Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractTraceability of honey origin based on volatiles pattern processing by artificial neural networks    Next AbstractPlanar Indium Tin Oxide Heater for Improved Thermal Distribution for Metal Oxide Micromachined Gas Sensors »

J Chromatogr A


Title:Recognition of beer brand based on multivariate analysis of volatile fingerprint
Author(s):Cajka T; Riddellova K; Tomaniova M; Hajslova J;
Address:"Institute of Chemical Technology, Prague, Faculty of Food and Biochemical Technology, Department of Food Chemistry and Analysis, Technicka 3, 16628 Prague 6, Czech Republic"
Journal Title:J Chromatogr A
Year:2010
Volume:20100104
Issue:25
Page Number:4195 - 4203
DOI: 10.1016/j.chroma.2009.12.049
ISSN/ISBN:1873-3778 (Electronic) 0021-9673 (Linking)
Abstract:"Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models 'Trappist vs. non-Trappist beers' with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and 'Rochefort 8 vs. the rest' with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach"
Keywords:Beer/*analysis Multivariate Analysis Quality Control Volatile Organic Compounds/*analysis;
Notes:"MedlineCajka, Tomas Riddellova, Katerina Tomaniova, Monika Hajslova, Jana eng Research Support, Non-U.S. Gov't Netherlands 2010/01/16 J Chromatogr A. 2010 Jun 18; 1217(25):4195-203. doi: 10.1016/j.chroma.2009.12.049. Epub 2010 Jan 4"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024