An Odor Recognition Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network for Spoiled Food Identification

The electronic nose is an odor detection instrument utilizing the bionic olfactory theory, and usually consisting of a gas sensor array and an odor recognition algorithm. Traditional odor recognition algorithms for electronic noses suffer from cumbersome feature extraction steps and low recognition accuracy, and some new algorithms based on deep learning have the disadvantage of insufficient computational efficiency. To solve these problems, a novel odor recognition algorithm based on convolutional spiking neural network is proposed. The network model of this algorithm is composed of convolutional spiking layers, fully-connected spiking layers, and skip connections, which combines the feature extraction ability of the residual convolutional neural network and the computational efficiency of the spiking neural network. We also established a spoiled food mixed odor dataset and a rotten fruit odor dataset for evaluation of our method and several other classic algorithms. Experimental results show that our algorithm achieves average test accuracy of 84.5% and 88.6% on these two datasets, respectively, and has better recognition capability and computational efficiency than several other classic algorithms, which indicates that our algorithm can efficiently identify the spoilage odor in food.