Virtual sensor array based on Butterworth-Van-Dyke equivalent model of QCM for selective detection of volatile organic compounds

Recently virtual sensor arrays(VSAs) have been developed to improve the selectivity of volatile organic compound (VOC)sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in loss of much valuable information. In this work, we propose a VSA with a high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in detection of the VOCs. We take the changes of parameters of the Butterworth-Van-Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. Dimensionality of the VSA’s responses has been expanded to four independent outputs, and the responses to the VOCs have shown a good linearity in multidimensional space. The response and recovery times are 16 s and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify “patients with diabetic ketosis” from volunteers with an accuracy of 95%, based on detection of their exhaled breath. The QCM-based VSA shows a great potential for detecting VOC biomarkers in human breath for disease diagnosis.