Differentiation through E‐nose and GC‐FID data modeling of rosé sparkling wines elaborated via traditional and Charmat methods

Author:

Muñoz‐Castells Raquel1,Modesti Margherita2ORCID,Moreno‐García Jaime1ORCID,Rodríguez‐Moreno María1,Catini Alexandro3,Capuano Rosamaria3,Di Natale Corrado3,Bellincontro Andrea2ORCID,Moreno Juan1ORCID

Affiliation:

1. Department of Agricultural Chemistry, Edaphology and Microbiology, Marie Curie (C3) and Severo Ochoa (C6) Buildings, Agrifood Campus of International Excellence ceiA3 University of Córdoba Córdoba Spain

2. Department for Innovation of Biological, Agrofood and Forest Systems (DIBAF) University of Tuscia Viterbo Italy

3. Department of Electronic Engineering University of Rome Tor Vergata Rome Italy

Abstract

AbstractBACKGROUNDThe growing demand for rosé sparkling wine has led to an increase in its production. Traditional or Charmat wine‐making influence the aromatic profiles in wine. An analysis such as gas chromatography makes an accurate assessment of wines based on volatile detection but is resource intensive. On the other hand, the electronic nose (E‐nose) has emerged as a versatile tool, offering rapid, cost‐effective discrimination of wines, and contributing insights into quality and production processes because of its aptitude to perform a global aromatic pattern evaluation. In the present study, rosé sparkling wines were produced using both methods and major volatile compounds and polyols were measured. Wines were tested by E‐nose and predictive modelling was performed to distinguish them.RESULTSVolatile profiles showed differences between Charmat and traditional methods, especially at 5 months of aging. A partial least square discriminant analysis (PLS‐DA) was carried out on E‐nose detections, obtaining a model that describes 94% of the variability, separating samples in different clusters and correctly identifying different classes. The differences derived from PLS‐DA clustering agree with the results obtained by gas‐chromatography. Moreover, a principal components regression model was built to verify the ability of the E‐nose to non‐destructively predict the amount of different volatiles analyzed.CONCLUSIONProduction methods of Rosé sparkling wine affect the final wine aroma profiles as a result of the differences in terms of volatiles. The PLS‐DA of the data obtained with E‐nose reveals that distinguishing between Charmat and traditional methods is possible. Moreover, predictive models using gas chromatography‐flame ionization detection analysis and E‐nose highlight the possibility of fast and efficient prediction of volatiles from the E‐nose. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Funder

Junta de Andalucía

Publisher

Wiley

Subject

Nutrition and Dietetics,Agronomy and Crop Science,Food Science,Biotechnology

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