## 3 Results and Discussion
### Regression Analysis  
Investigating the quality of fruits and vegetables during their storage is crucial to maintaining consistent quality for consumers and reducing post-harvest spoilage. Consequently,  
Figure 9: OPLS-DA analysis for the sample space: (**A**) score plot of OPLS-DA analysis. The red and green dots correspond to Group 1 and Group 2 classification, respectively; (**B**) VIP plot of the studied features; (**C**) validation of the OPLS-DA analysis by permutation test statistics indicates that the extracted model is significantly different from a model built on random data. The permutation tests were carried out with 1000 random permutations, thus providing significance of the model at the 0.001 level (R\({}^{2}\)Y = 0.932 and Q\({}^{2}\) = 0.901).  
the textural features derived from the image analysis of the cucumber peels were used to develop predictive models for the evaluation of storage time and flesh quality parameters during storage. A gradient-boosting regression model was selected to use for this particular task. Gradient boosting is a machine learning approach that is suitable for both regression and classification assignments. The method involves the construction of multiple decision trees sequentially, rectifying the errors of each tree in turn, thus enhancing the accuracy of the final model. The K-fold evaluation technique was used. In K-fold cross-validation, the dataset is partitioned into K subsets, and the model undergoes training and evaluation K times, with each repetition using a different subset as the test set and the remaining subsets as the training set. This approach aids in obtaining a more dependable estimation of the model's performance. The coefficient of determination (R-squared, R2) quantifies how well the regression model aligns with the observed data. In a further step, using the features extracted from the cucumber peel images, a regression analysis was performed to create models for cucumber samples' storage day prediction. The textural features with the highest coefficient of determination (R2 > 0.995) were gray-level non-uniformity (GLN), L*, a*, b*, contrast, mean, correlation, standard deviation, skewness, and long-run emphasis (LRE) (Figure 10). Taken together, the findings showed that a gradient-boosting regression model trained with image-extracted features can effectively predict the storage day of cucumber samples with high accuracy. Other studies in several food matrices have also yielded comparable findings [13; 24], thereby underscoring the significance of utilizing image texture analysis results in food preservation research.  
In an additional step, the physicochemical quality parameters of cucumber flesh (h angle, moisture, and firmness) were correlated with textural features derived from the grayscale images of the cucumber peel in order to estimate the quality of the cucumbers during the 28-day storage period (Figure 11). The results demonstrated that the standard deviation, contrast (the level of fluctuations in intensity within the image), energy (homogeneity of gray levels in an image), and angular second moment (ASM) (original image's homogeneity) values of the cucumber peel are reliable indicators of the moisture content of the cucumber flesh (with a mean R2 > 0.96 and a test R2 > 0.93). Moreover, the standard deviation, skewness, kurtosis, and angular second moment (ASM) values of the cucumber peel are effective indicators of the firmness values of the cucumber flesh (with a mean R2 > 0.97 and test R2 > 0.95), whereas the standard deviation, skewness, contrast, and homogeneity of hue angle values (with a mean R2 > 0.98 and test R2 > 0.96) were reliable indicators of the h angle of the cucumber flesh. A predictive modeling application that utilizes the features derived from image analysis can be employed as a tool for the assessment of fruit and vegetable quality and shelf life.  
Figure 10: Prediction of cucumber storage days from textural features of cucumber peel.