A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases

校准 方案(数学) 材料科学 环境科学 生物系统 计算机科学 分析化学(期刊) 化学 色谱法 数学 统计 数学分析 生物
作者
Yilun Ma,Xuchun Qiu,Zaihua Duan,Lili Liu,Juan Li,Yuanming Wu,Zhen Yuan,Yadong Jiang,Huiling Tai
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:9 (11): 6022-6031 被引量:1
标识
DOI:10.1021/acssensors.4c01867
摘要

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.
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