Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Ziani, Imane; Bouakline, Hamza; El Guerraf, Abdelqader; El Bachiri, Ali; Fauconnier, Marie-Laure; Sher, Farooq

doi:10.1016/j.tifs.2024.104850

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Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Ziani, Imane; Bouakline, Hamza; El Guerraf, Abdelqader et al.

2025 • In Trends in Food Science and Technology, 156, p. 104850

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10.1016/j.tifs.2024.104850

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Keywords :

Food safety technologies; spectroscopic advancement; Mass spectrometry; sensor innovation

Abstract :

[en] 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.

Disciplines :

Food science
Chemistry

Author, co-author :

Ziani, Imane

Bouakline, Hamza

El Guerraf, Abdelqader

El Bachiri, Ali

Fauconnier, Marie-Laure ; Université de Liège - ULiège > TERRA Research Centre > Chemistry for Sustainable Food and Environmental Systems (CSFES)

Sher, Farooq

Language :

English

Title :

Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

Publication date :

February 2025

Journal title :

Trends in Food Science and Technology

ISSN :

0924-2244

eISSN :

1879-3053

Publisher :

Elsevier BV

Volume :

156

Pages :

104850

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0924224424005260?httpAccept=text/xml

Available on ORBi :

since 02 January 2025

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