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Molecular Basis of Food Classification in Traditional Chinese Medicine

  • Xiaosong Han
  • Haiyan Zhao
  • Hao Xu
  • Yun Yang
  • Yanchun Liang
  • Dong XuEmail author
Chapter
  • 25 Downloads
Part of the Emerging Topics in Statistics and Biostatistics book series (ETSB)

Abstract

Traditional Chinese Medicine (TCM) started considering the medicinal and health effects of food thousands of years ago. TCM labels are placed on foods based on cold, neutral, and hot properties similar to Chinese herbal medicine. However, it is unclear whether such a classification has any molecular or biochemical basis, and what the relationship is between this TCM classification and the nutrient composition of food. To answer these questions, we collected a large dataset, in which each type of food has both TCM labels and molecular composition records for statistical analyses and machine-learning predictions. We applied machine-learning methods by using food molecular composition to predict the hot, neutral or cold label of food, and achieved more than 80% accuracy, which clearly indicated that TCM labels have a significant molecular basis. We also applied ANOVA to analyze the main factors contributing to the TCM labels. The ANOVA analysis shows that some molecular/biochemical compositions and categories, such as Energy, Fat, Protein, Water and Selenium (Se), have the strongest correlations with the TCM labels of food. To the best of our knowledge, this study represents the first effort to quantitatively explore the relationship between TCM labels and the molecular composition of food.

Keywords

Traditional Chinese medicine Zheng Food composition Health effect of food Machine learning 

Notes

Acknowledgements

This work has been partially supported by the National Natural Science Foundation of China (61503150, 61972174), the Jilin Scientific and Technological Development Plan (20160520012JH, 20170204057GX), the Guangdong Key-Project for Applied Fundamental Research (Grant 2018KZDXM076), the Guangdong Premier Key-Discipline Enhancement Scheme (Grant 2016GDYSZDXK036) and the Paul K. and Dianne Shumaker Endowed Fund at University of Missouri.

Supplementary material

478473_1_En_10_MOESM1_ESM.xlsx (3.4 mb)
Data 1 (XLSX 3521 kb)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiaosong Han
    • 1
  • Haiyan Zhao
    • 2
  • Hao Xu
    • 1
  • Yun Yang
    • 3
  • Yanchun Liang
    • 4
    • 5
  • Dong Xu
    • 6
    • 7
    Email author
  1. 1.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of EducationCollege of Computer Science and Technology, Jilin UniversityChangchunChina
  2. 2.Centre for Artificial IntelligenceFEIT, University of Technology Sydney (UTS)BroadwayAustralia
  3. 3.PhilocafeSan JoseUSA
  4. 4.College of Computer Science and Technology, Jilin UniversityChangchunChina
  5. 5.Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Department of Computer Science and TechnologyZhuhai College of Jilin UniversityZhuhaiChina
  6. 6.Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaUSA
  7. 7.Christopher S. Bond Life Sciences CenterUniversity of MissouriColumbiaUSA

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