Intelligent Food System: Generative AI for Smarter, Healthier Choices
DOI:
https://doi.org/10.47392/IRJAEH.2025.0065Keywords:
Adulteration Detection, Food Analysis, Generative AI, Nutritional Assessment, Generative Adversarial Networks (GANs), Large Language Models (LLMs)Abstract
In the modern era, maintaining a healthy lifestyle has become increasingly challenging. Ensuring food safety and making informed dietary choices are crucial due to the rising threats of food adulteration and health-related concerns. Our research focuses on developing a Generative AI-based model capable of tasks such as ingredient analysis, food adulteration detection, nutritional assessment, and personalized dietary guidance. Leveraging cutting-edge Generative AI technologies, including Large Language Models (LLMs), Bidirectional Encoder Representations from Transformers (BERT), and Generative Adversarial Networks (GANs), the system delivers accurate and real-time evaluations. The approach involves assessing nutritional content, identifying adulterants, interpreting ingredient lists, and providing tailored dietary suggestions. The system employs advanced AI tools like GANs for precise food recognition and adulteration detection, LLMs for processing ingredient information, and BERT with cross-attention mechanisms for comparing product ingredients. By offering reliable and actionable insights, this approach promotes healthier eating habits. The methodology marks a significant step forward in enhancing overall well-being.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Research Journal on Advanced Engineering Hub (IRJAEH)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.