Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/57287
Type: Artigo de periódico
Title: Correlation of quantitative sensorial descriptors and chromatographic signals of beer using multivariate calibration strategies
Author: da Silva, GA
Maretto, DA
Bolini, HMA
Teofilo, RF
Augusto, F
Poppi, RJ
Abstract: In this study, two important sensorial parameters of beer quality - bitterness and grain taste - were correlated with data obtained after headspace solid phase microextraction - gas chromatography with mass spectrometric detection (HS-SPME-GC-MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME-GC-MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively: while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists. (C) 2012 Elsevier Ltd. All rights reserved.
Subject: Beer
Solid phase microextraction
Gas chromatography
Sensorial analysis
Quantitative descriptive analyses
Genetic algorithm
Ordered predictors selection
Country: Inglaterra
Editor: Elsevier Sci Ltd
Rights: fechado
Identifier DOI: 10.1016/j.foodchem.2012.03.080
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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