As an alcoholic beverage, wine has remained prevalent for thousands of years, and the quality assessment of wines has been significant in wine production and trade. Scholars have proposed various deep learning and machine learning algorithms for wine quality prediction, such as Support vector machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Deep neural network (DNN), and Logistic regression (LR). However, these methods ignore the inner relationship between the physical and chemical properties of the wine, for example, the correlations between pH values, fixed acidity, citric acid, and so on. To fill the gap, this paper conducts the Pearson correlation analysis, PCA analysis, and Shapiro-Wilk test on those properties and incorporates 1D-CNN architecture to capture the correlations among neighboring features. In addition, it implemented dropout and batch normalization techniques to improve the robustness of the proposed model. Massive experiments have shown that our method can outperform baseline approaches in wine quality prediction. Moreover, ablation experiments also demonstrate the effectiveness of incorporating the 1-D CNN module, Dropout, and normalization techniques.

翻译：作为酒精饮料,葡萄酒在数千年中一直很普遍,对葡萄酒的生产和交易质量评估意义重大。学者们提出了各种关于酒质量预测的深层次学习和机器学习算法,如支持矢量机(SVM)、随机森林(RF)、K近邻(KNN)、深神经网络(DNN)和物流回归(LR)等。然而,这些方法忽视了葡萄酒物理和化学特性之间的内在关系,例如,PH值、固定酸性、柠檬酸等之间的相互关系。为填补空白,本文还进行了皮尔森相关性分析、CPA分析和Shapiro-Wilk测试,并纳入了1D-CNN结构,以捕捉邻近特征之间的相互关系。此外,它还实施了辍学和批次正常化技术,以提高拟议模型的稳健性。大规模实验表明,我们的方法在酒质量预测中可以超越基线方法。此外,消化实验还展示了纳入1DCNN模块、脱产和正常化技术的有效性。