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结合化学计量学的食品和功能食品质量控制分析的紫外指纹图谱方法:新趋势与应用的综合分析

UV Fingerprinting Approaches for Quality Control Analyses of Food and Functional Food Coupled to Chemometrics: A Comprehensive Analysis of Novel Trends and Applications.

作者信息

Farag Mohamed A, Sheashea Mohamed, Zhao Chao, Maamoun Amal A

机构信息

Pharmacognosy Department, College of Pharmacy, Cairo University, Kasr el Aini St., Cairo P.B. 11562, Egypt.

Aromatic and Medicinal Plants Department, Desert Research Center, Cairo P.B. 11435, Egypt.

出版信息

Foods. 2022 Sep 16;11(18):2867. doi: 10.3390/foods11182867.

DOI:10.3390/foods11182867
PMID:36140995
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9498431/
Abstract

(1) Background: Ultraviolet-visible (UV-Vis) spectroscopy is a common analytical tool to detect chromophore in compounds by monitoring absorbance spectral wavelengths. Further, it could provide spectral information about complex conjugated systems in mixtures aided by chemometric tools to visualize large UV-Vis datasets as typical in food samples. This review provides novel insight on UV-Vis applications in the last 20 years, as an advanced analytical tool in the quality control of food and dietary supplements, as well as several other applications, including chemotaxonomy, authentication, fingerprinting, and stability studies. (2) Conclusions: A critical assessment of the value of UV application and its novel trends in the quality control (QC) of nutraceuticals reveals the advantages and limitations, focusing on areas where future advancements are in need. Although simple, UV and its novel trends present potential analytical tools with an acceptable error for QC applications from a non-targeted perspective compared to other expensive spectral tools.

摘要

(1) 背景:紫外可见(UV-Vis)光谱法是一种常见的分析工具,通过监测吸收光谱波长来检测化合物中的发色团。此外,借助化学计量工具,它可以提供有关混合物中复杂共轭体系的光谱信息,从而将大型UV-Vis数据集可视化,这在食品样品中很常见。本综述对UV-Vis在过去20年中的应用提供了新的见解,作为食品和膳食补充剂质量控制中的一种先进分析工具,以及其他一些应用,包括化学分类学、鉴定、指纹识别和稳定性研究。(2) 结论:对UV应用的价值及其在营养保健品质量控制(QC)中的新趋势进行的批判性评估揭示了其优点和局限性,重点关注未来需要改进的领域。尽管UV及其新趋势很简单,但与其他昂贵的光谱工具相比,从非靶向角度来看,它们为QC应用提供了具有可接受误差的潜在分析工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/8dcfc2e0f630/foods-11-02867-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/2f5990344c3e/foods-11-02867-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/bcc564ace797/foods-11-02867-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/8dcfc2e0f630/foods-11-02867-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/2f5990344c3e/foods-11-02867-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/bcc564ace797/foods-11-02867-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3ed/9498431/8dcfc2e0f630/foods-11-02867-g003.jpg

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