Estimation of tea quality grade using statistical identification of key variables

The uncertainty in tea classification affects the market presence of tea and damages the related economic interests. The quick and accurate identification of tea quality grades has a significant impact on the profitability of the tea market as the prices of different grades of tea quality vary greatly. In this research, 19 chemical substances that affect the quality of Huangshan Maofeng tea were detected using stoichiometry. A model-based scheme comprising the use of the stepwise regression method (SRM) was established to estimate tea quality grades. The rationale of the filtering of sparse variables in SRM is to put the elements through the preset F-statistic test to determine the selection of variables. The results of the SRM are then compared with those of elastic net and the partial least squares discriminant analysis (PLS-DA) to demonstrate the effectiveness of the proposed scheme. Furthermore, in order to verify the stability of the model, Monte Carlo experiments were conducted on the constructed models. The predictive accuracy of the SRM, PLS-DA, and elastic net algorithms were 68.75%, 75.86%, and 71.88%, respectively. The radar diagram, which is drawn according to the sparse coefficient vector obtained using SRM, illustrates that the proposed scheme can overcome the correlation between all the detection variables. It is concluded that SRM achieves the highest prediction accuracy with the least number of features, thereby simplifying the process of chemical detection, and provides a new effective scheme for batch tea-quality-grade estimation.