Application of Electronic Nose for Detection of Wine-Aging Methods

Abstract

This study reports the application of an electronic nose for the identification and classification of red wines aged three different methods. The signals of the different wines detected by the 10 sensors present in the E-nose are significantly different from each other. The response to the signal generates a typical chemical fingerprint of the volatile compounds present in the wines. Principal Component Analysis can be applied for the dimensionality reduction of the collected signal. Since the total contribution rate of the first three principal components is up to 97.27%, different wines can be distinguished from each other by the three principal components. Euclidean distance, correlation analysis, Mahalanobis distance and linear discrimination analysis can offer 100% accuracy for known samples, and the accuracy rate can reach 88.9% for the 18 test samples. In addition, numerous advantages exist compared with sensory analysis in both authentication and quality control of wines.

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection

“…The electronic nose (E-nose), an apparatus designed to mimic human olfactory perception, has recently become a powerful tool in the food industry [6,7,8,9,10] and other fields [11,12,13]. Regarding wine quality detection, Wei et al [14] reported an E-nose application to distinguish between wines aged in oak barrels and others. Lozano et al [15] developed an in-situ, on-line E-nose system for monitoring wine preservation and evolution in tanks in real time.…”

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

“…In general, previous applications of e-noses in the wine industry have been implemented mainly for the analysis of grapes and crushing methods [ 37 ], improvement of maceration and fermentation processes [ 38 ], to monitor the aging of wine in barrels [ 39 , 40 , 41 ], geographical classification [ 42 ], wine spoilage [ 28 , 43 , 44 ], and to assess correlations with human perception through sensory evaluation [ 27 , 29 , 45 ]. However, most of these studies have been based on multivariate data analysis and correlation analysis.…”

Electronic Noses and Tongues in Wine Industry

“…For instance, e-noses based on MOX sensors can discriminate between red wines vinified under the same conditions but aged in oak barrels of different origins. Moreover, the measure of the changes in the volatile composition, measured periodically, can be a valuable tool to monitor aging (Lozano et al, 2008b; Apetrei et al, 2012; Wei et al, 2014). A potentiometric e-tongue has been able to classify Port wines of different ages (from 2 to 70 years) (Rudnitskaya et al, 2007) and Madeira wines produced from different varieties of grape and length of aging (Rudnitskaya et al, 2010).…”