A novel principal component-based virtual sensor approach for efficient classification of gases/odors

High-performance detection and estimation of gases/odors are challenging, especially in real-time gas sensing applications. Recently, efficient electronic noses (e-noses) are being developed using convolutional neural networks (CNNs). Further, CNNs perform better when they operate on a minimal size of vector response. In this paper, dimensions of the operational vectors have been augmented by using virtual sensor responses. These virtual responses are obtained from the principal components of the physical sensor responses. Accordingly, two sets of data are upscaled as a one-dimensional one. Another level of upscaling is further obtained by using the mirror mosaicking technique. Hence, with our proposed novel approach, the final vector size for CNN operations achieves a new dimension. With this upscaled hybrid dataset, consisting of physical and virtual sensor responses, a simpler CNN has achieved 100 percent correct classification in two different experimental settings. To the best of authors information, it is for the first time that an e-nose has been designed using a principal component-based hybrid, upscaled dataset and achieves 100 percent correct classification of the considered gases/odors.