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@article{200063,
        author = {ATHARVA SONAR and GOPAL DHURJAD and MADHUR DHURJAD},
        title = {NutriLens: An AI-Powered Smart Food Label Scanner for Real-Time Ingredient Transparency and Regulatory Compliance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1156-1162},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200063},
        abstract = {NutriLens is an AI-powered web and mobile application that enables consumers to make safer, more informed food choices by analyzing packaged food labels in real time. Leveraging Optical Character Recognition (OCR) and Natural Language Processing (NLP), the system extracts and interprets ingredient data from complex, multilingual labels. Extracted ingredients are cross-referenced against trusted global food safety databases including FSSAI, FDA, and EFSA to detect harmful substances, allergens, and banned additives. Users can scan a product label or upload an image to receive an instant, easy-to-understand safety report categorized into Safe, Moderate, Harmful, and Banned levels. The platform is built with ReactJS, Express.js, and MongoDB, and integrates Large Language Models (LLMs) such as Llama-3 to generate personalized, human-readable insights based on individual health profiles. NutriLens bridges the gap between complex food science and consumer awareness, setting a new standard for intelligent, transparent, and regulation-aware food labeling technology.},
        keywords = {Artificial Intelligence, OCR, NLP, Food Label Analysis, Ingredient Safety, FSSAI, FDA, EFSA, Allergen Detection, LLM, ReactJS, MongoDB, Consumer Health, Food Transparency, Regulatory Compliance.},
        month = {May},
        }