## 3 Results
### Identification of Strawberries with Impact Damage  
In practical applications, it is often not necessary to know the extent of mechanical damage suffered by the fruit, but only to determine whether the fruit has suffered mechanical damage during the supply chain. Therefore, a study was carried out to classify whether strawberries suffered impact damage based on their e-nose data. Discriminant models for mechanical damage detection were developed based on strawberries stored for 4, 8, and 24 h after impact and for all storage times, 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. For the models established based all three times, there were a total of 288 samples: 216 samples for calibration and 72 samples for prediction. Models were built based on different input variables using PLS-DA and LS-SVM algorithms, respectively. For strawberries at 4 h after impact, the correct rates of the discriminant models were basically above 90%, except for the LS-SVM-EN\({}_{\rm Diff}\) model. Among them, the LS-SVM-EN\({}_{\rm All}\), LS-SVM-EN\({}_{\rm 10}\), and LS-SVM-EN\({}_{\rm 80}\) models achieved 100% correct rates for both the calibration and prediction sets. For the strawberries at 8 h after impact, the results were similar to those of the models at 4 h after impact. The LS-SVM-EN\({}_{\rm Max}\), LS-SVM-EN\({}_{\rm Sum}\), LS-SVM-EN\({}_{\rm 60}\), LS-SVM-EN\({}_{\rm 100}\), and LS-SVM-EN\({}_{\rm 120}\) models achieved 100% correct rates for both the calibration and prediction sets. For the strawberries at 24 h after impact, the accuracy of the discriminative models was lower than those of the models at 4 and 8 h after impact, when the PLS-DA algorithm was used for the calculation. However, when the LS-SVM algorithm was used for the calculation, the accuracy of the models was basically similar to those of the models for the first two time points. In addition, the LS-SVM-EN\({}_{\rm Max}\) and LS-SVM-EN\({}_{\rm Sum}\) models achieved 100% correct rates for both the calibration and prediction sets. When strawberries from all storage time points were used to establish the discriminant models, the average correct rate of the calibration and prediction sets for the PLS-DA models was 80.42%, while that of the LS-SVM models was 97.67% (excluding the LS-SVM-ENDiff model). In addition, the best models were LS-SVM-EN\({}_{\rm Min}\), LS-SVM-EN\({}_{\rm Sum}\), and LS-SVM-EN\({}_{\rm 10}\), which had the same correct rates of 100% and 97.50% for calibration and prediction sets, respectively. Table 2 shows only the best results based on two calibration algorithms. Analyzing the results of the models built based on different input variables, it can be found that for the models developed at the three time points, the numbers of models with 100% correct rates in both the calibration and prediction sets were 3, 6, and 2, respectively, which indicates that it is more favorable to discern whether the strawberry has suffered impact damage around 8 h after the impact, while it is more difficult to detect after 24 h. Nevertheless, when the LS-SVM algorithm was used to perform the calculation, the best models at all three time points achieved 100% correct rates (data not shown), indicating that if we only need to determine whether the strawberry is damaged by impact, then at least 4 h after the impact is sufficient for the detection. When all three time points were used to build the discrimination models, no models reached 100% correct rates in the prediction set, which, again, indicates that detection would be more difficult if fruits with different storage times after suffering an impact are used for modeling. However, when the LS-SVM algorithm was used, the accuracy of the best model built based on all time points was already close to those of the models built based on individual time points. This shows a promising application of e-nose technology to detect whether strawberries have suffered mechanical damage during the supply chain.