连续监测化学传感器中温湿度的在线解相关
Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoring
摘要：
提出了一种在线去相关化学传感器信号的环境湿度和温度变化的影响的方法。目标是通过处理来自环境湿度和温度的同时读数的数据来提高电子鼻测量的准确性，以进行连续监测。为这项研究建立的电子鼻装置包括八个金属氧化物传感器，温度和湿度传感器，并与外部计算机进行无线通信。该无线电子鼻在一位作者的住宅中进行了两年的空气监测，在537天内连续采集数据，采样率为每秒1个样本。为了估计空气的湿度和温度的变化对化学传感器信号的影响，我们使用了n型金属氧化物（MOX）气体传感器的标准能带模型。该模型的主要假设是传感器电导率的变化可以表示为外部湿度和温度变化存在下的半导体能带变化的非线性函数。将此模型拟合到收集的数据，我们证实，最具统计学意义的因素是湿度变化以及温度和湿度的相关变化。这个简单的模型实现了出色的准确性与决定系数$R^2$接近1。为了展示湿度-温度校正模型如何用于气体鉴别，我们构建了香蕉、葡萄酒和基线响应之间的在线鉴别模型。这表明模式识别算法通过包括化学传感器的过滤信号来提高性能和可靠性。
英文摘要：
A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 samples per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination $R^2$ close to 1. To show how the humidity-temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors.
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