# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 4 Results and Discussion
### Comparison with Conventional Machine-Learning Approaches and Two Deep Networks
Unlike RF, which requires meticulous tuning of hyperparameters for each prediction target, our algorithm exhibits robustness and adaptability, consistently showing competitive performance across a range of prediction tasks. In comparison to the two deep networks, GCNN-LSTM-AT demonstrates stable and satisfactory performance in all five prediction tasks. DeepSpectra2D and CNN-AT have shown good performance in certain tasks, but they achieve unsatisfactory results at times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Method** & **RMSECV** & \(R^{2}\) & **MAE** \\ \hline \multirow{8}{*}{SSC (\({}^{\circ}\)Brix)} & GCNN-LSTM-AT & 0.143019 & 0.988546 & 0.119733 \\  & CNN-AT & 0.441304 & 0.930002 & 0.403427 \\  & MWPLS & 0.630621 & 0.857062 & 0.564087 \\  & DeepSpectra2D & 0.633972 & 0.855539 & 0.523082 \\  & SVR & 0.635088 & 0.855030 & 0.550726 \\  & RF & 0.637721 & 0.853826 & 0.545897 \\ \hline \multirow{8}{*}{TA (\%)} & GCNN-LSTM-AT & 0.086817 & 0.807487 & 0.072121 \\  & RF & 0.106826 & 0.708527 & 0.095840 \\  & CNN-AT & 0.11266 & 0.67582 & 0.080944 \\  & DeepSpectra2D & 0.116552 & 0.653038 & 0.097312 \\  & SVR & 0.128481 & 0.578378 & 0.115217 \\  & MWPLS & 0.132757 & 0.549845 & 0.110507 \\ \hline \multirow{8}{*}{A/S} & GCNN-LSTM-AT & 1.998381 & 0.901365 & 1.600419 \\  & CNN-AT & 3.621092 & 0.669026 & 2.897637 \\  & RF & 3.66037 & 0.661807 & 3.025368 \\  & SVR & 3.787508 & 0.637906 & 2.619833 \\  & DeepSpectra2D & 4.172031 & 0.560651 & 3.241931 \\  & MWPLS & 4.234571 & 0.547381 & 3.200423 \\ \hline \multirow{8}{*}{Firmness (kg)} & GCNN-LSTM-AT & 0.029408 & 0.947206 & 0.020084 \\  & DeepSpectra2D & 0.038597 & 0.820301 & 0.027363 \\ \cline{1-1}  & MWPLS & 0.043178 & 0.775109 & 0.035490 \\ \cline{1-1}  & CNN-AT & 0.048934 & 0.711151 & 0.038188 \\ \cline{1-1}  & RF & 0.064057 & 0.505032 & 0.052835 \\ \cline{1-1}  & SVR & 0.090967 & 0.19752 & 0.073538 \\ \hline \multirow{8}{*}{VC (mg/100 g)} & DeepSpectra2D & 28.941088 & 0.746859 & 27.861427 \\ \cline{1-1}  & GCNN-LSTM-AT & 29.410427 & 0.738583 & 23.131868 \\ \cline{1-1}  & MWPLS & 35.492973 & 0.619271 & 25.987210 \\ \cline{1-1}  & CNN-AT & 36.66847 & 0.593635 & 30.667585 \\ \cline{1-1}  & RF & 43.01874 & 0.440698 & 31.808692 \\ \cline{1-1}  & SVR & 45.583222 & 0.372027 & 33.289951 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of six models on the five qualities of tangerines. The quality parameters are soluble solid content (SSC), total acidity (TA), acid–sugar ratio (A/S), firmness, and Vitamin C (VC). It indicates that GCNN-LSTM-AT can adapt well to various task characteristics and has better generalization ability. GCNN-LSTM-AT combines the advantage of GCN, diverse CNN kernels, Bi-LSTM, and the attention mechanism. GCN propagates node features and aggregates graph information, CNN is used to extract local features in the spatial dimension, while Bi-LSTM models the spectral sequential characteristics. This hierarchical representation learning enables the model to capture various relationships and patterns, enhancing the model's expressive ability. Figure 9 depicts the predictive performance of the proposed GCNN-LSTM-AT algorithm for five quality parameters on all tangerines. The x-axis of each dot in the figures is the actual measured value, and the y-axis is the predicted value of the model. The dispersion of each point is demonstrated by the color bar, where dark blue represents a small dispersion and yellow represents a larger dispersion. The red line is the fitting line between the predicted values and the true values, the slope and intercept of which are labeled in the figures. The blue line is the reference line, which is \(y=x\). From the five scatter plots, it can be seen that the proposed algorithm can predict the SSC, TA, A/S, firmness, and VC of tangerines with relatively high accuracy, indicating good applicability. Among them, the prediction performance for SSC is the best, with the smallest prediction error and the highest degree of fit.