.3, pp. 793-807, 2025, DOI:10.32604/phyton.2025.063549 - 31 March 2025

    Abstract Lycium ruthenicum Murray, a plant widely cultivated in northwestern China, is integral to traditional Chinese medicine, with applications in treating menstrual disorders, cardiovascular diseases, and menopausal symptoms. Despite its recognized medicinal value and use as a functional food, comprehensive knowledge of its metabolites and their pharmacological effects remains limited. This study presents an innovative approach using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC–QTOF/MS) to conduct a detailed analysis of both wild and cultivated L. ruthenicum samples. A total of 62 peaks were detected in the total ion current profile, with 59 metabolites identified based… More >

  • Open Access

    ARTICLE

    Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

    Simon Crase1,2, Benjamin Hall2, Suresh N. Thennadil3,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1945-1965, 2021, DOI:10.32604/cmc.2021.018517 - 21 July 2021

    Abstract Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the More >

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