Macrowine 2021
IVES 9 IVES Conference Series 9 Neural networks and ft-ir spectroscopy for the discrimination of single varietal and blended wines. A preliminary study.

Neural networks and ft-ir spectroscopy for the discrimination of single varietal and blended wines. A preliminary study.

Abstract

Blending wines from different grape varieties is often used in order to increase wine complexity and balance. Due to their popularity, several types of blends such as the Bordeaux blend, are protected by PDO legislation. In the case of monovarietal wines blending is forbidden, however there is no method to authenticate their status, and for this reason adulteration can are difficult to identify. Fourier Transform Infrared Spectroscopy (FT-IR) has proven successful for the discrimination of wines based on several parameters such as geographical origin and type of aging[1], while the use of Neural Networks is now used more often for the development of prediction models. FT-IR spectroscopy coupled with Neural Networks have been used to develop a prediction model for the discrimination of single varietal and blended wines. Generalized RSquare for the training set was 0,9011 and 0,689 for the validation set, while the -Loglikelihood was 3,918 for the training and 0,111 for the validation set. The misclassified rate was 0,03 for the training set and 0,11 for the validation set, showing very good potential for the use of IR spectroscopy for the authentication of single varietal and blended wines.

DOI:

Publication date: September 10, 2021

Issue: Macrowine 2021

Type: Article

Authors

Marianthi Basalekou

University of West Attica,Christos, PAPPAS, Agricultural University of Athens Petros, TARANTILIS, Agricultural University of Athens Anna, Georgoulaki, University of West Attica Anna, STEFOU, University of West Attica

Contact the author

Keywords

ftir, wine, blend, neural networks

Citation

Related articles…

Toward a model of grape proanthocyanidin extraction during vinification

PAs are compartmentalised within the grape berry, and differ in their composition and degree of extractability. Within each compartment, the CWM limits PA extraction firstly by its degree of permeability and secondly its ability to complex with PA molecules.

Influence of the irrigation period in Tempranillo grapevine, under the edaphoclimatic conditions of the Duero river valley

Irrigation of vineyards is a matter of controversial arguments at areas of high quality wine production. Besides, the effects of the water in the plant are closer related to the water availability than to the irrigation regime.

Terroir effects from the reflectance spectra of the canopy of vineyards in four viticultural regions

Knowledge of the reflectance spectrum of grape leaves is important to the identification of grape varieties in images of viticultural regions where several cultivars co-exist.

Barrels ad-hoc: Spanish oak wood classification by NIRs 

The wooden barrel is a key factor in enology, since wine chemical composition and sensory properties changes significantly in contact with the barrel[1]. Today’s highly competitive market constantly demands new differentiated products and wineries search innovations continuously.
Wood selection is crucial: barrels stability to keep constant their contribution and the result on products, and additional and differentiated wood contributions to impact their new products. Oak wood selection has traditionally been carried out using parameters such as specie, location and grain, however, it goes one step further nowadays. Large cooperage work with non-destructive techniques that allow classifying oak wood quickly and easily according to their organoleptic contribution[2].

Specificities of red wines without sulfites: which role for acetaldehyde and diacetyl? A compositional and sensory approach.

Sulfur dioxide is the most commonly used additive in oenology to protect wine from oxidation and microorganisms. Once added to wine SO2 is able to react with carbonyl compounds to form carbonyl bisulfites what affects their reactivity.