Jordan Pennells, Peter Watkins, Alexander L. Bowler, Nicholas J. Watson, Kai Knoerzer
{"title":"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","authors":"Jordan Pennells, Peter Watkins, Alexander L. Bowler, Nicholas J. Watson, Kai Knoerzer","doi":"10.1007/s12393-025-09413-w","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":565,"journal":{"name":"Food Engineering Reviews","volume":"17 3","pages":"465 - 489"},"PeriodicalIF":7.6000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12393-025-09413-w.pdf","citationCount":"0","resultStr":"{\"title\":\"Mapping the AI Landscape in Food Science and Engineering: A Bibliometric Analysis Enhanced with Interactive Digital Tools and Company Case Studies\",\"authors\":\"Jordan Pennells, Peter Watkins, Alexander L. Bowler, Nicholas J. 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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.</p></div>\",\"PeriodicalId\":565,\"journal\":{\"name\":\"Food Engineering Reviews\",\"volume\":\"17 3\",\"pages\":\"465 - 489\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s12393-025-09413-w.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Engineering Reviews\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12393-025-09413-w\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Engineering Reviews","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s12393-025-09413-w","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Mapping the AI Landscape in Food Science and Engineering: A Bibliometric Analysis Enhanced with Interactive Digital Tools and Company Case Studies
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.
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.