集成人工智能和先进的光谱技术,用于精确的食品安全和质量控制

IF 15.4 1区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY Trends in Food Science & Technology Pub Date : 2025-02-01 Epub Date: 2024-12-20 DOI:10.1016/j.tifs.2024.104850
Imane Ziani , Hamza Bouakline , Abdelqader El Guerraf , Ali El Bachiri , Marie-Laure Fauconnier , Farooq Sher
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引用次数: 0

摘要

传统的高效液相色谱(HPLC)和气相色谱-质谱(GC-MS)等方法在食品分析中得到了广泛的应用,但在检测超低水平或复杂基质中的痕量污染物时往往面临局限性。本综述重点介绍了食品分析技术的最新突破,这些技术为保护消费者健康提供了前所未有的灵敏度和准确性。在这些进步中,宽线表面增强拉曼散射(WL-SERS)将灵敏度提高了十倍,能够在远低于常规阈值的浓度下检测原料牛奶中的三聚氰胺等污染物。质谱成像(MSI),特别是基质辅助激光解吸/电离(MALDI-MSI),在空间分辨率方面取得了重大进展,可以精确绘制食品成分和污染物。此外,二维液相色谱法(2D-LC)和多维气相色谱法发展迅速,在复杂的食品系统中可以实现低至1 ppb的检测。创新的传感器技术,如Dpyt近红外(NIR)荧光探针和电化学发光(ECL)适体传感器,提供快速和高灵敏度的检测,有效地补充了传统方法。此外,人工智能(AI)和机器学习(ML)的整合已经彻底改变了食品质量评估,卷积神经网络(cnn)等模型在识别掺假方面的准确率高达99.85%。尽管取得了这些进步,但诸如高运营成本、传感器稳定性和人工智能计算需求等挑战仍然存在。这篇综述强调了先进光谱学、人工智能驱动分析和新型传感器技术的整合,概述了未来的战略,如小型化、纳米材料创新和标准化协议。这些方法为提高食品安全和质量管理的准确性、效率和可及性提供了变革性途径,最终加强了对公众健康的保护。
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Integrating AI and advanced spectroscopic techniques for precision food safety and quality control
Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) are widely used in food analysis but often face limitations in detecting trace contaminants at ultra-low levels or in complex matrices. This review highlights recent breakthroughs in food analysis technologies that deliver unprecedented sensitivity and accuracy for consumers' health protection. Among these advances, Wide Line Surface-Enhanced Raman scattering (WL-SERS) has delivered a tenfold increase in sensitivity, enabling the detection of contaminants like melamine in raw milk at concentrations far below conventional thresholds. Mass spectrometry imaging (MSI), particularly matrix-assisted laser desorption/ionization (MALDI-MSI), has made significant progress in spatial resolution, allowing for precise mapping of food constituents and contaminants. Additionally, two-dimensional liquid chromatography (2D-LC) and multidimensional gas chromatography have evolved rapidly, achieving detection as low as 1 ppb in complex food systems. Innovative sensor technologies, such as the Dpyt near-infrared (NIR) fluorescent probe and electrochemiluminescence (ECL) aptasensors, offer rapid and highly sensitive detection, effectively complementing traditional methods. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized food quality assessment, with models like convolutional neural networks (CNNs) reaching up to 99.85% accuracy in identifying adulterants. Despite these advancements, challenges such as high operational costs, sensor stability and AI's computational demands remain. This review highlights the integration of advanced spectroscopy, AI-driven analysis, and novel sensor technologies, outlining future strategies such as miniaturization, nanomaterial innovations, and standardized protocols. These approaches present transformative pathways for improving the precision, efficiency, and accessibility of food safety and quality management, ultimately enhancing public health protection.
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来源期刊
Trends in Food Science & Technology
Trends in Food Science & Technology 工程技术-食品科技
CiteScore
32.50
自引率
2.60%
发文量
322
审稿时长
37 days
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.
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