Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg.

Journal: Food chemistry
PMID: 39721433

Abstract

Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is an edible plant with considerable medicinal properties, the quality of which varies depending on its origin. Therefore economically motivated adulteration has emerged. So there is an urgent need to develop effective techniques for determining the origin of T. hemsleyanum. This study combined stable isotopes and multiple elements with machine learning chemometric models (SVM, RF and FNN models) for T. hemsleyanum origin traceability. The results showed that this approach successfully distinguished T. hemsleyanum form different regions with the SVM, RF and FNN models all displaying a 100 % prediction accuracy and the FNN model exhibiting superior performance. This study provides a technical and theoretical basis for research on the origin traceability of T. hemsleyanum.

Authors

  • Lu Bai
    College of Chemical Engineering, Department of Pharmaceutical Engineering, Northwest University, Taibai North Road 229, Xi'an 710069, Shaanxi, China.
  • Zixuan Zhang
    Department of Electrical & Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.
  • Yalan Li
    Institute of Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Shanshan Zhao
    Department of Ultrasound, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, China.
  • Xiaoting Yang
    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China.
  • Chengqun Chen
    Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China; Institute of Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Shilin Zhao
    Departments of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Ping Zhang
    Department of Computer Science and Engineering, The Ohio State University, USA.
  • Xin Peng
  • Yan Zhao
    Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
  • Kehong Liang
    Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China. Electronic address: liangkehong@caas.cn.

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