## 5 Discussion  
Based on the extensive analysis carried out for this research work, we can state the following considerations. For feature extraction, although, the CNN model has given discriminatory features. However, various quality distortions in the images impact the robustness of features as observed in Figure 10. Fine-tuning the network by using our proposed data augmentation \"random iterative mixup\" improves the robustness of feature vectors extracted from the deep model by reducing the impact of distortions as shown in Figure 11. Our proposed augmentation method does not increase the training set size or training time compared to fine-tuning with low-quality images. It also inherits the benefits of mixup and is extremely good at regularizing deep models. To the best of our knowledge, it is the first detailed work to evaluate the impact of quality distortions and tackled this challenge for food datasets. Despite advantages, we have not evaluated our method at extreme quality distortions as such distortions occur rarely in the photo captured by using modern smartphone cameras. In the second step, we selected the optimal subset of quality resilient features from the high dimension space. Due to the absence of a rigid structure of the food, selecting the optimal subset of features is a problem. We tackle this problem by employing the Gradient Explainer strategy to understand the contribution of an area of interest in food images towards the output layer. Our visualization by Gradient Explainer in Figures 15-17 shows that all pixels in the food image do not contribute equally or positively towards the most likely class. Based on these findings, we proposed a novel strategy that uses the SHAP values from the gradient explainer method to rank the features. Our method selects the top 500 most relevant features by filtering out irrelevant ones. For food category and ingredient detection, the accumulated training time is reduced by 51.73%. It is also observed in Table 4 that it improves the performance of the classifier as compared to Relieff. Following feature extraction and selection, we employed the progressive classifier PKELM for food categorization and ingredient detection. It successfully replaced the ran \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **F1-Score** & **Average F1-Score of All Sessions** \\ \hline Arch-D [24] & 67.17\% & - \\ ResNet-152(AFN+BFL) [50] & 73.63\% & - \\ QIF+SHAP+PKELM & 87.02\% & 89.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Result of comparison with other methods of ingredient detection for VireoFood-172 dataset. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Accuracy** & **Average Accuracy of All Sessions** \\ \hline EnsembleNet [46] & 72.10\% & - \\ ResNet-152+SVM-RBF [48] & 64.98\% & - \\ [PERSON] et al. [18] & 77.00\% & - \\ GAN [49] & 75.30\% & - \\ CNN+Inception [45] & 77.40\% & - \\ SELC [26] & 55.89\% & - \\ ARCIKELM [1] & 81.24\% & - \\ QIF+SHAP+PKELM & 83.71\% & 86.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of research using deep feature extraction and fine-tuning methods to classify the Food101 dataset. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Accuracy** & **Average Accuracy of All Sessions** \\ \hline [PERSON] et al. [18] & 54.50\% & - \\ AlexNet [47] & 67.57\% & - \\ ARCIKELM [1] & 69.32\% & 75.85\% \\ QIF+SHAP+PKELM & 71.12\% & 79.45\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of research using deep feature extraction and fine-tuning to classify the UEC-FOOD256 dataset. dom selection of nodes in ARCIKELM [1] by activity value for the progression of mapping nodes. To extend PKELM for multi-label classification, we employed the bipolar step function to process test output and then selected the column labels of the resulting matrix with value one.