Artificial intelligence-driven detection of microplastics in food: A comprehensive review of sources, health risks, detection techniques, and emerging artificial intelligence solutions

IF 6.5 1区农林科学 Q1 CHEMISTRY, APPLIED

Food Chemistry: X Pub Date : 2025-06-24 DOI:10.1016/j.fochx.2025.102687

Himani Rawat , Ashish Gaur , Narpinder Singh , Manickam Selvaraj , Arun Karnwal , Gaurav Pant , Tabarak Malik

{"title":"Artificial intelligence-driven detection of microplastics in food: A comprehensive review of sources, health risks, detection techniques, and emerging artificial intelligence solutions","authors":"Himani Rawat , Ashish Gaur , Narpinder Singh , Manickam Selvaraj , Arun Karnwal , Gaurav Pant , Tabarak Malik","doi":"10.1016/j.fochx.2025.102687","DOIUrl":null,"url":null,"abstract":"<div><div>Microplastic contamination in food is an escalating concern due to associated environmental and health risks, with a rising global plastic production projected to exceed 2.1 billion tons annually by 2060. This makes it essential to have effective detection and identification of microplastics for determining environmental risk and secure food safety. This study is an effort to compare conventional methods (optical detection, thermo-analytical, hyperspectral imaging) with advanced techniques (Fourier transform infrared spectroscopy, pyrolysis-gas chromatography–mass spectrometry, Raman spectroscopy) in the detection of microplastics in food. While conventional methods are effective enough in providing qualitative insights, advanced techniques provide superior sensitivity and specificity for the detection of smaller particles. The article analyses the advantages and limits of these methods, considering factors such as accuracy, cost, sensitivity, and efficiency. It also analyses the basic advantages of artificial intelligence in addressing these limitations. Artificial intelligence's speed, accuracy, and adaptability can enhance microplastic detection and identification, supporting regulatory compliance and food safety monitoring. This comprehensive analysis addresses artificial intelligence's vital role as a future research tool to the rising challenges of microplastic contamination.</div></div>","PeriodicalId":12334,"journal":{"name":"Food Chemistry: X","volume":"29 ","pages":"Article 102687"},"PeriodicalIF":6.5000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry: X","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590157525005346","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}

引用次数: 0

Abstract

Microplastic contamination in food is an escalating concern due to associated environmental and health risks, with a rising global plastic production projected to exceed 2.1 billion tons annually by 2060. This makes it essential to have effective detection and identification of microplastics for determining environmental risk and secure food safety. This study is an effort to compare conventional methods (optical detection, thermo-analytical, hyperspectral imaging) with advanced techniques (Fourier transform infrared spectroscopy, pyrolysis-gas chromatography–mass spectrometry, Raman spectroscopy) in the detection of microplastics in food. While conventional methods are effective enough in providing qualitative insights, advanced techniques provide superior sensitivity and specificity for the detection of smaller particles. The article analyses the advantages and limits of these methods, considering factors such as accuracy, cost, sensitivity, and efficiency. It also analyses the basic advantages of artificial intelligence in addressing these limitations. Artificial intelligence's speed, accuracy, and adaptability can enhance microplastic detection and identification, supporting regulatory compliance and food safety monitoring. This comprehensive analysis addresses artificial intelligence's vital role as a future research tool to the rising challenges of microplastic contamination.

查看原文本刊更多论文

食品中微塑料的人工智能驱动检测：对来源、健康风险、检测技术和新兴人工智能解决方案的全面回顾

由于相关的环境和健康风险，食品中的微塑料污染日益引起人们的关注，预计到2060年，全球塑料产量每年将超过21亿吨。因此，必须对微塑料进行有效检测和鉴定，以确定环境风险和确保食品安全。本研究旨在比较传统方法（光学检测、热分析、高光谱成像）与先进技术（傅里叶变换红外光谱、热解-气相色谱-质谱、拉曼光谱）在食品中微塑料检测中的应用。虽然传统方法在提供定性见解方面足够有效，但先进的技术为检测较小的颗粒提供了卓越的灵敏度和特异性。本文从准确性、成本、灵敏度和效率等方面分析了这些方法的优点和局限性。文章还分析了人工智能在解决这些局限性方面的基本优势。人工智能的速度、准确性和适应性可以增强微塑料的检测和识别，支持法规遵从和食品安全监测。这项全面的分析解决了人工智能作为未来研究工具的重要作用，以应对日益严峻的微塑料污染挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Food Chemistry: X CHEMISTRY, APPLIED-

CiteScore

4.90

自引率

6.60%

发文量

315

审稿时长

55 days

期刊介绍： Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.