CS-MA-CNN: A Fast Recognition Network of Electronic Nose for Ignitable Liquids Detection

Detecting ignitable liquids (ILs) remaining at the fire scene is a critical task in fire investigation. Current detection methods mainly rely on large analytical instruments, which suffer from slow detection speeds and high costs. Electronic nose (e-nose) has been widely used in the fields of food testing, environmental monitoring, and disease diagnosis due to its advantages of fast detection speed and low cost. However, there has been limited research on the detection of ILs using e-nose technology. This study introduces a novel identification method called the channel-separated multiscale attentional convolutional neural network (CS-MA-CNN). The CS-MA-CNN utilizes a portion of the response time data from an e-nose to achieve rapid identification of ILs. The highlights of CS-MA-CNN are as follows: 1) CS-MA-CNN is a channel separation (CS) technique for 1-D convolutional neural networks (CNNs) that effectively captures the correlations between the temporal and channel dimensions of e-nose data; 2) a multiscale attention (MA) module is applied to enhance important features in the main channel and temporal feature maps and suppress the redundant features; and 3) the fully connected-layer network is used for classifying the ILs labels. Recognition experiments were conducted on four types of ILs using a homemade portable e-nose. The results show that the CS-MA-CNN achieved an impressive classification accuracy of 94.53% with just 5 s of the e-nose response data.