Blockchain-Integrated AI Systems for Contamination Prediction in Ready-to-Eat Foods: A Systematic Review

Nwanneka Joseph

Abstract: The integration of blockchain and artificial intelligence (AI) technology is an emerging approach to enhancing traceability and contamination prediction in the ready-to-eat (RTE) food industry. This systematic review evaluates the current research on blockchain-integrated AI systems, examining their applications in food safety, supply chain transparency, and contamination risk prediction. A comprehensive analysis of reports, articles, and case studies assesses the effectiveness, challenges, and future prospects of these technologies. The findings highlight blockchain-AI integration as a promising solution for RTE food safety while identifying key gaps in scalability, interoperability, and regulatory compliance. This review provides a framework for future research and practical implementation in the food industry.

Keywords: Food safety, Foodborne illness, Food traceability, Blockchain technology, Contamination prediction, Ready-to-eat foods (RTE), Predictive analytics, Machine learning (ML), Regulatory compliance, Food supply chain

How to Cite?: Nwanneka Joseph, "Blockchain-Integrated AI Systems for Contamination Prediction in Ready-to-Eat Foods: A Systematic Review", Volume 14 Issue 2, February 2025, International Journal of Science and Research (IJSR), Pages: 772-778, https://www.ijsr.net/getabstract.php?paperid=SR25212021618, DOI: https://dx.doi.org/10.21275/SR25212021618