Wine Quality Assessment using Electronic Nose

The measurement of alcohol quality plays a key role in the alcohol industry. The traditional methods to evaluate the quality of alcohol, requires special training, costly equipment, and time-consuming procedure. An electronic nose (E- nose) with the integration of machine learning (ML) algorithms can be a reliable solution for fast and easy evaluation of alcohol quality. In this paper, design and functional aspects of E-nose architecture in alcohol industries are discussed with emphasis on the type of sensing technologies and decision-making systems. An intelligent E-nose for assessing alcohol quality is introduced in this paper. The ML algorithms, artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) are implemented on wine spoilage threshold dataset. The performance of intelligent E-nose is validated by extensive simulations. The simulation results confirm better performance with ML algorithms.