# Evaluation of Table Grape Flavor Based on Deep Neural Networks
## 5 Discussion  
In this paper, we propose a deep-neural-network-based grape flavor evaluation model and report the results of comparative experiments to illustrate our model's accuracy and reliability in comparison with other models. Furthermore, we analyzed the contribution of each module in our model to its prediction performance. The embedding layer achieves a more comprehensive representation of the input data in the form of feature vectors, the convolutional layer extracts combined features through convolution kernels of different step sizes to characterize the interactions between flavor-influencing metrics, and the model integrates the attention module to increase the prediction accuracy. The complete model outperforms all other compared models in terms of all metrics. We collected measurements of five physicochemical indicators: the total soluble solids, titratable acid, solid/acid ratio, total phenols, and volatile substances of grapes. Since these indicators reflect grapes' chemical composition and flavor profile, they are used as input features of the model to predict the sensory scores of grape flavor. These five physicochemical indicators significantly impact the flavor of fresh grapes: soluble solids affect the sweetness and taste of grapes, while titratable acid affects the acidity of  
Figure 4: Effects of different dimensions on model prediction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **RMSE** & **R2** & **AIC** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Without embedding layer & 3.526 & 0.923 & 512.444 & 0.906 & 0.899 & 0.903 & 0.901 \\ Without convolutional layer & 4.291 & 0.869 & 553.997 & 0.891 & 0.845 & 0.898 & 0.869 \\ Without attention layer & 2.964 & 0.948 & 392.081 & 0.924 & 0.901 & 0.930 & 0.915 \\
**The complete model** & **2.657** & **0.987** & **206.472** & **0.948** & **0.920** & **0.936** & **0.928** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effects of different layers on model prediction. grapes. The solid/acid ratio reflects the balance of acidity and sugar in grape juices. Total phenols provide grapes with complex flavors and textures, while volatiles are the source of aromas that provide grapes with a unique smell and taste. Since there is a complex, interactive relationship between these five indicators, we need to consider their collective effects to achieve a high accuracy in flavor assessment. Considering the advantages of machine learning in nonlinear, multivariate fitting, we designed our flavor evaluation model based on a deep neural network with different levels of neurons to represent the complex interplay between different indicators. As a result of the experimental validation analysis, we obtained a range of indicators for predicting the flavor of grapes belonging to the first class, as shown in Table 9.  
To ensure the quality of fresh grapes, certain sugar and acidity requirements must be satisfied. These requirements can vary according to different varieties and different origins. For example, the first grade of Thompson Seedless grapes in Xinjiang, China, requires that the soluble solids be above 20% and the total acid content be less than 0.80% [23]; the first grade of Summer Black grapes in Henan, China, should have a soluble solids content above 18% and a total acid content below 0.5% [24]. These requirements are generally in line with the predicted ranges in this work. In addition, the taste of fresh grapes primarily depends on the balance between sugar and acid contents, i.e., the solid/acid ratio [25]. A study by the Grape Research Group at Beijing Agricultural University, China, shows that an appropriate ratio of soluble solids to acid contents for Xuanhua Milk Grapes is about 35:1 to 40:1 [26]. In Xinjiang, China, the grading standard for Red Globe grapes requires a solid/acid ratio of not less than 39.1% for extra-grade fruits [27]. Total phenols mainly determine grapes' astringency, bitterness, color, and antioxidant properties [28].