Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender
Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender
复制标题
DOI：
10.1109/jsen.2022.3215127
复制
发表时间：
2022-12
影响因子：
4.3
通讯作者：
M. Aleixandre;T. Nakamoto
中科院分区：
文献类型：
--
作者：
M. Aleixandre;T. Nakamoto
在这项工作中，以前提出的主动气味传感的程序进行了改进。在该主动感测测量程序期间，控制气味混合器以准确地再现先前测量的混合气味的目标气味。最终混合的气味是主动感测测量程序的结果。通过由四个涂有不同传感膜的石英晶体微天平（QCM）组成的传感器阵列测量气味，以获得频率变化和电阻变化。气味混合器混合两种挥发性有机化合物，戊酸乙酯和丙酸。由于相对测量，该程序在一定程度上对传感器漂移或环境条件（如湿度）的变化具有鲁棒性。然而，在不断变化的环境下，需要重新校准。在这篇文章中，我们提出了一个改进的过程，在线学习的参数，避免重新校准。在主动测量过程中，采用递推最小二乘法对模型参数进行更新，并重新设计控制回路。该程序在不同的场景、传感器漂移和湿度下进行了测试。结果表明，该系统能够自适应传感器参数的变化，提高了鲁棒性的主动传感过程中使用的在线学习。
In this work, a previously proposed procedure of active odor sensing was improved. During this active sensing measurement procedure, an odor blender was controlled to reproduce accurately the target odor of a previously measured blended odor. The final blended odor was the result of the active sensing measurement procedure. The odor was measured by a sensor array composed of four quartz crystal microbalances (QCMs) coated with different sensing films to obtain frequency changes and resistance changes. The odor blender mixed two volatile organic compounds, ethyl valerate and propionic acid. The procedure was robust to some degree against sensor drift or changes of ambient conditions such as humidity because of relative measurement. However, a recalibration was needed under changing environment. In this article, we present an improvement of the procedure with online learning of the parameters that avoid recalibrations. During the active measurement, the model parameters are updated by recursive least squares and the control loop is redesigned. This procedure was tested in different scenarios, sensor drift, and humidity. The results show that the system can adapt itself to sensor parameter changes improving the robustness of the active sensing procedure using the online learning.