Your browser doesn't support javascript.
loading
Identification of Saccharomyces cerevisiae strains for alcoholic fermentation by discriminant factorial analysis on electronic nose signals
Calderon-Santoyo, Montserrat; Chalier, Pascale; Chevalier-Lucia, Dominique; Ghommidh, Charles; Ragazzo-Sanchez, Juan Arturo.
  • Calderon-Santoyo, Montserrat; Instituto Tecnológico de Tepic. Laboratorio de Investigación Integral en Alimentos. Tepic. MX
  • Chalier, Pascale; Université Montpellier 2. UMR 1208 Ingénierie des Agropolymères et des Technologies Emergentes. Montpellier. FR
  • Chevalier-Lucia, Dominique; Université Montpellier 2. UMR 1208 Ingénierie des Agropolymères et des Technologies Emergentes. Montpellier. FR
  • Ghommidh, Charles; Université Montpellier 2. UMR Démarche intégrée pour l'obtention d'aliments de qualité. Montpellier. FR
  • Ragazzo-Sanchez, Juan Arturo; Instituto Tecnológico de Tepic. Laboratorio de Investigación Integral en Alimentos. Tepic. MX
Electron. j. biotechnol ; 13(4): 8-9, July 2010. ilus, tab
Article Dans En | LILACS | ID: lil-577113
Responsable en Bibliothèque : CL1.1
ABSTRACT
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 percent.
Sujets)


Texte intégral: 1 Indice: LILACS Sujet Principal: Saccharomyces cerevisiae / Techniques de biocapteur / Chromatographie en phase gazeuse / Alcools / Fermentation / Odorisants Type d'étude: Diagnostic_studies / Prognostic_studies langue: En Texte intégral: Electron. j. biotechnol Thème du journal: BIOTECNOLOGIA Année: 2010 Type: Article

Texte intégral: 1 Indice: LILACS Sujet Principal: Saccharomyces cerevisiae / Techniques de biocapteur / Chromatographie en phase gazeuse / Alcools / Fermentation / Odorisants Type d'étude: Diagnostic_studies / Prognostic_studies langue: En Texte intégral: Electron. j. biotechnol Thème du journal: BIOTECNOLOGIA Année: 2010 Type: Article