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Publishing Language: Chinese | Open Access

Research Progress on the Application of Machine Learning in Predicting Food Flavor

Weitong CAI , Tao FENG (), Shiqing SONG, Lingyun YAO, Min SUN, Huatian WANG, Chuang YU, Qian LIU
School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
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Abstract

Food flavor plays an important role in people’s life. Traditional methods for flavor analysis and detection have limited ability to predict food flavor. In recent years, many researchers have used machine learning models to effectively process food flavor information and establish classification and prediction models, making flavor prediction more accurate and efficient. The principles of traditional and novel machine learning methods, such as support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and neural network, as well as recent progress on their combined application with flavor analysis instruments and molecular structure analysis for food flavor prediction are reviewed, aiming to provide new ideas for the application of machine learning models in food flavor analysis and prediction. It is found that machine learning models can be used to predict the impacts of different substance components on food flavor, identify the flavor characteristics of foods from different regions. The combination of multiple machine learning models can improve the accuracy and reliability of prediction, and promote in-depth research and development of food flavor.

Keywords

machine learningfood flavorpredictionflavor detectionmolecular structure
CLC number: TP181 Document code: A Article ID: 1002-6630(2024)12-0011-11

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Food Science
Volume 45 Issue 12,
June 2024
Pages 11-21
DOI: 10.7506/spkx1002-6630-20240103-032
Cite this article:
CAI W, FENG T, SONG S, et al. Research Progress on the Application of Machine Learning in Predicting Food Flavor. Food Science, 2024, 45(12): 11-21. https://doi.org/10.7506/spkx1002-6630-20240103-032
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