## 3 Results and Discussion
### Prediction of Storage Period and Tomato Quality During Storage by Regression Analysis  
The necessity of studying tomato quality during storage is twofold: to preserve their freshness and to minimize post-harvest spoilage. To this end, predictive models were developed to assess storage time and flesh quality parameters using textural information obtained from image analysis of tomato peels. A gradient boosting regression model was selected for this specific challenge. The process involved the construction of multiple decision trees in succession, with the errors in each tree being corrected to enhance the accuracy of the final model. The model was trained and evaluated K times by using different parts of the dataset for training and testing in several iterations, ensuring that the model generalizes effectively to unknown data. This method helps to provide a more accurate estimate of the model's performance. The degree to which the model fits the data is measured by the coefficient of determination (R-squared, R\({}^{2}\)). The models focusing on the predictability of the tomato's storability were built using regression analysis and the features extracted from the tomato images. \begin{table}
\begin{tabular}{c c c c} \hline  & **TPC (mg GAE/kg Flesh)** & \
\begin{tabular}{c} **ABTS (mg Trolox (TE)/kg** \\ **Flesh)** \\ \end{tabular} &
\begin{tabular}{c} **FRAP (mg Fe (II)/kg Flesh)** \\ \end{tabular} \\ \hline Day 3 & 154.17 \(\pm\) 4.78a \({}^{*}\) & 232.11 \(\pm\) 14.65a & 1052.67 \(\pm\) 47.29a \\ Day 7 & 168.17 \(\pm\) 10.49a & 218.79 \(\pm\) 25.46a & 1104.89 \(\pm\) 47.40a \\ Day 10 & 188.64 \(\pm\) 9.15b & 212.92 \(\pm\) 28.59a & 1097.14 \(\pm\) 63.81a \\ Day 14 & 229.59 \(\pm\) 9.62c & 273.14 \(\pm\) 11.44b & 782.43 \(\pm\) 31.05b \\ Day 17 & 204.81 \(\pm\) 15.28b & 328.56 \(\pm\) 15.05c & 763.73 \(\pm\) 42.91b \\ Day 24 & 167.01 \(\pm\) 17.76a & 242.44 \(\pm\) 24.11 ab & 801.05 \(\pm\) 28.99b \\ \hline \end{tabular} \({}^{*}\)Statistically (_p_ < 0.05) different values are illustrated by different letters in the same column.  
\end{table} 
Table 3: Total phenolic content (TPC), antiradical activity (ABTS), and antioxidant activity (FRAP) of tomato flesh during storage. The features that exhibited the highest R\({}^{2}\) (R\({}^{2}\) > 0.952), were identified as being long-run emphasis (LRE), correlation, angular second moment (ASM), RLN, skewness, and RP (Figure 8). The findings demonstrated that the storage duration of tomato samples can be accurately predicted using a gradient-boosting regression model that was trained using the extracted features of tomato peel images. This finding aligns with the results of similar studies conducted by our research team and other researchers [35; 36; 77; 78; 79], who have investigated various vegetable products. The findings underscore the importance of leveraging machine learning methodologies, underpinned by image texture and instrumental analysis outcomes, to ensure the conservation of vegetables and fruits. To further estimate the tomatoes' quality during 24-day storage period, textural features derived from grayscale and colored images of the tomato peel were correlated with physicochemical attributes (brix, firmness, and moisture) as well as the TPC, antioxidant, and antiradical activity of tomato (see Figure 9), using ML algorithms. The results demonstrated that the standard deviation, energy, mean, a*, and b* values of the tomato peel have been found to be reliable indicators of the antiradical activity of the tomato flesh towards ABTS free radical (with a mean R\({}^{2}\) > 0.952 and a test R\({}^{2}\) > 0.798) (Figure 9a). Regarding TPC (Figure 9b) and antioxidant activity via FRAP assay (Figure 9c) of the tomato flesh, the indicators found were kurtosis, energy, mean, L*, and a*, plus mean, skewness, grey level non-uniformity (GLN), and L*, respectively, with prediction accuracy R\({}^{2}\) higher than 0.9.