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.

制约气体传感器发展的一个难题是多组分识别。在此，通过微加工技术制造了装载有三种气敏材料的气敏 (GS) 微芯片。然后，搭建了便携式气体检测系统，采集芯片在六氟化硫（SF ₆）。通过堆叠去噪自编码器（SDAE），可以从原始信号中提取总共五个高级特征。结合机器学习算法，实现了47个模拟物的准确分类，5折交叉验证证明了可靠性。为了考察泛化能力，进行了 30 组未知气体测试。结果表明，无论噪声大小如何，基于 SDAE 的模型都比基于 PCA 的模型表现出更好的泛化性能。此外，还引入了假设检验来检查各种模型的显着差异，带有 SDAE 的基于 Bagging 的反向传播神经网络在 95% 的置信度下表现出卓越的性能。