Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/354571
Type: Artigo
Title: Multivariate optimization by statistical methods of ultra high performance liquid chromatography conditions for the separation of 17 capsaicinoids
Author: Coutinho, Janclei Pereira
Barbero, Gerardo Fernández
Godoy, Helena Teixeira
Palma, Miguel
Barroso, Carmelo García
Abstract: In this work, multivariate optimization by statistical methods (Derringer and Suich optimization) was proposed in order to find the optimum conditions of an Ultra High Performance Liquid Chromatograph with Diode Array Detection (UHPLCDAD) for the separation of seventeen capsaicinoids (natural and synthetic). Capsaicinoids were analyzed at 280 nm. The variables optimized were the mobile phase (water (0.1% acetic acid as solvent A) and acetonitrile (0.1% as solvent B)), gradient time and flow rate. Two columns with different lengths (50 and 100 mm) were used for the chromatographic separation. The two columns used properly separated fifteen of the seventeen capsaicinoids, but capsaicin (C) and N-(4-hydroxy-3-methoxybenzyl) nonanamide (N9C) could not be separated. However the 50 mm column length showed a better chromatographic separation with a shorter run time and smaller peak widths. These results provided better values of limits of detection and quantification for the 50 mm column length. The better conditions of separation with the 50 mm column length were established with: initial mobile phase with 0% of solvent B; 8.12 minutes of the linear gradient time to reach 100% of solvent B; a flow rate of 0.8 mL min−1. A validation of the method has been done with good values of repeatability (RSD < 1.92) and intermediate precision (RSD < 3.92). The developed method has been applied to real food samples. Capsaicin and dihydrocapsaicin have been identified and quantified in all of the spicy foods analyzed
Subject: Capsaicinóides
Cromatografia líquida
Country: Reino Unido
Editor: Royal Society of Chemistry
Rights: Aberto
Identifier DOI: 10.1039/c5ay03211c
Address: https://pubs.rsc.org/en/content/articlelanding/2016/AY/C5AY03211C
Date Issue: 2016
Appears in Collections:FEA - Artigos e Outros Documentos

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