Year of wine aromas classification by using principal component analysis as feature reduction

In the area of electronic noses (e-nose), applications in the field of wine aromas detection are uncommon. The number of qualified human wine experts is low and their cost is high. This paper has been developed for the purpose of recognition of typical aromas in red wines at a low cost. We propose simple linear regression analysis to classify typical aromatic compounds in wine by years of an electronic nose and using feature reduction-based method, principal component analysis (PCA) as feature extraction techniques show datasets of this group of compounds are clearly improved the requirement as follows percent classification rates (performance evaluation). The experiment simple linear regression analysis classification results different types of wine grapes percentage of correlation extract and different years of wine grapes.