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高通量方法用于植物源食品和尿液样品的广泛覆盖和定量酚类指纹图谱分析。

High-Throughput Method for Wide-Coverage and Quantitative Phenolic Fingerprinting in Plant-Origin Foods and Urine Samples.

机构信息

Agrifood Laboratory, Faculty of Experimental Sciences, University of Huelva, 21007 Huelva, Spain.

International Campus of Excellence CeiA3, University of Huelva, 21007 Huelva, Spain.

出版信息

J Agric Food Chem. 2022 Jun 29;70(25):7796-7804. doi: 10.1021/acs.jafc.2c01453. Epub 2022 Jun 15.

Abstract

The use of mass spectrometry is currently widespread in polyphenol research because of its sensitivity and selectivity, but its usual high cost, reduced robustness, and nonavailability in many analytical laboratories considerably hinder its routine implementation. Herein, we describe the optimization and validation of a high-throughput, wide-coverage, and robust metabolomics method based on reversed-phase ultra-high-performance liquid chromatography with diode array detection for the identification and quantification of 69 phenolic compounds and related metabolites covering a broad chemical space of the characteristic secondary metabolome of plant foods. The method was satisfactorily validated following the Food and Drug Administration guidelines in terms of linearity (4-5 orders of magnitude), limits of quantification (0.007-3.6 mg L), matrix effect (60.5-124.4%), accuracy (63.4-126.7%), intraday precision (0.1-9.6%), interday precision (0.6-13.7%), specificity, and carryover. Then, it was successfully applied to characterize the phenolic fingerprints of diverse food products (i.e., olive oil, red wine, strawberry) and biological samples (i.e., urine), enabling not only the detection of many of the target compounds but also the semi-quantification of other phenolic metabolites tentatively identified based on their characteristic absorption spectra. Therefore, this method represents one step further toward time-efficient and low-cost polyphenol fingerprinting, with suitable applicability in the food industry to ensure food quality, safety, authenticity, and traceability.

摘要

基于反相超高效液相色谱-二极管阵列检测的高通量、广覆盖、稳健代谢组学方法用于鉴定和定量 69 种酚类化合物及相关代谢物

质谱法由于其灵敏度和选择性,目前在多酚研究中得到广泛应用,但由于其通常成本高、稳健性降低且在许多分析实验室中不可用,因此严重阻碍了其常规应用。本文描述了一种基于反相超高效液相色谱-二极管阵列检测的高通量、广覆盖、稳健代谢组学方法的优化和验证,用于鉴定和定量 69 种酚类化合物及相关代谢物,涵盖了植物性食物特征次生代谢物的广泛化学空间。该方法按照美国食品和药物管理局的指导原则进行了令人满意的验证,包括线性度(4-5 个数量级)、定量限(0.007-3.6mg/L)、基质效应(60.5-124.4%)、准确度(63.4-126.7%)、日内精密度(0.1-9.6%)、日间精密度(0.6-13.7%)、特异性和拖尾。然后,它成功地应用于描述各种食品产品(即橄榄油、红酒、草莓)和生物样品(即尿液)的酚指纹图谱,不仅能够检测到许多目标化合物,还能够对基于特征吸收光谱初步鉴定的其他酚类代谢物进行半定量分析。因此,该方法朝着高效、低成本的多酚指纹分析又迈进了一步,在食品工业中具有适当的适用性,可确保食品质量、安全、真实性和可追溯性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0148/10550202/644347aacac4/jf2c01453_0002.jpg

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