Analysis of Multivariable Sensor Responses to Multi-Analyte Gas Samples in the Presence of Interferents and Humidity

Abstract

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the mixtures, with reliable identification and accurate quantification, even in the presence of various interferents. Concentration errors of approximately ±5% are obtained for mixtures containing up to 6 BTEX compounds (including chemical isomers) and up to 4 interferents. Additionally, the study investigates the impact of humidity on the polymer/plasticizer-coated shear-horizontal surface acoustic wave (SH-SAW) sensors, demonstrating accurate concentration estimation in a relative humidity range from dry nitrogen to 65%. This sensing-and-multivariate-signal-processing approach is a promising candidate for reliable environmental monitoring in real-world applications.