Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions

Machine Learning for Enhanced Operation of Underperforming Sensors in Humid Conditions

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Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaining good results with underperforming sensors that, at first glance, would be discarded.For this aim, we characterized chemical gas sensors with low repeatability and random drift towards both dangerous and Lacrosse - Accessories innocuous volatile organic compounds (VOCs) under different levels of relative humidity.Our results show classification accuracies Wall Clock higher than 90% when differentiating harmful from harmless VOCs and coefficients of determination, R2, higher than 80% when determining their concentration in the parts per billion to parts per million range.