Although electronic nose technology has been studied for years, drift effects remain one of the major challenges. While ongoing research focuses on effective correction methods, the evaluation of these methods requires reliable and well-documented datasets. However, only a few drift datasets are available, some of which lack sufficient experimental detail or are outdated. This motivated us to introduce a new long-term drift dataset. It has been collected over 12 months using a commercial electronic nose, which is based on 62-metal oxide sensors. The measurements were conducted under controlled experimental conditions with three analytes (diacetyl, 2-phenylethanol, and ethanol) in different concentrations. The dataset consists of 700 time-series recordings, for which we provide both the raw data and a set of pre-extracted features. The data can support the development, evaluation, and comparison of methods for feature extraction and selection, as well as drift detection and compensation. By providing a comprehensive, well-documented dataset, we aim to advance research on sensor drift in electronic nose systems.

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.