Despite recent significant advancements in gas sensor array technology, accurately identifying gases in mixed environments remains challenging. This difficulty is primarily due to the rapid and competing processes of gas molecules attaching to (adsorption) and detaching from (desorption) the sensor. In this study, we present a simple method to fabricate a 2 × 4 SMO-based gas sensor array, coupled with a sparse recurrent neural network (SRNN) that employs weight regularization. The recurrent layers of the SRNN process nonlinear information and capture temporal dependencies in the sensor data, while the regularization technique simplifies the model, making it both efficient and easier to interpret. Additionally, we introduce a novel feature: the dynamics of current, labeled as ΔI. This feature enables the SRNN model to efficiently detect the adsorption and desorption of gas molecules. We demonstrate that our model can distinguish between three intuitively indistinguishable datasets of gas species (NO2, HCHO, and a mixture) with up to 92 % accuracy. By utilizing the fast and competitive adsorption/desorption information of gas molecules, our model can be applied to various gas combination environments, unlike conventional gas sensing data measured over longer periods. By integrating the sensor array with the advanced SRNN model, we pave the way for sophisticated e-nose systems, with potential applications in advanced gas sensing technologies, such as disease diagnosis through exhaled breath analysis and the detection of toxic species in mixed gas environments.
