Unified Comprehensive Freshness Classification Dataset (UC-FCD) for Diverse Food Categories

The proper evaluation of food freshness is critical to ensure safety, quality along with customer satisfaction in the food industry. While numerous datasets exists for individual food items,a unified and comprehensive dataset which encompass diversified food categories remained as a significant gap in research. This research presented UC-FCD, a novel dataset designed to address this gap. The dataset comprised of meticulously curated images of multiple food categories, which included fish (Labeo rohita, Mola mola, Pampus argenteus, Dendrobranchiata), grain (Oryza sativa L.), meat product (Gallus gallus domesticus liver), dairy product (Withania coagulans), baked goods (bread, cake, samosa, laddu, dhokla), pickles (Berry pickle, Mango pickle), and egg. Each food category is annotated and divided into two classes representing its freshness status. The validation of the dataset for utility was done using state-of-the-art deep learning models for freshness classification. The dataset enabled comprehensive experiments on cross-category generalization, transfer learning and multi-modal classification approaches, which provided a robust foundation for researchers and industry practitioners. The results underscored the potential of advanced neural networks to achieve high accuracy in freshness classification which challenges posed by inter-category variability. The UC-FCD dataset is publicly available and aimed for further advancements in food quality assessment, ultimately paving the way for more intelligent and automated food safety solutions.