## 3 Results
### Prediction of the Extent of Impact Damage to Strawberries Falling from Different Heights  
3.1 Prediction of the Extent of Impact Damage in Strawberries at Different Times after Being Impacted  
Predictive models for the extent of impact damage were developed based on EN\({}_{\text{All}}\) and 11 characteristic variables of strawberries at different times (4, 8, 24 h) after suffering the impact, respectively. For the models established based on each time, there were a total of 96 samples: 72 samples for calibration and 24 samples for prediction. Since there are many models built based on different input variables, Table 1 only shows the best results obtained based on the two calibration algorithms. The RPD values for the PLSR models based on strawberry samples at 4 and 8 h after exposure to impact were 3.007 and 2.983, respectively. Nevertheless, the RPD value for the PLSR models was 1.377 for strawberry samples 24 h after impact, whose accuracy was worse than the models based on strawberry samples 4 and 8 h after impact. The LS-SVM models obtained higher RPD values compared to the PLSR models. The average RPD value of all LS-SVM models was 3.642, which was 48.31% higher than that of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Feature** & **Calibration** & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} & \multirow{2}{*}{**AB\_RMSE**} \\  & **Variables** & **Method** & \(\mathbf{R_{c}}\) & \(\mathbf{Rc^{2}}\) & **RMSEC** & \(\mathbf{R_{p}}\) & \(\mathbf{Rp^{2}}\) & **RMSEP** & **RPD** \\ \hline
4 h & EN\({}_{\text{Sum}}\) & PLSR & 0.944 & 0.892 & 0.372 & 0.944 & 0.858 & 0.426 & 3.007 & 0.053 \\
4 h & EN\({}_{60}\) & LS-SVM & 0.995 & 0.989 & 0.117 & 0.960 & 0.918 & 0.323 & 3.548 & 0.206 \\
8 h & EN\({}_{\text{All}}\) & PLSR & 0.936 & 0.876 & 0.388 & 0.945 & 0.873 & 0.407 & 2.983 & 0.019 \\
8 h & EN\({}_{\text{All}}\) & LS-SVM & 0.986 & 0.972 & 0.185 & 0.964 & 0.929 & 0.304 & 3.764 & 0.119 \\
24 h & EN\({}_{100}\) & PLSR & 0.837 & 0.701 & 0.634 & 0.688 & 0.434 & 0.821 & 1.377 & 0.187 \\
24 h & EN\({}_{\text{Sum}}\) & LS-SVM & 0.990 & 0.980 & 0.165 & 0.962 & 0.920 & 0.309 & 3.614 & 0.144 \\ All & EN\({}_{\text{All}}\) & PLSR & 0.648 & 0.420 & 0.858 & 0.510 & 0.250 & 0.982 & 1.156 & 0.124 \\ All & EN\({}_{\text{All}}\) & LS-SVM & 0.993 & 0.984 & 0.143 & 0.931 & 0.858 & 0.428 & 2.730 & 0.285 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Best models for the prediction of the extent of impact damage in strawberries at different storage times (4, 8, and 24 h) after being impacted and all kinds of storage times. Correlation coefficient of calibration (Rc), root-mean-square error of calibration (RMSEC), correlation coefficient of prediction (Rp), root-mean-square error of prediction (RMSEP), residual predictive deviation (RPD), and the absolute difference between RMSEC and RMSEP (AB_RMSE). Figure 2: Polar plot of the average response data of the e-nose signal for strawberries that have suffered different extents of impact and have been stored for different times after the occurrence of the impact. the PLSR model (2.456). Particularly, the RPD value of the LS-SVM model, based on the samples 24 h after impact, was 2.6 times higher than that of the PLSR model. This illustrates that the LS-SVM algorithm was more suitable for the analysis of e-nose data in the present work, especially for the detection of strawberries that have been stored for a relatively long time after suffering an impact. The reason why it was more difficult to predict the extent of damage of the samples 24 h after the impact than the samples 4 and 8 h after the impact might be that strawberries with different extents of impact damage may change VOCs after storage for a period of time, and the difference in VOCs due to the impact becomes less obvious, so it will cause difficulty in predicting the extent of damage if the storage time after the impact is longer.