Abstract 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.

中文翻译：

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使用关键变量的统计识别估计茶叶质量等级

摘要 茶叶分类的不确定性影响了茶叶的市场占有率，损害了相关经济利益。由于不同等级的茶品质价格差异很大，因此快速准确地识别茶叶品质等级对茶叶市场的盈利能力有重大影响。本研究采用化学计量法检测了19种影响黄山毛峰茶品质的化学物质。建立了一个基于模型的方案，包括使用逐步回归方法 (SRM) 来估计茶叶质量等级。SRM 中稀疏变量过滤的基本原理是将元素通过预设的 F 统计量检验来确定变量的选择。然后将 SRM 的结果与弹性网和偏最小二乘判别分析 (PLS-DA) 的结果进行比较，以证明所提出方案的有效性。此外，为了验证模型的稳定性，对构建的模型进行了蒙特卡罗实验。SRM、PLS-DA 和弹性网络算法的预测准确率分别为 68.75%、75.86% 和 71.88%。根据使用 SRM 获得的稀疏系数向量绘制的雷达图表明，该方案可以克服所有检测变量之间的相关性。得出的结论是，SRM 以最少的特征数量实现了最高的预测精度，从而简化了化学检测的过程，