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Highlights • Four biomarkers were identified by UPLC-Q/Orbitrap HRMS and PLS-DA analysis. • Classification models were built with machine learning and four biomarkers. • The optimal decision tree model can effectively identify adulterated moxa floss. Abstract Detecting adulteration in closely related herbal medicines is a challenge for quality control and market standardization. This study aimed to identify whether Artemisia mongolica floss was adulterated into moxa floss.
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Home > NSS > Vol. 72 Z.M. Elsherif, Ahmed; A. A. Salama; O. M. Khaled; Mostafa Herajy; E. I. Elsedimy; Huda E. Khalid; and Ahmed K. Essa. "Unveiling Big Data Insights: A Neutrosophic Classification Approach for Enhanced Prediction with Machine Learning." Neutrosophic Sets and Systems 72, 1 (2024). https://digitalrepository.unm.edu/nss_journal/vol72/iss1/7 COinS
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