A review of deep learning models for food flavor data analysis

Deep learning, a core branch of artificial intelligence, has shown great potential in food flavor analysis, prediction and optimization with its powerful data processing and pattern recognition capabilities. This article reviews deep learning applications in food flavor, discussing various deep learning algorithms and models including artificial neural network, convolutional neural network, recurrent neural network, AutoEncoder, graph neural network, and generative adversarial network. Besides, the latest progress and development trends of deep learning are discussed in this field. Compared with traditional flavor analysis methods, deep learning methods have obvious advantages and important application prospects in the field of food flavor. With the continuous advancement of technology in the future, it is expected that more deep learning applications will appear in the food industry.