A variance-based, BIC-penalized variable selection method identifies influential interferents in sensor calibration and improves pollutant prediction in simulated and outdoor carbon nanotube sensor settings.
Gas sensing technologies -- status, trends, perspectives and novel applications
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abstract
The strong, continuous progresses in gas sensors and electronic noses resulted in improved performance and enabled an increasing range of applications with large impact on modern societies, such as environmental monitoring, food quality control and diagnostics by breath analysis. Here we review this field with special attention to established and emerging approaches as well as the most recent breakthroughs, challenges and perspectives. In particular, we focus on (1) the transduction principles employed in different architectures of gas sensors, analysing their advantages and limitations; (2) the sensing layers including recent trends toward nanostructured, low-dimensional and composite materials; (3) advances in signal processing methodologies, including the recent advent of artificial neural networks. Finally, we conclude with a summary on the latest achievements and trends in terms of applications.
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Variance-based variable selection in sensor calibration with strong interferents -- application to air pollution monitoring with a carbon nanotube sensor array
A variance-based, BIC-penalized variable selection method identifies influential interferents in sensor calibration and improves pollutant prediction in simulated and outdoor carbon nanotube sensor settings.