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.

Key words: machine learning; food flavor; prediction; flavor detection; molecular structure