Shikha Pandhi, Nilima Kumari, Amit Jain, Vinay Sharma
{"title":"食品安全中的新兴技术:基于人工智能、纳米技术和生物传感器的快速污染物检测策略","authors":"Shikha Pandhi, Nilima Kumari, Amit Jain, Vinay Sharma","doi":"10.1007/s12161-025-02844-5","DOIUrl":null,"url":null,"abstract":"<div><p>Food safety is essential, but conventional detection techniques are slow and ineffective. New technologies such as artificial intelligence (AI), nanotechnology, and biosensors allow fast, accurate, real-time contaminant identification. AI-based machine learning improves food analysis, enhances imaging methods, and combines IoT and blockchain for tracking. Nanotechnology allows ultra-sensitive detection with nanoparticle-based sensors and nanozymes. Biosensors provide specific detection of toxins, pathogens, and contaminants using enzyme-based, immunosensor, and DNA/aptamer-based technologies. Next-generation electrochemical, optical, and paper biosensors increase practical usage. Hybrid concepts such as AI-sensing nanosensors and IoT-based smart packaging have intelligent, automated responses. Challenges of scaling up, regulation, affordability, and sustainability are limitations to widespread adoption. They can be overcome with collaboration, policy structures, and public sensitization. This review aims to explore the latest advancements in food safety technologies, focusing on AI-powered, nano-enabled, and biosensor-based strategies for rapid contaminant detection. It examines how artificial intelligence, nanotechnology, and biosensors transform food safety by enabling the real-time, sensitive, and accurate detection of microbial pathogens, chemical residues, allergens, and other contaminants. Additionally, it analyses recent innovations and effectiveness across different food matrices while addressing challenges such as regulatory concerns, integration barriers, and technological limitations. By providing a comprehensive evaluation of these emerging approaches, this review highlights their potential to enhance food safety monitoring, ensuring improved consumer protection and regulatory compliance.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"18 9","pages":"2010 - 2024"},"PeriodicalIF":3.0000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Emerging Technologies in Food Safety: AI-Powered, Nano-enabled, and Biosensor-Based Strategies for Rapid Contaminant Detection\",\"authors\":\"Shikha Pandhi, Nilima Kumari, Amit Jain, Vinay Sharma\",\"doi\":\"10.1007/s12161-025-02844-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Food safety is essential, but conventional detection techniques are slow and ineffective. New technologies such as artificial intelligence (AI), nanotechnology, and biosensors allow fast, accurate, real-time contaminant identification. AI-based machine learning improves food analysis, enhances imaging methods, and combines IoT and blockchain for tracking. Nanotechnology allows ultra-sensitive detection with nanoparticle-based sensors and nanozymes. Biosensors provide specific detection of toxins, pathogens, and contaminants using enzyme-based, immunosensor, and DNA/aptamer-based technologies. Next-generation electrochemical, optical, and paper biosensors increase practical usage. Hybrid concepts such as AI-sensing nanosensors and IoT-based smart packaging have intelligent, automated responses. Challenges of scaling up, regulation, affordability, and sustainability are limitations to widespread adoption. They can be overcome with collaboration, policy structures, and public sensitization. This review aims to explore the latest advancements in food safety technologies, focusing on AI-powered, nano-enabled, and biosensor-based strategies for rapid contaminant detection. It examines how artificial intelligence, nanotechnology, and biosensors transform food safety by enabling the real-time, sensitive, and accurate detection of microbial pathogens, chemical residues, allergens, and other contaminants. Additionally, it analyses recent innovations and effectiveness across different food matrices while addressing challenges such as regulatory concerns, integration barriers, and technological limitations. By providing a comprehensive evaluation of these emerging approaches, this review highlights their potential to enhance food safety monitoring, ensuring improved consumer protection and regulatory compliance.</p></div>\",\"PeriodicalId\":561,\"journal\":{\"name\":\"Food Analytical Methods\",\"volume\":\"18 9\",\"pages\":\"2010 - 2024\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Analytical Methods\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12161-025-02844-5\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Analytical Methods","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s12161-025-02844-5","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Emerging Technologies in Food Safety: AI-Powered, Nano-enabled, and Biosensor-Based Strategies for Rapid Contaminant Detection
Food safety is essential, but conventional detection techniques are slow and ineffective. New technologies such as artificial intelligence (AI), nanotechnology, and biosensors allow fast, accurate, real-time contaminant identification. AI-based machine learning improves food analysis, enhances imaging methods, and combines IoT and blockchain for tracking. Nanotechnology allows ultra-sensitive detection with nanoparticle-based sensors and nanozymes. Biosensors provide specific detection of toxins, pathogens, and contaminants using enzyme-based, immunosensor, and DNA/aptamer-based technologies. Next-generation electrochemical, optical, and paper biosensors increase practical usage. Hybrid concepts such as AI-sensing nanosensors and IoT-based smart packaging have intelligent, automated responses. Challenges of scaling up, regulation, affordability, and sustainability are limitations to widespread adoption. They can be overcome with collaboration, policy structures, and public sensitization. This review aims to explore the latest advancements in food safety technologies, focusing on AI-powered, nano-enabled, and biosensor-based strategies for rapid contaminant detection. It examines how artificial intelligence, nanotechnology, and biosensors transform food safety by enabling the real-time, sensitive, and accurate detection of microbial pathogens, chemical residues, allergens, and other contaminants. Additionally, it analyses recent innovations and effectiveness across different food matrices while addressing challenges such as regulatory concerns, integration barriers, and technological limitations. By providing a comprehensive evaluation of these emerging approaches, this review highlights their potential to enhance food safety monitoring, ensuring improved consumer protection and regulatory compliance.
期刊介绍:
Food Analytical Methods publishes original articles, review articles, and notes on novel and/or state-of-the-art analytical methods or issues to be solved, as well as significant improvements or interesting applications to existing methods. These include analytical technology and methodology for food microbial contaminants, food chemistry and toxicology, food quality, food authenticity and food traceability. The journal covers fundamental and specific aspects of the development, optimization, and practical implementation in routine laboratories, and validation of food analytical methods for the monitoring of food safety and quality.