Zhixin Jia , Hongwei Hou , Huan Chen , Yaning Fu , Wenjun Mu , Xinting Yang , Jingbin Zhang
{"title":"从被动包装到自我感知包装:柔性传感器与人工智能集成为智能、可持续食品包装提供动力","authors":"Zhixin Jia , Hongwei Hou , Huan Chen , Yaning Fu , Wenjun Mu , Xinting Yang , Jingbin Zhang","doi":"10.1016/j.tifs.2025.105254","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The global agri-food sector faces persistent challenges in ensuring food quality, safety, and sustainability. Conventional packaging functions as a passive barrier, incapable of monitoring physicochemical or microbiological changes in real time. This limitation necessitates reliance on conservative expiration dates, thereby contributing to significant food waste. Smart interactive packaging addresses these issues by detecting and communicating the dynamic conditions of food products.</div></div><div><h3>Scope and approach</h3><div>This review critically assesses flexible sensor materials, including conductive polymers and carbon-based nanomaterials, as well as their integration with wireless communication systems and energy-harvesting technologies. It also evaluates machine learning-driven artificial intelligence (AI) methodologies for freshness scoring and shelf-life prediction. Practical implementations are reviewed through case studies involving sensor patches, embedded films, logistics optimization, and consumer interfaces.</div></div><div><h3>Key findings and conclusions</h3><div>Flexible sensors enable millimeter-scale monitoring of temperature, humidity, gases, pH, and microbial metabolites, while roll-to-roll printing supports scalable integration into packaging. AI-driven pipelines process multimodal sensor data to generate freshness scores, estimate shelf life, and detect anomalies. These systems employ regression and tree-based models as well as convolutional and recurrent neural networks enhanced by explainable AI techniques. Demonstrations of sensor patches, embedded films, and AI-driven logistics have achieved spoilage reductions. Despite these advances, challenges remained in addressing sensor drift, achieving energy autonomy‒systems capable of operating independently through on-board energy harvesting and management, ensuring material circularity, and meeting regulatory requirements. Future efforts should focus on integrating self-healing materials, improved energy harvesting, biodegradable electronics, and human–computer interaction to realize sustainable autonomous smart packaging systems.</div></div>","PeriodicalId":441,"journal":{"name":"Trends in Food Science & Technology","volume":"164 ","pages":"Article 105254"},"PeriodicalIF":15.4000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From passive to self-aware packs: Flexible Sensor-AI integration powering intelligent, sustainable food packaging\",\"authors\":\"Zhixin Jia , Hongwei Hou , Huan Chen , Yaning Fu , Wenjun Mu , Xinting Yang , Jingbin Zhang\",\"doi\":\"10.1016/j.tifs.2025.105254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The global agri-food sector faces persistent challenges in ensuring food quality, safety, and sustainability. Conventional packaging functions as a passive barrier, incapable of monitoring physicochemical or microbiological changes in real time. This limitation necessitates reliance on conservative expiration dates, thereby contributing to significant food waste. Smart interactive packaging addresses these issues by detecting and communicating the dynamic conditions of food products.</div></div><div><h3>Scope and approach</h3><div>This review critically assesses flexible sensor materials, including conductive polymers and carbon-based nanomaterials, as well as their integration with wireless communication systems and energy-harvesting technologies. It also evaluates machine learning-driven artificial intelligence (AI) methodologies for freshness scoring and shelf-life prediction. Practical implementations are reviewed through case studies involving sensor patches, embedded films, logistics optimization, and consumer interfaces.</div></div><div><h3>Key findings and conclusions</h3><div>Flexible sensors enable millimeter-scale monitoring of temperature, humidity, gases, pH, and microbial metabolites, while roll-to-roll printing supports scalable integration into packaging. AI-driven pipelines process multimodal sensor data to generate freshness scores, estimate shelf life, and detect anomalies. These systems employ regression and tree-based models as well as convolutional and recurrent neural networks enhanced by explainable AI techniques. Demonstrations of sensor patches, embedded films, and AI-driven logistics have achieved spoilage reductions. Despite these advances, challenges remained in addressing sensor drift, achieving energy autonomy‒systems capable of operating independently through on-board energy harvesting and management, ensuring material circularity, and meeting regulatory requirements. Future efforts should focus on integrating self-healing materials, improved energy harvesting, biodegradable electronics, and human–computer interaction to realize sustainable autonomous smart packaging systems.</div></div>\",\"PeriodicalId\":441,\"journal\":{\"name\":\"Trends in Food Science & Technology\",\"volume\":\"164 \",\"pages\":\"Article 105254\"},\"PeriodicalIF\":15.4000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Food Science & Technology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924224425003905\",\"RegionNum\":1,\"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":"Trends in Food Science & Technology","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924224425003905","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
From passive to self-aware packs: Flexible Sensor-AI integration powering intelligent, sustainable food packaging
Background
The global agri-food sector faces persistent challenges in ensuring food quality, safety, and sustainability. Conventional packaging functions as a passive barrier, incapable of monitoring physicochemical or microbiological changes in real time. This limitation necessitates reliance on conservative expiration dates, thereby contributing to significant food waste. Smart interactive packaging addresses these issues by detecting and communicating the dynamic conditions of food products.
Scope and approach
This review critically assesses flexible sensor materials, including conductive polymers and carbon-based nanomaterials, as well as their integration with wireless communication systems and energy-harvesting technologies. It also evaluates machine learning-driven artificial intelligence (AI) methodologies for freshness scoring and shelf-life prediction. Practical implementations are reviewed through case studies involving sensor patches, embedded films, logistics optimization, and consumer interfaces.
Key findings and conclusions
Flexible sensors enable millimeter-scale monitoring of temperature, humidity, gases, pH, and microbial metabolites, while roll-to-roll printing supports scalable integration into packaging. AI-driven pipelines process multimodal sensor data to generate freshness scores, estimate shelf life, and detect anomalies. These systems employ regression and tree-based models as well as convolutional and recurrent neural networks enhanced by explainable AI techniques. Demonstrations of sensor patches, embedded films, and AI-driven logistics have achieved spoilage reductions. Despite these advances, challenges remained in addressing sensor drift, achieving energy autonomy‒systems capable of operating independently through on-board energy harvesting and management, ensuring material circularity, and meeting regulatory requirements. Future efforts should focus on integrating self-healing materials, improved energy harvesting, biodegradable electronics, and human–computer interaction to realize sustainable autonomous smart packaging systems.
期刊介绍:
Trends in Food Science & Technology is a prestigious international journal that specializes in peer-reviewed articles covering the latest advancements in technology, food science, and human nutrition. It serves as a bridge between specialized primary journals and general trade magazines, providing readable and scientifically rigorous reviews and commentaries on current research developments and their potential applications in the food industry.
Unlike traditional journals, Trends in Food Science & Technology does not publish original research papers. Instead, it focuses on critical and comprehensive reviews to offer valuable insights for professionals in the field. By bringing together cutting-edge research and industry applications, this journal plays a vital role in disseminating knowledge and facilitating advancements in the food science and technology sector.