Current Sample Preparation Methodologies for Determination of Catecholamines and Their Metabolites

当前用于测定儿茶酚胺及其代谢物的样品制备方法

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作者:Nian Shi, Xinmiao Bu, Manyu Zhang, Bin Wang, Xinli Xu, Xuezhong Shi, Dilshad Hussain, Xia Xu, Di Chen

Abstract

Catecholamines (CAs) and their metabolites play significant roles in many physiological processes. Changes in CAs concentration in vivo can serve as potential indicators for the diagnosis of several diseases such as pheochromocytoma and paraganglioma. Thus, the accurate quantification of CAs and their metabolites in biological samples is quite important and has attracted great research interest. However, due to their extremely low concentrations and numerous co-existing biological interferences, direct analysis of these endogenous compounds often suffers from severe difficulties. Employing suitable sample preparation techniques before instrument detection to enrich the target analytes and remove the interferences is a practicable and straightforward approach. To date, many sample preparation techniques such as solid-phase extraction (SPE), and liquid-liquid extraction (LLE) have been utilized to extract CAs and their metabolites from various biological samples. More recently, several modern techniques such as solid-phase microextraction (SPME), liquid-liquid microextraction (LLME), dispersive solid-phase extraction (DSPE), and chemical derivatizations have also been used with certain advanced features of automation and miniaturization. There are no review articles with the emphasis on sample preparations for the determination of catecholamine neurotransmitters in biological samples. Thus, this review aims to summarize recent progress and advances from 2015 to 2021, with emphasis on the sample preparation techniques combined with separation-based detection methods such capillary electrophoresis (CE) or liquid chromatography (LC) with various detectors. The current review manuscript would be helpful for the researchers with their research interests in diagnostic analysis and biological systems to choose suitable sample pretreatment and detection methods.

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