Quantitative analysis of NO2 in the presence of CO using a single tungsten oxide semiconductor sensor and dynamic signal processing
Quantitative analysis of NO2 in the presence of CO using a single tungsten oxide semiconductor sensor and dynamic signal processing
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DOI：
10.1039/b205009a
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发表时间：
2002-01-01
期刊：
影响因子：
4.2
通讯作者：
Correig, X
中科院分区：
文献类型：
--
作者：
Ionescu, R;Llobet, E;Correig, X
我们证明，NO2可以定量分析CO的存在下，使用一个单一的氧化钨为基础的电阻式气体传感器。传感器的工作温度在190和380 degreesC之间调制，并监测其对不同浓度的CO、NO2和CO + NO2混合物的动态响应。无论是快速傅立叶变换（FFT）或离散小波变换（DWT）被用来提取传感器响应的重要特征。然后将这些特征输入到不同的（统计和神经）模式识别方法中。所考虑的物种可以区分与成功率高于90%，使用模糊ARTMAP或径向基函数神经网络。研究的气体的浓度可以准确地预测，通过使用小波变换耦合偏最小二乘（PLS）模型。CO + NO2混合物中CO、NO2和NO2的预测浓度与真实的浓度的相关系数分别为0.923、0.870和0.866。
We demonstrate that NO2 can be quantitatively analysed in the presence of CO using a single tungsten oxide based resistive gas sensor. The working temperature of the sensor was modulated between 190 and 380 degreesC and its dynamic response to different concentrations of CO, NO2, and CO + NO2 mixtures was monitored. Either the fast Fourier transform (FFT) or the discrete wavelet transform (DWT) was used to extract important features from the sensor response. These features were then input to different (statistical and neural) pattern recognition methods. The species considered can be discriminated with a success rate higher than 90% using a Fuzzy ARTMAP or a radial basis function neural network. The concentrations of the gases studied can be accurately predicted, by using the DWT coupled to partial least squares (PLS) models. The correlation coefficients of the predicted versus real concentrations were 0.923, 0.870 and 0.866 for CO, NO2, and NO2 in CO + NO2 mixtures, respectively.