Application of Machine Learning Algorithms in Predicting Flavor and Quality of Jasmine Tea

As a subfield of artificial intelligence, machine learning has gained widespread application due to its exceptional ability to learn models and summarize experiences from large datasets. To address the issues of time consumption, labor intensity, poor objectivity, and low accuracy in the flavor quality prediction of jasmine tea, machine learning algorithms were introduced. As a branch of artificial intelligence and computer science, machine learning utilizes data and algorithms to simulate or replicate human learning behavior, exhibiting strong capabilities in handling irrelevant information, extracting feature variables, and building calibration models. It has found broad applications in the food industry. In recent years, there have been numerous reports on the application of machine learning in tea processing, but there are relatively few review articles specifically focused on the application of machine learning techniques in predicting the flavor quality of jasmine tea. This paper reviewed the principles of commonly used machine learning models and their application in predicting the flavor quality of jasmine tea. It introduced the application of current machine learning models in the physical testing, chemical indicators, and microbial and pest detection aspects of jasmine tea flavor quality prediction, with the aim of providing a reference for the application of machine learning in the development of the jasmine tea industry.