Food Safety Prediction System: A Machine Learning Approach to Determining Safe Food Consumption Windows

K. Satyanarayana Raju; P. Yuva Rajesh; M. Abhishek; K. Nimshi Babu

doi:10.55524/ijircst.2025.13.2.10

Abstract

This paper presents a novel machine learning-based system for predicting safe food consumption windows. By integrating environmental factors, cooking methods, and storage conditions, our system dynamically estimates food safety durations. Using a Gradient Boosting Regressor model, the system achieves robust performance (with a mean absolute error of approximately ±2.3 hours and an R² score of 0.89) across diverse storage scenarios. In addition, the full-stack implementation—featuring a Next.js frontend and a Flask API backend—facilitates real-time predictions and user-friendly data entry. This approach has significant potential to reduce foodborne illness risks while optimizing storage practices.

Keywords

Food Safety, Machine Learning, Gradient Boosting, Food Consumption Window, Full-Stack Deployment, Real-Time Prediction.

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Cites this article as

K. S. Raju, P. Y. Rajesh, M. Abhishek, K. N. Babu, "Food Safety Prediction System: A Machine Learning Approach to Determining Safe Food Consumption Windows", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 13, no. 2, pp. 67-71, 2025. doi: https://doi.org/10.55524/ijircst.2025.13.2.10

Corresponding Author

Pechetti Yuva Rajesh

B.Tech Scholar, Information Technology, SRKR Engineering College, Bhimavaram., Palakollu, India

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