Mass spectrometry-based techniques for identification of compounds in milk and meat matrix.

Q1 Agricultural and Biological Sciences Advances in Food and Nutrition Research Pub Date : 2023-01-01 DOI:10.1016/bs.afnr.2022.10.004
Heena Sharma, Fatih Ozogul
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Abstract

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

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基于质谱的牛奶和肉类基质中化合物鉴定技术。
包括牛奶和肉类在内的食物通常被视为不同成分的混合物,如脂肪、蛋白质、碳水化合物、水分和灰分,这些成分是使用成熟的方案和技术进行估计的。然而,随着代谢组学的出现,低分子量物质也被称为代谢物,已被认为是影响生产、质量和加工的主要因素之一。因此,不同的分离和检测技术已经被开发出来,用于快速、稳健和可重复的分离和鉴定化合物,以有效控制牛奶和肉类生产和供应链。基于质谱的技术,如GC-MS和LC-MS以及核磁共振波谱技术,由于其相关的好处,在详细的食品成分分析中已被证明是成功的。不同的代谢物提取方案,衍生化,光谱生成,数据处理和数据解释是这些分析技术的主要顺序步骤。本章不仅详细讨论了这些分析技术,而且还阐明了这些分析技术在牛奶和肉制品中的各种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Food and Nutrition Research
Advances in Food and Nutrition Research Agricultural and Biological Sciences-Food Science
CiteScore
8.50
自引率
0.00%
发文量
50
期刊最新文献
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