Authors: Sanket Dudhade, Sahil Gilbile, Aditya Gavali, Atul Chaudhari
Abstract: Food safety concerns, particularly the presence of undeclared allergies and artificial ingredients, have significantly increased worldwide as a result of the exponential growth in the consumption of packaged foods. Customers' manual label reading is inefficient, error-prone, and frequently hampered by multilingual packaging and complex ingredient nomenclature. An innovative technique for automating the detection of allergens and additives is provided by Artificial Intelligence (AI) through the use of Deep Learning (DL), Natural Language Processing (NLP), and Optical Character Recognition (OCR). A comprehensive analysis of AI-based smart systems for detecting chemicals and allergies in packaged foods is presented in this study. It looks at benchmark datasets, talks about different machine learning and transformer-based models, looks at key performance validation measures, and looks at the architectures that are already in place. The article also discusses difficulties such as data imbalance, interpretability problems, and computing constraints in real-time systems. Experimental trends show that hybrid OCR–NLP frameworks achieve detection accuracies of over 97% on benchmark datasets and demonstrate greater generalization across languages and package formats.The results of the study indicate that integrating state-of-the-art AI technology into food safety systems has the potential to revolutionize consumer protection, regulatory compliance, and public health. The findings emphasize that AI models must be globally scalable, interpretable, and privacy-preserving in order to guarantee transparency and confidence in automated food labeling.
DOI: http://doi.org/10.5281/zenodo.20765726
