A difficult issue restricting the development of gas sensors is multicomponent recognition.Herein,a gas-sensing(GS)microchip loaded with three gas-sensitive materials was fabricated via a micromachining technique.Then,a portable gas detection system was built to collect the signals of the chip under various decomposition products of sulfur hexafluoride(SF6).Through a stacked denoising autoencoder(SDAE),a total of five high-level features could be extracted from the original signals.Combined with machine learning algorithms,the accurate classification of 47 simulants was realized,and 5-fold cross-validation proved the reliability.To investigate the generalization ability,30 sets of examinations for testing unknown gases were performed.The results indicated that SDAE-based models exhibit better generalization performance than PCA-based models,regardless of the magnitude of noise.In addition,hypothesis testing was introduced to check the significant differences of various models,and the bagging-based back propagation neural network with SDAE exhibits superior performance at 95％confidence.
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