Machine Learning Based Research Of Correlation Between Nutrient Composition And Cold And Hot Properties Of Food

The reform of medical mode promotes the change of people's concept of health.People begin to pay attention to the health care of body,which agrees with the theory of “Preventive treatment of disease”.Among them,food therapy is the most popular.Traditional Chinese Medicine(TCM)started to consider medicinal and health effects of food thousands of years ago.TCM labels food by 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 is the relationship between this classification and nutrient composition of food.In the existing study,most scholars still distinguish the hot and cold properties of food through food color,taste,etc.,which may result in errors.Some scholars use some mathematical statistics methods,such as multiple discriminant methods,single factor analysis,and principal component analysis,to analyze the data and find the correlation between some nutrient composition and cold and hot properties of food.Although the experimental results can illustrate some problems,they are not representative and reliable because their dataset is too small and methods they apply cannot mine the hidden information of the data.To answer these questions,we collected two datasets,one was collected manually,which contains 25 features,and another was collected by the crawler system containing 99 features.In both the two datasets,each food entry was labeled by TCM experts.Prior to data analysis,the data was cleaned and the data was complemented with the matrix completion algorithm.We applied machine-learning methods,respectively SVM,XGboost,Random Forest and Deep Learning,for using food molecular composition to predict the hot,neutral or cold label of food.Genetic algorithm was used to optimize SVM algorithm and achieved about 85% accuracy.The result clearly indicates that the TCM labels have a significant molecular basis.We compared the result of genetic algorithm with that of ANOVA.And the result shows that many features are both selected by ANOVA and genetic algorithm,such as phosphorus(P),Protein,Fat,which explains the importance of these elements on the classification of cold and hot properties of food and indicates the reliability of the results.We have found when food contains more energy,the food tends to be hot.When food contains much water,the food tends to be cold.The model obtained in the research can help TCM scholars to judge the hot and cold properties of food and help them improve the research efficiency.The quantitative research on the nutrient composition and hot and cold properties of food raises people's awareness of hot and cold properties of TCM,and promotes the development and perfection of the scientific theory of TCM in this field.