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An electronic nose sensor array can classify and quantify different types of gases; however, the sensor can alter its measurement capability over time. The main problem presented during the measurements of the sensors is related to the variation of the data acquired for long periods due to changes in the chemosensory response, thus affecting the correct functioning of the implemented measuring system. This research presents an approach to improve gas quantification through the implementation of machine learning regression techniques in an array of nose-type electronic sensors. The implemented methodology uses a domain adaptation approach with the Kullback–Leibler importance estimation procedure (KLIEP) to improve the performance of the gas quantification electronic nose array. This approach is validated using a three-year dataset measured by a 16-electronic-nose-sensor array. The R2 regression error obtained for each of the gases fits the resulting dataset’s measured values with good precision.

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