  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.
