A study on the development of a compensation method for humidity effect in QCM sensor responses

In this study, humidity influence on quartz crystal microbalance (QCM) sensor responses during gas applications is investigated. The goal is to search a compensation method to remove humidity influence over the QCM sensor array at the data processing stage. Some experiments have been conducted to observe sensors’ behavior with the industrial gas of toluene under changing humidity conditions. The observed drifts due to humidity in the sensor responses point out that a compensation model is possible. The system consists of a sensor cell, an electronic circuitry and a data processing unit. The data processing unit has a humidity compensation module which is built using an artificial neural network (ANN). The dimensionality of the inputs to the compensation module is reduced to two using principal component analysis (PCA). The data processing unit uses the sensors’ stable state frequency shift values, and outputs the gas concentration independent from ambient humidity. The system does not need a humidity sensor. The ANN is trained using 80% of the data, and remaining is used for validation and testing. The average absolute relative error in predicting the toluene concentrations is 1.15%.