Deep Learning Research on Quantitative Evaluation Model of Tea Taste Based on NAR Neural Network

The demand for tea from consumers exhibits a diversified and personalized trend. Initiating from the scientific analysis of tea taste, this paper addresses issues such as the incomplete cognition standard of tea market taste, varying evaluation criteria for tea, and the development of a young tea consumption group, utilizing the Nonlinear Auto-Regressive (NAR) model, a quantitative evaluation of tea taste is conducted. In this study, raw and ripe tea samples from Pu-erh tea were used as training data, and the NAR was employed for deep learning, error analysis, and comparison. The aim was to predict the taste resulting from different content ratios of chemical components in tea, further verifying the feasibility and scientific accuracy of the quantitative evaluation of tea taste based on the NAR. This study not only broadens the research field of NAR neural network, but also further enables tea enterprises to better provide consumers with diversified tea taste, and provides an important reference for tea taste evaluation.