Authors : Nimmy Prabha; Anto Mervin S; Abishek Gokul V; Rakhul M; Tamil Maran B

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/y4rc8xdw

Scribd : https://tinyurl.com/5ehuum8x

DOI : https://doi.org/10.38124/ijisrt/25apr1126

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Abstract : The AI Ingredient Analyzer is an advanced system that enables users to gain in-depth insights into the composition of food products. This platform employs AI-driven image processing and text recognition techniques to extract ingredient information from product labels, analyse their nutritional impact, and detect allergens or harmful substances. The system features various functionalities, including real-time ingredient analysis, product comparison, allergen detection, and alternative ingredient suggestions. Additionally, the interactive chatbot assists users with ingredient-related queries, making the tool more user-friendly and accessible. Unlike traditional systems that depend on barcode databases or deep learning models, this approach offers an efficient and lightweight solution that supports both web and mobile interfaces. By integrating AI-driven ingredient analysis and providing accurate, real-time insights, this system allows consumers to make informed dietary decisions with minimal effort.

Keywords : AI Ingredient Analyzer, Image Processing, Ingredient Analysis, Food Transparency, Allergen Detection, Product Comparison, Alternative Suggestions, Health Impact.

References :

1. Rohit, & Sharma, A. (2024). AI-powered ingredient analysis: A study on generative AI and its applications in food safety and nutrition. Journal of AI and Health Tech, 6(1), 45-61.
2. Kumar, S., & Desai, V. (2023). Real-time ingredient analysis using machine learning models. International Journal of Food and AI, 10(2), 102-119.
3. Patel, S., & Bhatia, K. (2024). Leveraging AI for personalized ingredient impact analysis and health recommendations. International Journal of Nutritional AI, 7(3), 134-150.
4. Verma, P., & Gupta, S. (2023). Enhancing food safety with AI-driven allergen detection models. Journal of Artificial Intelligence in Food Science, 5(4), 88-100.
5. Roy, A. (2024, January 15). The impact of generative AI in transforming food safety and ingredient analysis. Retrieved from https://www.aiingredientanalysis.com
6. Agarwal, R., & Joshi, P. (2023). Improving ingredient recommendation systems using machine learning. AI in Food Tech Journal, 9(1), 50-63.
7. Mehta, S., & Singh, N. (2022). Real-time allergen detection using AI for food safety. Proceedings of the 2022 AI and Food Safety Conference, 89-97.
8. Patel, A., & Kumar, J. (2024). Generative AI for ingredient personalization and healthier alternatives in food. Journal of Machine Learning in Food Science, 6(2), 78-95.
9. Reddy, A., & Chauhan, K. (2023). Integrating NLP techniques in food ingredient analysis: A machine learning approach. Artificial Intelligence in Nutrition and Health, 12(1), 104-116.
10. Kapoor, M. (2024, February 5). How AI is revolutionizing food ingredient analysis and consumer health. Retrieved from https://www.ingredientanalysisai.com