## 4 Discussion  
Accurate detection of whether the fruit is damaged by mechanical forces or not could avoid the problem of reduced merchantability of the fruit product due to the inclusion of damaged fruit and also could prevent the damaged fruit from infecting other fruits around it. Therefore, the selection of strawberries that have suffered mechanical damage is important to guide distributors, retailers, and sellers to better decision-making on storage, transportation, and sales strategies. Particularly, since the packaging of fruits during the supply chain is commonly not completely sealed, a probe for detecting VOCs can be inserted into the packaging to collect VOCs from the fruits or detect VOCs at some openings of the packaging to determine whether the fruits have suffered mechanical damage based on VOCs. Therefore, the detection of VOCs in the fruit is expected to enable the identification of those packaged strawberries that have suffered mechanical damage during the supply chain, resulting in good application prospects. Nevertheless, there are only a few studies using e-nose technology to detect mechanically damaged horticultural products. [PERSON] et al. [40] used e-nose technology to detect blueberries with repeated impact; the correct classification rates ranged from 80% to 100%, and the cross-validation rates ranged from 75% to 100%. In another work, [PERSON] et al. [41] classified the impact damage of apples using e-nose technology coupled with multivariate statistical analyses. The present work not only investigated whether e-nose technology could detect strawberries that have suffered impact damage but also further established and compared the results of models based on samples with different storage times after impact and samples with all storage times, thus making the developed models more realistic. However, the VOCs in fruits subjected to mechanical damage changed with longer storage times lead to greater difficulty in modeling based on samples with all storage times after impact. This is also well illustrated by the results in the present study. Meanwhile, in order to provide users with more data about the extent of damage to the fruit, the detection models for damage extent were also established through e-nose technology and obtained good prediction accuracy, which makes it possible to inform users of the specific extent of damage in addition to whether the fruit has suffered mechanical damage. Moreover, the present study evaluated whether e-nose technology could be used to predict when the impact damage had occurred. Figure 6: Five VOCs whose relative contents decreased with increasing storage time after impact onset. Different letters (a, b, c, d) indicate significant differences (\(p<0.05\)). To our knowledge, relatively little work has been done for this purpose. The results will help us to know when the fruit suffers impact damage and determine why the impact occurred, which, in turn, will help to further optimize the fruit supply chain. In addition, it should be noted that the measurement process of e-nose signals of VOCs in the present study did not need us to destroy the strawberry fruits but was applied in a non-destructive way, so it is more suitable for application in the actual fruit supply chain process. From the modeling results, it can be seen that when the impact occurred for 4 h, it was possible to determine the strawberry that has suffered impact damage and also to predict the extent of the damage. Of course, the detection was also possible when the impact occurred 8 and 24 h before. Further research is needed regarding the shortest detection time after the impact occurs. It should be noted, however, that if the time after the impact is too short to start the detection, the VOCs in the strawberry may not have changed enough to be detected.