A Novel Feature Extraction Method an Electronic Nose for Aroma Classification

In this paper, we describe an electronic nose (e-Nose) capable of classifying the aroma of alcoholic beverages. The novelty of this research is using signal processing for initial feature extraction from a sensor and then the use of deep learning to identify patterns of alcoholic beverage aromas. The sensor array was formed by nine types of metal oxide semiconductor sensors. The dataset was formed by images of standard deviations and correlation coefficients for processed signals from the e-Nose sensors. Thus, two patterns were generated. The first pattern came from a polar-chart image of the processed signals' standard deviations. The second pattern was produced by correlation coefficient converted into 3D heat-map images. The image size is 256 x 256 pixels. The convolutional architecture for fast feature embedding framework then trained GoogLeNet network using the dataset images. The training process was configured for 300 epochs and 0.0001 learning rate. The GoogLeNet network model from deep learning was compared with the AlexNet network model. The final classification was based on two patterns of prediction. The true label is used if the prediction accuracy value exceeds 70 %. The result is true only if both 3D heat-map and polar-chart prediction have true labels. The aroma detection accuracies of the GoogLeNet model are 85.0% for polar-chart and 85.416% accuracy for 3D heat-map. The aroma identification accuracy of AlexNet model are 85.0% for polar-chart and 85.416% accuracy for 3D heat-map.