Articles | Volume 12, issue 1
Regular research article
27 Jan 2023
Regular research article |  | 27 Jan 2023

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Tanja Dorst, Tizian Schneider, Sascha Eichstädt, and Andreas Schütze

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Cited articles

Amann, J., Baur, T., and Schultealbert, C.: Measuring Hydrogen in Indoor Air with a Selective Metal Oxide Semiconductor Sensor: Dataset, Zenodo [data set],, 2021a. a
Amann, J., Baur, T., Schultealbert, C., and Schütze, A.: Bewertung der Innenraumluftqualität über VOC-Messungen mit Halbleitergassensoren - Kalibrierung, Feldtest, Validierung, tm - Tech. Mess., 88, S89–S94,, 2021b. a
Asikainen, A., Carrer, P., Kephalopoulos, S., Fernandes, E. d. O., Wargocki, P., and Hänninen, O.: Reducing burden of disease from residential indoor air exposures in Europe (HEALTHVENT project), Environ. Health, 15, S35,, 2016. a
Baur, T., Schütze, A., and Sauerwald, T.: Optimierung des temperaturzyklischen Betriebs von Halbleitergassensoren, tm - Tech. Mess., 82, 187–195,, 2015. a
Baur, T., Amann, J., Schultealbert, C., and Schütze, A.: Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air, Atmosphere, 12, 647,, 2021. a, b, c
Short summary
A fundamental problem of machine learning (ML) is measurement uncertainty and the influence on ML results. Measurement uncertainty, which is critical in hazardous gas detection, is directly addressed in this paper. A previously published toolbox is extended for regression. One of the benefits of this approach is obtaining a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision.