## 1 Introduction  
Progressive learning is the natural continuous learning process exhibited by humans. There is no limit to the training and testing process, and whenever the human brain is tripped against new concepts, learning resumes. This learning process creates new neurons and remodels existing neurons in the brain. The formation of new neurons decreases the plasticity of the old neurons. The key feature of the human learning process is that the acquisition of new concepts or domain variations does not affect the existing knowledge. These aspects of progressive learning demonstrate their importance in many real-world recognition problems, including food recognition. Food datasets have high diversity due to the continuous arrival of new food categories and ingredients. The definitions of the concepts are different for each person due to several cooking procedures to prepare the same food type. Similarly, in practical computer vision applications, an input image undergoes some form of distortion that causes domain variations. These attributes of food datasets require a network that can mimic the properties of progressive learning as exhibited by humans. The proposed network should remodel automatically by adding new nodes, parameters and interconnections for the novel task while reducing catastrophic forgetting and negative forward and backward transfer for previous concepts. Although in the recent era, food recognition by computer vision methods has attained much attention among the research community, in practical applications, multiple challenges associated with modern techniques have increased the gap with the laboratory environment. In practical food recognition applications, the food photo taken from the mobile camera varies in quality. These variations in picture quality make features extracted from the deep model susceptible to quality distortions. Secondly, the fully connected classifier in deep learning models and other batch classifiers has a fixed class architecture and is unsuitable for progressive learning. Incremental classifiers for food recognition partially address this problem by using features from the deep model due to its good generalization ability [1]. However, high dimensions of deep features increase training time and computational complexity. Previous studies use classical techniques such as Relieff and so forth [2] to reduce the feature dimension. These feature selection methods have black box behavior, and there are no visual explanations of selected features. Finally, most of the existing literature and the incremental classifier proposed by [PERSON] et al. [1] classify only food categories and do not classify food ingredients. The classification of food ingredients has pivotal importance for dietary assessment apps. By keeping in view these challenges, this work makes several contributions to the food recognition literature. (1) This work provides an evaluation of the InceptionResNetV2 model employed for feature extraction under quality distortions. It considers five common types of distortions: Gaussian blur, defocus blur, Gaussian noise, JPEG, contrast. Based on the evaluation, we extracted feature vectors from the quality resilient InceptionResNetV2 model after fine-tuning it on synthetic images generated using our novel data augmentation method \"random iterative mixup\" at no extra training cost. To the best of our knowledge, it is the first detailed evaluation available for food datasets and the first method to tackle quality distortions in the images for food datasets;  
(2) We analyzed the visualizations from the Gradient Explainer, which shows that all the pixels in an area of interest do not contribute equally or positively to the desired output. Based on our findings, we introduced a feature selection technique that uses SHAP scores from the gradient explainer to select the optimal subset of features from the high dimension space by filtering out the irrelevant ones;  
(3) We introduced PKELM for food categorization and ingredient recognition.