4.7 Review

Carboxyl-containing compounds in food: Category, functions, and analysis with chemical derivatization-based LC-MS

期刊

TRAC-TRENDS IN ANALYTICAL CHEMISTRY
卷 157, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2022.116818

关键词

Nutrients; Chemical derivatization; Biological functions; LC-MS

资金

  1. Science and Technology Development Fund, Macau SAR
  2. [FDCT 0025/2021/A1]
  3. [0044/2018/AFJ]

向作者/读者索取更多资源

Carboxyl-containing compounds (CCCs) are crucial for the human body and their determination in food is challenging. Derivatization LC-MS technique plays a significant role in overcoming these challenges in food chemistry. This review comprehensively discusses CCCs in terms of structures, biological functions, and food sources, highlighting the importance of derivatization LC-MS as a powerful analytical tool.
Carboxyl-containing compounds (CCCs) are indispensable for human body and related to the manage-ment of numerous diseases. However, it is still rather challenging to determine them in food both qualitatively and quantitatively because of their similar or diverse structures and the difference of po-larity, concentration, ionization efficiency, and separation ability. Fortunately, derivatization-LC-MS can overcome such limitation and has been playing a more important role in food chemistry for recent decades. There have been no systematic reviews about CCCs together in food, thus CCCs were grouped and discussed comprehensively in terms of structures, biological functions, and abundant food sources. In addition, how derivatization LC-MS technique can provide increased detection sensitivity, improved chromatographic separation, improved identification capacity, and accurate quantitative capacity are summarized. Herein we aim to emphasize various functional CCCs in food and identify derivatization LC -MS as a powerful analytical tool to analyze food samples.(c) 2022 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据