## 2 Materials and Methods
### Sample Sets and Feature Matrix  
In order to verify whether the e-nose technology could be used to detect strawberries that have suffered mechanical damage due to impact, a total of three types of models was developed in the present study. The first type of model was the one for the prediction of the extent of impact damage to strawberries falling from different heights, including 0, 20, 40, and 60 cm. Therefore, the dependent variable for modeling was the extent of impact damage, which included: 0, 20, 40, and 60 cm. The second type of model was the one for the identification of strawberries with impact damage. In this type of model, there were two types of samples, i.e., fruits that have suffered impact damage and fruits that have not suffered impact damage. Therefore, the dependent variable for modeling was whether the fruits have suffered impact damage, including: yes (1) and no (\(-1\)). The third type of model was the one for the prediction of the time of occurrence of the impact on the fruit. In the present study, the time between impact occurrence and e-nose data measurement (the time after the occurrence of the impact on the fruit) was used to represent the time of occurrence of the impact on the fruit. Specifically, there were four kinds of times to be predicted, which were 0, 4, 8, and 24 h after impact. Since the VOCs of the fruit will change after being mechanically damaged, the present work analyzed the VOCs of the strawberry fruit and established quantitative models between the VOC data and the time between impact occurrence and e-nose data measurement so as to predict when the strawberry was impacted. Therefore, the dependent variable for modeling was the time of the occurrence of the impact on the fruit, including: 0, 4, 8, and 24 h. For a specific impact extent and a specific post-impact storage time, there were 24 samples. For each model sample set, 75% of the samples were randomly selected as the calibration set, and 25% of the samples were selected as the prediction set.  
A total of two major categories of variable sets were used as inputs to establish the chemometric models in this study. One category included all 18 sensors, each measuring 121 s (121 variables), so there were 2178 variables in total (EN\({}_{\text{All}}\)). The other category included some characteristic variables of the 18 sensors, such as the response data at the 10 th, 20 th, 40 th, 60 th, 80 th, 100 th, and 120 th seconds, which were called EN\({}_{10}\), EN\({}_{20}\), EN\({}_{40}\), EN\({}_{60}\), EN\({}_{80}\), EN\({}_{100}\), and EN\({}_{120}\), respectively; the sum of the response curve values, which was called EN\({}_{\text{Sun}}\); the minimum value of the response curve, which was called EN\({}_{\text{Max}}\); the minimum value of the response curve, which was called EN\({}_{\text{Min}}\); and the difference between the maximum and minimum values of the response curve, which was called EN\({}_{\text{Diff}}\). Extracting the signal at different time points of the e-nose signal acquisition process is a commonly used method to select the e-nose signal for modeling. Additionally, EN\({}_{\text{Sum}}\), EN\({}_{\text{Max}}\), EN\({}_{\text{Min}}\), and EN\({}_{\text{Diff}}\) are frequently used for the modeling of e-nose data, including in studies such as the prediction of the ripeness of kiwifruit [32] and the detection of the quality of pecans [33]. The above-mentioned variables were used separately to establish the models to determine the best characteristic variable. Only one characteristic variable was used in each model, and because there were 18 sensors, there were 18 variables in total for each model, based on the characteristic variable. In general, for all types of modeling, both \(\text{EN}_{\text{All}}\) and characteristic variables were used separately.