# Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder
## 4 Results and Discussion  
### Performance Analysis of SAE-PLSR Model  
The input of SAE-PLSR was a hyperspectral characteristic curve and the corresponding SSC value of a 119-dimensional green plum. The number of neurons in the SAE was set to [90,55] and the sparsity parameter \(\rho\) was set to 0.01. The SAE was used to train the raw data beforehand. The hidden layer outputs of the two SAEs are shown in Figure 6.  
The correlation coefficient (\(R\)) and root mean squared error (RMSE) were used to evaluate the performance of the models. \(R_{C}\) (correlation coefficient in calibration set) and RMSEC (root mean squared error in calibration set) were used to evaluate the performance of the calibration set, whereas \(R_{P}\) (correlation coefficient in prediction set) and RMSEP (root mean squared error in prediction set) were used to evaluate the performance of the prediction set. After training, the prediction performance of the SAE-PLSR model on green plum was quantified, the \(R_{C}\), RMSEC, \(R_{P}\), and RMSEP values were 0.957, 0.542, 0.938, and 0.654, respectively. In order to evaluate the performance of the present model, the SAE-PLSR model was compared with three traditional methods, namely, BP neural network [31], SVR machine [32], and PLSR [33] to reflect prediction performance. For these methods, [PERSON] et al. [34] have used PLSR model to predict the SSC of plum with the short wave near infrared hyperspectral image and obtained good prediction results. Figure 7 shows the prediction results of different models and Table 2 shows the results of the comparison between the SAE-PLSR model and other traditional models. The results of the comparison showed that the PLSR model had good prediction performance on the calibration set. However, the prediction performance on the prediction set was not good enough. Compared with the calibration set, the prediction set had a 82.6% increase in RMSEP and 7.5% decrease in \(R_{P}\), indicating that the robustness of the PLSR model was not good or that overfitting occurred. The SVR and the BP models were inferior to the PLSR model in prediction performance. The \(R_{P}\) for the prediction sets of the SVR and the BP models decreased by 1.5% and 2.5%, respectively, and the RMSE for the prediction sets of the SVR and the BP models increased by 1.4% and 9.2%, respectively. The prediction performance of the SAE-PLSR model greatly improved compared with that of the PLSR model. The calibration set had a 1.1% decrease in \(R_{C}\) and a 22.6% increase in RMSEC. The main reason for the analysis results was that the input of the PLSR model was the raw spectral pattern of green plum, which contained 119-dimensional data. The SAE-PLSR model only had a PLSR module input of only 55-dimensional data. Therefore, the calibration set of the SAE-PLSR model was not as effective as that of the PLSR model. However, the prediction set of SAE-PLSR model had better performance compared with that of the PLSR model, the \(R_{P}\) increased by 4.8% and the RMSEP decreased by 19.0%. Figure 6: Sparse autoencoder output data. Meanwhile, the PLSR model in the SAE-PLSR model was replaced with BP and SVR for prediction and analysis. The experiment found that the prediction effect of the two models was not as good as SAE-PLSR. The \(R_{P}\) of the SAE-BP and the SAE-SVR models decreased by 0.9% and 1.2%, respectively, and the RMSE of the SAE-BP and the SAE-SVR models increased by 2.1% and 3.8%. These results indicated that the SAE-PLSR model had good performance in the green plum SSC prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(R_{C}\) & **RMSEC** & \(R_{P}\) & **RMSEP** \\ \hline BP & 0.886 & 0.820 & 0.873 & 0.881 \\ SVR & 0.894 & 0.628 & 0.882 & 0.818 \\ PLSR & 0.968 & 0.442 & 0.895 & 0.807 \\ SAE–BP & 0.942 & 0.582 & 0.930 & 0.668 \\ SAE–SVR & 0.939 & 0.602 & 0.927 & 0.679 \\
**SAE–PLSR** & **0.957** & **0.542** & **0.938** & **0.654** \\ \hline \hline \end{tabular}
\end{table}