Mapping the AI Landscape in Food Science and Engineering: A Bibliometric Analysis Enhanced with Interactive Digital Tools and Company Case Studies

IF 7.6 2区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Jordan Pennells, Peter Watkins, Alexander L. Bowler, Nicholas J. Watson, Kai Knoerzer
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引用次数: 0

Abstract

The proliferation of research on Artificial Intelligence (AI) in food science and engineering has made it increasingly difficult to synthesise relevant insights effectively. Although AI adoption in the food industry has grown, it lags behind sectors like finance and healthcare due to the complexity of food systems, including high process variability, risk aversion towards novel technologies, and constrained investment appetite. Historically, computational techniques and AI-adjacent technologies like expert systems and empirical modelling have supported food research and development for decades. More recently, AI applications have broadened to include process control, food safety, ingredient and product quality, sensory evaluation, traceability, and supply chain management. In response to the rapid increase in AI-related food science publications – particularly since 2019 – this review introduces tools for dynamically synthesising and exploring this evolving knowledge base. We present an interactive dashboard that integrates a curated dataset of food AI review articles with advanced bibliometric analyses, enabling user-driven exploration of research trends and thematic relationships. Additionally, we demonstrate the use of customised large language model (LLM) tools for targeted literature interrogation, enhancing accessibility for researchers and industry stakeholders. Complementing this academic synthesis, we profile selected industry case studies where AI plays a central role in ingredient discovery, product development, intelligent sorting, and sensory analytics. By combining interactive research tools with real-world case studies, this review offers a comprehensive snapshot of Food AI and begins to bridge the gap between academic research and industry implementation, providing a valuable resource for those seeking both domain-specific knowledge and actionable insights.

绘制食品科学与工程中的人工智能景观:交互式数字工具和公司案例研究增强的文献计量分析
人工智能(AI)在食品科学和工程领域的研究激增,使得有效地综合相关见解变得越来越困难。尽管人工智能在食品行业的应用有所增长,但由于食品系统的复杂性,包括高流程可变性、对新技术的风险厌恶以及投资意愿受限,它落后于金融和医疗保健等行业。从历史上看,几十年来,计算技术和人工智能相关技术(如专家系统和经验建模)一直在支持食品研发。最近,人工智能的应用范围已经扩大到过程控制、食品安全、成分和产品质量、感官评估、可追溯性和供应链管理。为了应对与人工智能相关的食品科学出版物的快速增长,特别是自2019年以来,本综述介绍了动态合成和探索这一不断发展的知识库的工具。我们提供了一个交互式仪表板,集成了食品人工智能评论文章的精选数据集和先进的文献计量分析,使用户能够驱动研究趋势和主题关系的探索。此外,我们展示了使用定制的大型语言模型(LLM)工具进行有针对性的文献查询,提高了研究人员和行业利益相关者的可访问性。为了补充这一学术综合,我们介绍了人工智能在成分发现、产品开发、智能分类和感官分析中发挥核心作用的行业案例研究。通过将交互式研究工具与实际案例研究相结合,本综述提供了食品人工智能的全面快照,并开始弥合学术研究与行业实施之间的差距,为那些寻求特定领域知识和可操作见解的人提供了宝贵的资源。
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来源期刊
Food Engineering Reviews
Food Engineering Reviews FOOD SCIENCE & TECHNOLOGY-
CiteScore
14.20
自引率
1.50%
发文量
27
审稿时长
>12 weeks
期刊介绍: Food Engineering Reviews publishes articles encompassing all engineering aspects of today’s scientific food research. The journal focuses on both classic and modern food engineering topics, exploring essential factors such as the health, nutritional, and environmental aspects of food processing. Trends that will drive the discipline over time, from the lab to industrial implementation, are identified and discussed. The scope of topics addressed is broad, including transport phenomena in food processing; food process engineering; physical properties of foods; food nano-science and nano-engineering; food equipment design; food plant design; modeling food processes; microbial inactivation kinetics; preservation technologies; engineering aspects of food packaging; shelf-life, storage and distribution of foods; instrumentation, control and automation in food processing; food engineering, health and nutrition; energy and economic considerations in food engineering; sustainability; and food engineering education.
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