Application of Electronic Nose for Detection of Wine-Aging Methods

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.