Using a Light-Weight CNN for Perfume Identification with An Integrated Handheld Electronic Nose

Abstract

Exposing counterfeit perfume products is significant for protecting the legal profit of genuine perfume manufacturers and the health of perfume consumers. As a holistic solution to the problem of perfume identification (PI) using an electronic nose (EN), the methods based on convolutional neural network (CNN) simplifies the inconvenient selection of methods and parameter values, which has traditionally complicated existing combinatory methods. However, existing CNN methods that can be used for EN-based PI were designed on the premise that the CNN model can be trained with plenty of computational resources in divide-body ENs. Aiming at PI with an integrated handheld EN, a novel light-weight CNN method, namely LwCNN, is presented for being entirely conducted on a resource-constrained NVDIA Jetson nano module. LwCNN utilizes a sequenced stack of two feature flattening layers, two one-dimensional (1D) convolutional layers, a 1D max-pooling layer, a feature dropout layer, and a fully connected layer. Extensive real experiments were conducted on an integrated handheld EN to the performance of LwCNN with those of four existing benchmark methods. Experimental results show that LwCNN obtained an average identification accuracy of 98.35% with model training time of about 26 s.