Process Biotechnology
  Biotechnology Industry
Electronic Journal of Biotechnology ISSN: 0717-3458 Vol. 13 No. 4, Issue of July 15, 2010
© 2010 by Pontificia Universidad Católica de Valparaíso -- Chile Received September 3, 2009 / Accepted March 12, 2010
DOI: 10.2225/vol13-issue4-fulltext-11
RESEARCH ARTICLE

Identification of Saccharomyces cerevisiae strains for alcoholic fermentation by discriminant factorial analysis on electronic nose signals

Montserrat Calderon-Santoyo
Laboratorio de Investigación Integral en Alimentos
Instituto Tecnológico de Tepic
Tepic, Nayarit. Mexico 

Pascale Chalier
UMR 1208 Ingénierie des Agropolymères et des Technologies Emergentes
Université Montpellier 2
Place E. Bataillon, Montpellier, France Cedex 5 

Dominique Chevalier-Lucia
UMR 1208 Ingénierie des Agropolymères et des Technologies Emergentes
Université Montpellier 2
Place E. Bataillon, Montpellier, France Cedex 5 

Charles Ghommidh
UMR Démarche intégrée pour l'obtention d'aliments de qualité
Université Montpellier 2
Place E. Bataillon, Montpellier, France Cedex 5 

Juan Arturo Ragazzo-Sanchez*
Laboratorio de Investigación Integral en Alimentos
Instituto Tecnológico de Tepic
Tepic, Nayarit. Mexico
E-mail: arturoragazzo@hotmail.com

*Corresponding author

Keywords: alcoholic fermentation, discrimination, electronic nose, gas sensor, yeasts.

Abbreviations:

DFA: Discriminant factorial analysis
E-nose: electronic nose
LDA: linear discriminant analysis
MOS: Metal Oxide Semiconducting
MS: mass-spectroscopy
NN: neural network
PCA: Principal Component Analysis
PCs: Principal Components

Abstract   Full Text

An electronic nose (E-nose) coupled to gas chromatography was tested to monitor alcoholic fermentation by Saccharomyces cerevisiae ICV-K1 and Saccharomyces cerevisiae T306, two strains well-known for their use in oenology. The biomass and ethanol concentrations and conductance changes were measured during cultivations and allowed to observe the standard growth phases for both yeast strains. The two strains were characterized by a very similar tendency in biomass or ethanol production during the fermentation. E-nose was able to establish a kinetic of the production of aroma compounds production and which was then easy to associate with the fermentation phases. Principal Component Analysis (PCA) showed that the data collected by E-nose during the fermentation mainly contained cultivation course information. Discriminant factorial analysis (DFA) was able to clearly identify differences between the two strains using the four main principal components of PCA as input data. Nevertheless, the electronic nose responses being mainly influenced by cultivation course, a specific data treatment limiting the time influence on data was carried out and permitted to achieve an overall performance of 83.5%.

Supported by UNESCO / MIRCEN network