## 3 Results
### Prediction of the Extent of Impact Damage to Strawberries Falling from Different Heights
3.2 Prediction of the Extent of Impact Damage in Strawberries at All Storage Times after Being Impacted  
In practice, when e-nose technology is used to detect damaged strawberries, it is difficult to know how much time has passed since the damage occurred. Therefore, the models developed respectively based on strawberries at different storage times after the onset of impact can only be applied to some cases where it is known when the impact occurred. Therefore, other PLSR and LS-SVM models were established for the prediction of the extent of impact damage based on the strawberries at all storage times after being impacted, which means strawberries with different storage times after impact were all used for modeling. For the models established based all three times, there were a total of 288 samples: 216 samples for calibration and 72 samples for prediction. The results are also shown in Table 1.  
When the PLSR algorithm was used to establish prediction models, the prediction results for both ENAll and the 11 characteristic variables were poor and could not be used for practical applications (data not shown). When the LS-SVM algorithm was used to establish prediction models, their prediction results were significantly better than the PLSR algorithm, except for the model based on the ENDiff variable (data not shown). The best LS-SVM model achieved an RPD value of 2.730, which was 136.16% higher than the best PLSR model (1.156). Although the AB_RMSE values of the LS-SVM models were higher than those of the PLSR models, they did not exceed 0.3, except for the LS-SVM-ENDiff model (data not shown), indicating that the robustness of the LS-SVM models was acceptable. By comparing the model based on a certain storage time with the model based on all storage times, it can be seen that the models in the former had a significantly higher prediction accuracy. This suggests that models based on samples from different storage times obtained better prediction results than models based on strawberries from all storage times. The reason for this can be assumed to be that both the different extents of impact and the storage time after impact could cause changes in the VOCs of strawberries. For example, the change in VOCs of strawberries with a lighter impact extent after a long storage period may be similar to the change in VOCs of strawberries with a severe impact extent but a shorter storage period after the impact. Therefore, when strawberries with different storage times after impact were all placed in the same calibration sample set for calculation, it was more difficult than modeling strawberries based on different storage times after impact separately. The results of some previous studies also showed that it is more difficult to predict the extent of mechanical damage in fruits based on all storage times after suffering mechanical damage [38; 39]. However, such difficulties could be compensated by using the LS-SVM algorithm to calibrate the prediction model. Especially considering that it is impossible to know the time of occurrence of the damage suffered by the fruit in the actual supply chain, the LS-SVM models developed in Table 1 are more suitable for practical applications.