Differentiation through E-Nose and GC-FID data modeling of rosé sparkling wines elaborated via Traditional and Charmat methods

BACKGROUND

The 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.

RESULTS

Volatile 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.

CONCLUSION

Production 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.