Mapping the AI Landscape in Food Science and Engineering: A Bibliometric Analysis Enhanced with Interactive Digital...

Area of Science:

• Food Science and Engineering
• Artificial Intelligence Applications

Background:

• AI adoption in food science lags due to system complexity and risk aversion.
• Computational techniques have long supported food R&D, with AI applications expanding significantly since 2019.
• AI is increasingly used in food process control, safety, quality, sensory evaluation, traceability, and supply chain management.

Purpose of the Study:

• To provide tools for synthesizing and exploring the rapidly growing body of AI in food science literature.
• To enhance accessibility of research findings for researchers and industry stakeholders.
• To bridge the gap between academic AI research and practical industry implementation.

Main Methods:

• Developed an interactive dashboard with a curated dataset of food AI review articles.
• Utilized advanced bibliometric analyses for trend and relationship exploration.
• Demonstrated customized large language model (LLM) tools for literature interrogation.
• Included industry case studies on AI in ingredient discovery, product development, sorting, and sensory analytics.

Main Results:

• An interactive platform enabling user-driven exploration of food AI research trends.
• LLM tools enhance targeted literature searches and understanding.
• Case studies illustrate AI's practical role in various food industry sectors.
• A comprehensive overview combining academic synthesis with real-world applications.

Conclusions:

• The review offers a valuable resource for navigating the evolving field of AI in food science.
• Interactive tools and case studies facilitate knowledge discovery and actionable insights.
• This work supports both domain-specific learning and practical industry adoption of AI technologies.