A Gas Classification Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network
Authors: Xiong Yizhou , Xue Yingying , Chen Yuantao , Wan Hao , Wang Ping 
Published: July 2021

The electronic nose is a gas detection instrument using the bionic olfactory mechanism, which usually consists of the gas sensor array and the gas classification algorithm. Furthermore, the capability of the gas classification algorithm is critical to the reliable accuracy of gas recognition. The process of gas classification usually involves pattern recognition of multiple time-related gas sensor response curves. Traditional gas classification algorithms are mainly machine learning methods, such as PCA, LDA, ICA, SVM, KNN, etc. These algorithms are relatively cumbersome because we need to extract handcrafted features before using them. Deep learning also has applications in electronic nose gas classification algorithms[1-4] which improve the accuracy of classification result, but most of deep neural networks have complex structures and consume huge computing resources. The spiking neural network is the third generation of artificial neural network, and its spiking neuron model which is more bionic than previous artificial neurons can process spike sequence signals[5]. The spiking neural network model has a simple structure with higher computational efficiency, and takes up less computational resources. Moreover, its time attribute makes it more suitable for processing information about time series. In order to simultaneously take advantage of the efficient feature extraction of the convolutional neural network and the high computational efficiency of the spiking neural network, our team converted the convolutional neural network into a convolutional spiking neural network(CSNN) and applied it to gas classification. The activation function layer in the traditional convolutional layer was replaced with the spiking neuron layer which used the IF or LIF spiking neuron model to transform the continuous values passed by the convolutional later into discrete values so as to achieve the transmission of spikes between layers. The first convolutional spiking layer was used as a spiking encoder, so the spiking encoding method such as Gaussian encoding was not used. The spike-firing-frequency output by the last layer of neurons was calculated to obtain the classification result. The probability that a gas sample belonged to a certain class was proportional to the spike-firing-frequency of the corresponding neuron of the class. Our team built a convolutional spiking neural network model with 9 layers of convolutional spiking layer and 2 layers of fully connected spiking layer, and used the food spoilage gas dataset collected by us and open source gas mixtures dataset[6] to evaluate the capability of our model. With regard to the gas mixtures dataset, ethylene, methane, CO and their mixed state need to be classified. After training, CSNN achieved the test accuracy of 92.6%, and the other algorithms’ test accuracy were 92.9% of ResNet-18, 91.2% of one-dimensional deep convolutional neural network(1D-DCNN) and 88.5% of SVM. As for the food spoilage gas dataset, 30 types of spoiled meat, vegetables, fruits and their mixed state samples were measured. The first task was to classify the major categories of spoiled food, furtuer, the 30 types of spoiled food odor samples were going to be divided into 4 categories: fresh food, spoiled meat, spoiled vegetables and spoiled fruits. After training, CSNN achieved the test accuracy of 81.4% which had a certain accuracy improvement comparing with 80.6% of ResNet-18, 80.1% of 1D-DCNN and 77.3% of SVM. The second task was to classify the subcategories of spoiled fruits, that is, 8 classes of spoiled fruit odor samples should be classified. After training, CSNN could achieve high test accuracy of 90.7%, and the accuracy of other algorithms was 88.8% of ResNet-18, 87.1% of 1D-DCNN and 77.9% of PCA+ANN. The CSNN output of a spoiled watermelon sample is shown in Figure 1.

In conclusion, CSNN had similar odor classification performance to ResNet-18, but the computing resources occupied by CSNN was only 1/5 of ResNet-18. This research shows that the spiking neural network has the advantages of high odor classification accuracy, great calculation efficiency and occupying few computing resources. It is suitable as a gas classification algorithm of electronic nose and for further development.

References

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Figure 1