Rivera, Paula AngaritaWoollam, MarkSiegel, Amanda P.Agarwal, Mangilal2023-01-302023-01-302021-06Rivera, P. A., Woollam, M., Siegel, A. P., & Agarwal, M. (2021). Gaussian Process Regression and Monte Carlo Simulation to Determine VOC Biomarker Concentrations Via Chemiresistive Gas Nanosensors. 2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS), 1–6. https://doi.org/10.1109/DTS52014.2021.9498219978-1-66542-542-1https://hdl.handle.net/1805/31053Utilizing chemiresistive gas sensors for volatile organic compound (VOC) detection has been a growing area of investigation in the last decade. VOCs have been extensively studied as potential biomarkers for biomedical applications as they are byproducts of metabolic pathways which are dysregulated by disease. Therefore, sensor arrays have been fabricated in previous studies to detect VOC biomarkers. In the process of testing these sensors, it is highly advantageous to quantify the concentration of the VOC biomarkers with high accuracy to diagnose the disease with high sensitivity and specificity. To investigate, analyze, and understand the relation between the concentrations of the VOC to the sensor resistance response, Gaussian Process (GP) models were implemented to predict the behavior of the data with respect to the resistance when the sensor is exposed to a range of concentrations of VOCs. Additionally, the relation between the concentration and resistance of the sensor was studied to predict the concentration of the VOC when a resistance is obtained. Monte Carlo Simulation Sampling from the GP model was utilized to generate data to further understand the trend. The results demonstrated that the relation between the concentration and resistance is linear. The model was tested with sampling data and its accuracy was evaluated.en-USPublisher PolicyBiological system modelingGaussian Process RegressionMonte Carlo SimulationNanosensorsArticle