Understanding Application of AI Technologies in Food Processing Industry: A Machine Learning Approach

Abstract

This study conducts a literature review on artificial intelligence applications in the food processing industry (FPI), investigating its intellectual structure and highlighting critical research themes and emerging trends. Analyzing 490 Scopus indexed articles (1979-2025), the study systematically assessed through a multifaceted analytical framework encompassing metrics such as temporal publication trends, authorship dynamics, journal contributions, and the influence of academic institutions. Furthermore, a structural topic modeling (STM) methodology was employed which uncovers following eight research themes: leveraging AI, blockchain, and robotics for supply chain transformation; AI adoption for increasing performance among SMEs in emerging economies; AI-driven innovations in biofilm and microbial management; use of AI for sustainable process optimization in the food industry; AI-driven quality assurance in food products; predictive modeling and neural networks in food processing management; health risks and consumer behavior in agriculture and food sectors; and managing food safety and quality for consumer health and trust using AI. The findings provide critical insights for researchers, industry practitioners, and policymakers, highlighting the transformative role of AI in FPI and offering directions for future research and practical implementation.