数字农科院2.0

Recent advancements in chemometrics based non-destructive analytical techniques for rapid detection of adulterants in milk and dairy products – A review

文献类型： 外文期刊

作者： Rui Xu;Muhammad Zeeshan Adil;Sidra Jabeen; Khansa;Mahwish Tanveer;Sadia Younis;Bakhtawar Shafique;Long Li

作者机构：

关键词： Chemometrics;Dairy products;Imaging techniques;Milk adulteration;Non-destructive analytical techniques;Rapid detection;Spectroscopic techniques

期刊名称： Food Control

ISSN： 0956-7135

年卷期： 2025 年 174 卷

页码：

收录情况： SCIE（2025版）

摘要： Adulteration of milk and dairy products is considered a deceptive practice for economic gain and poses serious health risks. Conventional techniques, such as chromatography, spectrofluorimetry, immunological methods, and DNA-based procedures, have limitations in terms of complexity, cost, and technical expertise requirements. Non-destructive spectroscopic and imaging techniques have emerged as promising alternatives for the rapid, specific, and sensitive detection of adulterants. Fluorescence spectroscopy, surface-enhanced Raman spectroscopy, near-infrared spectroscopy, mid-infrared spectroscopy, laser-induced breakdown spectroscopy, terahertz spectroscopy, photoacoustic spectroscopy, and nuclear magnetic resonance spectroscopy have all demonstrated their potential for milk analysis and monitoring. Imaging techniques such as hyperspectral imaging, multispectral imaging, Raman imaging, and terahertz imaging provide advantages in terms of sensitivity, specificity, cost-effectiveness, and reliability. However, spectral data include a large number of complex spectral features and peaks, making the direct analysis of spectral data challenging. It is essential to construct quantitative models using chemometrics to identify specific components of dairy products. Chemometrics coupled with these techniques have played a crucial role in expediting adulterant detection and ensuring the traceability and authenticity of dairy products. The additional benefits of employing chemometric models include taxonomic research, counterfeit product detection, process monitoring, geographical origin assessment, and quality control. Exploratory data analysis, classification, discrimination, prediction, and regression methods are commonly used, with multivariate classification models leveraging chemometrics to extract diverse information from spectra for sample classification and differentiation. This review highlights advancements in non-destructive analytical techniques coupled with chemometric modeling for the rapid detection of milk and dairy product adulteration, emphasizing the importance of these methods to ensure food safety and quality, and to facilitate the development of real-time, on-site monitoring platforms.

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