## 3 Results and Discussions
### Statistical Analysis  
The statistical measures obtained for the CNN and RBNN based classification models are shown in Figure 13. As can be seen, except for Recall, both medium and range values were better for CNNs.  
The median F1-score obtained in our experiment were 89.42% and 85.43% for CNN and RBNN models, respectively. However, comparing the adjust with those obtained by [PERSON] and [PERSON] [6], over 97%, are relatively lower, which could be due to fewer differences between elements in _Cucurbita pepo_ L. in front of _Malus Domestica Borkh_, mainly expressed in their angularity and roundness.  
The classification capacity of both models (CNN and RBNN) was compared through the Accuracy, Recall, Precision, and F1-score indicators. The results, Table 1, show that  
Figure 10: Images of microstructural elements: (**a**) cells and (**b**) intercellular spaces.  
using CNN gives a higher performance in terms of the four indicators mentioned. When performing the \(t\)-test, it was found that the \(p\)-value is less than 0.05, which shows that there is a statistically significant difference between the means of both models.  
Figure 11: Selected feature values for microstructural elements in the studied sample: (**a**) perimeter, (**b**) length of mayor axis, (**c**) length of minor axis, and (**d**) roundness.  
Figure 12: Accuracy during CNN training.  
The significant superiority in the CNN's performance reflects its ability to encode classification information in the internal layers, which it does not share with the RBNN structure. Similarly, when comparing the standard deviation of performance in terms of F1-score, both classifiers have similar stability, with a reduced variation between the one hundred different iterations performed using the \(k\)-fold cross-validation strategy. This stability is indicative of the generalization capacity of both models.  
Although the CNN AlexNet allows us to classify using two totally connected layers and the _softmax_ function, some works have improved the adjustment by employing a classifier coupled to the CNN, such as the works of [PERSON] et al. (2018), [PERSON] et al. (2018), or they have created specific CNN architectures. However, this requires a much more extensive database for training, such as in the works of [PERSON] et al. (2018), [PERSON] et al. (2018), and [PERSON] et al. (2018), among others. In this sense, new studies should be carried on to test this in microstructural element discrimination in vegetal origin food.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & \multicolumn{5}{c}{**Performance Measure**} \\ \cline{2-6}  & & **Accuracy** & **Recall** & **Precision** & \(\mathbf{F_{1}-Score}\) \\ \hline \multirow{2}{*}{Means} & \(t\)-test & \(-47.4999\) & \(-25.9556\) & \(-36.9701\) & \(-14.8146\) \\  & \(p\)-value & 0 & 0 & 0 & 0 \\ \hline \multirow{2}{*}{Standard deviation} & F-test & 0.5036 & 0.6453 & 2.0789 & 1.2957 \\  & \(p\)-value & 0.0007 & 0.0304 & 0.0003 & 0.1992 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for the comparison of the models.  
Figure 13: Performing measures for CNN and RBNN based classifier: (**a**) perimeter, (**b**) length of mayor axis, (**c**) length of minor axis, and (**d**) roundness.