A wireless and portable electronic nose to differentiate musts of different ripeness degree and grape varieties

• Published in: Sensors (Basel, Switzerland) Vol. 15; no. 4; pp. 8429 - 8443
• Main Authors: Aleixandre, Manuel, Santos, Jose Pedro, Sayago, Isabel, Cabellos, Juan Mariano, Arroyo, Teresa, Horrillo, Maria Carmen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-04-2015; MDPI
• Subjects: analytical parameters; CCA; Correlation analysis; degree of ripeness; electronic nose; Electronic noses; Grapes; must; PCA; PNN; Portability; Quality; Ripening; Sensors; VOCs; Volatile organic compounds; Wines; Spain
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s150408429

Two novel applications using a portable and wireless sensor system (e-nose) for the wine producing industry-The recognition and classification of musts coming from different grape ripening times and from different grape varieties-Are reported in this paper. These applications are very interesting because a lot of varieties of grapes produce musts with low and similar aromatic intensities so they are very difficult to distinguish using a sensory panel. Therefore the system could be used to monitor the ripening evolution of the different types of grapes and to assess some useful characteristics, such as the identification of the grape variety origin and to prediction of the wine quality. Ripening grade of collected samples have been also evaluated by classical analytical techniques, measuring physicochemical parameters, such as, pH, Brix, Total Acidity (TA) and Probable Grade Alcoholic (PGA). The measurements were carried out for two different harvests, using different red (Barbera, Petit Verdot, Tempranillo, and Touriga) and white (Malvar, Malvasía, Chenin Blanc, and Sauvignon Blanc) grape musts coming from the experimental cellar of the IMIDRA at Madrid. Principal Component Analysis (PCA) and Probabilistic Neural Networks (PNN) have been used to analyse the obtained data by e-nose. In addition, and the Canonical Correlation Analysis (CCA) method has been carried out to correlate the results obtained by both technologies.