Using an electronic nose and volatilome analysis to differentiate sparkling wines obtained under different conditions of temperature, ageing time and yeast formats

•Volatile markers were selected related to temperature, aging and yeast format.•E-nose is revealed useful in quality control of sparkling wine.•PCA from both datasets highlights differences by temperature, aging and format.•PLSDA on E-nose dataset allowed best success rate for aging and format model...

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Bibliographic Details
Published in:Food chemistry 2021-01, Vol.334, p.127574-127574, Article 127574
Main Authors: Martínez-García, Rafael, Moreno, Juan, Bellincontro, Andrea, Centioni, Luna, Puig-Pujol, Anna, Peinado, Rafael A., Mauricio, Juan Carlos, García-Martínez, Teresa
Format: Article
Language:English
Subjects:
Biocapsules
Chemometric
Electronic Nose
Quartz microbalance sensors
Saccharomyces cerevisiae - metabolism
Sparkling wine
Temperature
Time Factors
Volatile Organic Compounds - analysis
Volatilome
Wine - analysis
Yeast immobilization
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Summary:•Volatile markers were selected related to temperature, aging and yeast format.•E-nose is revealed useful in quality control of sparkling wine.•PCA from both datasets highlights differences by temperature, aging and format.•PLSDA on E-nose dataset allowed best success rate for aging and format models.•Aroma profiles based on Odorant series show differences due to the factors. Effect of yeast inoculation format (F), temperature (T), and “on lees” ageing time (t) factors were evaluated on the composition of sparkling wines by a quantitative fingerprint obtained from volatile metabolites and the response of an electronic nose (E-nose). Wines elaborated according the traditional method at 10 and 14 °C, free cells and yeast biocapsules formats were monitored at 15 and 24 months of ageing time. Sixty-six volatiles identified and quantified in the eight sampling lots were subjected to a pattern recognition technique. A dual criterion based on univariate (ANOVA) and multivariate analysis (PLS-DA) through the variable importance projection (VIP) values, allowed to identify ten volatiles as potential markers for T factor, eleven for t and twelve for F factors. The discriminant models based on E-nose dataset enable a 100% correct classification of samples, in relation with t and F factors and the 83% for T factor.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2020.127574