Descriptor: Unified Comprehensive Freshness Classification Dataset (UC-FCD)

Properly evaluating food freshness is critical to ensure safety, quality, and customer satisfaction in the food industry. While numerous datasets exist for individual food items, a unified and comprehensive dataset encompassing diversified food categories remains a significant gap in research. This research presented UC-FCD, a novel dataset designed to address this gap. The dataset comprises meticulously curated images of multiple food categories, including fish (Labeo rohita, Mola mola, Pampus argenteus, Dendrobranchiata), grain (Oryza sativa L.), meat products (Gallus gallus domesticus liver), dairy products (Withania coagulans), baked goods (bread, cake, samosa, laddu, dhokla), pickles (berry pickle, mango pickle), and eggs. Each food category is annotated and divided into two classes representing its freshness status. The utility data set was validated 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 despite challenges posed by inter-category variability. The UC-FCD dataset is publicly available and aims for further advancements in food quality assessment, ultimately paving the way for more intelligent and automated food safety solutions.