Wu Peiwen, Qiu Xingchang, Wu Yuanming, Duan Zaihua, Ma Yilun, Yu Haichao, Yuan Zhen, Jiang Yadong, Tai Huiling
State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
49th Research Institute of China Electronics Technology Group Corporation, Harbin 150028, China.
ACS Sens. 2025 Mar 28;10(3):1948-1958. doi: 10.1021/acssensors.4c03092. Epub 2025 Mar 12.
Electronic noses have been widely used in industrial production, food preservation, agricultural product storage, environmental monitoring, and other fields. However, due to the cross-sensitivity of gas-sensing responses, accurately measuring the concentration of mixed gases remains challenging. To address this issue, this study attempts to determine the number of state variables that produce the cross-influence based on the experimental data, establish the state space model from the equivalent circuit model, and obtain model parameters through parameter correlation iterative algorithms and a Kalman filter. The sensor response model and the concentration measurement model of mixed gases are established accordingly. The simulation and experimental results show that these two models have high accuracy in predicting the sensor response and measuring the concentrations of mixed gases under the influence of mixed gases on the sensors.
电子鼻已广泛应用于工业生产、食品保鲜、农产品储存、环境监测等领域。然而，由于气敏响应的交叉敏感性，准确测量混合气体的浓度仍然具有挑战性。为了解决这个问题，本研究试图根据实验数据确定产生交叉影响的状态变量数量，从等效电路模型建立状态空间模型，并通过参数相关迭代算法和卡尔曼滤波器获得模型参数。据此建立了传感器响应模型和混合气体浓度测量模型。仿真和实验结果表明，这两个模型在预测传感器响应以及在混合气体对传感器产生影响的情况下测量混合气体浓度方面具有很高的准确性。
