Jun Cheng, Yao Shen, Yulu Gu, Tongyue Xiang, Hui Shen, Yi Wang, Zhenyang Hu, Zhen Zheng, Zhilong Yu, Qin Wu, Yinghui Wang, Tiancong Zhao, Yunfei Xie
{"title":"金属-多酚多级竞争配位比色法监测肉类新鲜度","authors":"Jun Cheng, Yao Shen, Yulu Gu, Tongyue Xiang, Hui Shen, Yi Wang, Zhenyang Hu, Zhen Zheng, Zhilong Yu, Qin Wu, Yinghui Wang, Tiancong Zhao, Yunfei Xie","doi":"10.1002/adma.202503246","DOIUrl":null,"url":null,"abstract":"<p>A low-cost, high-precision, and secure real-time system for monitoring food freshness can significantly improve spoilage issues, yet traditional colorimetric sensor arrays often suffer from chemical dyes’ high toxicity and limited color changes. Here, a metal-polyphenol network colorimetric sensor array (MPN-CSA) is built for detecting total volatile base nitrogen (TVB-N) markers of meat freshness. The multi-level competitive coordination process between the metal-polyphenol system and amine substances endows the system with color changes far beyond those of traditional dyes (reaching a detection limit of 300 ppb). By integrating convolutional neural network (CNN) technology, an online platform is developed for monitoring meat freshness, achieving an overall detection accuracy rate of 99.83%. This environmentally friendly, economically viable MPN-CSA that monitors the freshness of meat in complex storage environments can be incorporated into food packaging boxes, enabling consumers and suppliers to assess the freshness of meat in real-time, thus helping to reduce food waste and prevent foodborne illnesses.</p>","PeriodicalId":114,"journal":{"name":"Advanced Materials","volume":"37 21","pages":""},"PeriodicalIF":26.8000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metal-polyphenol Multistage Competitive Coordination System for Colorimetric Monitoring Meat Freshness\",\"authors\":\"Jun Cheng, Yao Shen, Yulu Gu, Tongyue Xiang, Hui Shen, Yi Wang, Zhenyang Hu, Zhen Zheng, Zhilong Yu, Qin Wu, Yinghui Wang, Tiancong Zhao, Yunfei Xie\",\"doi\":\"10.1002/adma.202503246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A low-cost, high-precision, and secure real-time system for monitoring food freshness can significantly improve spoilage issues, yet traditional colorimetric sensor arrays often suffer from chemical dyes’ high toxicity and limited color changes. Here, a metal-polyphenol network colorimetric sensor array (MPN-CSA) is built for detecting total volatile base nitrogen (TVB-N) markers of meat freshness. The multi-level competitive coordination process between the metal-polyphenol system and amine substances endows the system with color changes far beyond those of traditional dyes (reaching a detection limit of 300 ppb). By integrating convolutional neural network (CNN) technology, an online platform is developed for monitoring meat freshness, achieving an overall detection accuracy rate of 99.83%. This environmentally friendly, economically viable MPN-CSA that monitors the freshness of meat in complex storage environments can be incorporated into food packaging boxes, enabling consumers and suppliers to assess the freshness of meat in real-time, thus helping to reduce food waste and prevent foodborne illnesses.</p>\",\"PeriodicalId\":114,\"journal\":{\"name\":\"Advanced Materials\",\"volume\":\"37 21\",\"pages\":\"\"},\"PeriodicalIF\":26.8000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202503246\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Materials","FirstCategoryId":"88","ListUrlMain":"https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202503246","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Metal-polyphenol Multistage Competitive Coordination System for Colorimetric Monitoring Meat Freshness
A low-cost, high-precision, and secure real-time system for monitoring food freshness can significantly improve spoilage issues, yet traditional colorimetric sensor arrays often suffer from chemical dyes’ high toxicity and limited color changes. Here, a metal-polyphenol network colorimetric sensor array (MPN-CSA) is built for detecting total volatile base nitrogen (TVB-N) markers of meat freshness. The multi-level competitive coordination process between the metal-polyphenol system and amine substances endows the system with color changes far beyond those of traditional dyes (reaching a detection limit of 300 ppb). By integrating convolutional neural network (CNN) technology, an online platform is developed for monitoring meat freshness, achieving an overall detection accuracy rate of 99.83%. This environmentally friendly, economically viable MPN-CSA that monitors the freshness of meat in complex storage environments can be incorporated into food packaging boxes, enabling consumers and suppliers to assess the freshness of meat in real-time, thus helping to reduce food waste and prevent foodborne illnesses.
期刊介绍:
Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.