2017
DOI: 10.1039/c6ay02937j
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Determination of phenolic compounds in extracts of Amazonian medicinal plants by liquid chromatography-electrospray tandem mass spectrometry

Abstract: A method for the separation, identification and quantification of 24 phenolic compounds using ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) was developed and validated.

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Cited by 15 publications
(8 citation statements)
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“…We used a mobile phase composed of acetonitrile gradients and 0.1% formic acid in water (v/v) which is typical in separating complex samples. [26,27] The developed method can be considered rapid as it allowed the separation of compounds at 254 nm in only 35 min. The optimized method was validated based on ICH guidelines.…”
Section: Discussionmentioning
confidence: 99%
“…We used a mobile phase composed of acetonitrile gradients and 0.1% formic acid in water (v/v) which is typical in separating complex samples. [26,27] The developed method can be considered rapid as it allowed the separation of compounds at 254 nm in only 35 min. The optimized method was validated based on ICH guidelines.…”
Section: Discussionmentioning
confidence: 99%
“…The MS detection was performed on a Qtrap 6500 MS (AB Sciex, Framingham, MA, USA) equipped with an ESI source operating in negative-ion mode. Quantification of 9 metabolites was carried out in the MRM mode [ 30 , 31 ]. At the same time, optimization to voltage (DP), cluster collision (CE) and mass spectrum parameters, such as the optimal conditions for 9 types of compound ion pairs for rapid screening, was performed.…”
Section: Methodsmentioning
confidence: 99%
“…In the Amazon region considered the most biodiverse region of the planet , numerous plant species have been the subject of phytochemical studies aimed at identifying their mineral composition, the concentration rate of primary and secondary metabolites, and their activities. In this context, several scientific articles have been published with these objectives (FACCIN et al, 2017;SILVEIRA et al, 2015;RAMOS et al, 2017).…”
Section: Capítulo 60mentioning
confidence: 99%