On data collection time by an electronic nose

Abstract

<p>We use electronic nose data of odor measurements to build machine learning classification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.</p>

The Application of In Situ Methods to Monitor VOC Concentrations in Urban Areas—A Bibliometric Analysis and Measuring Solution Review

“…Portable devices for direct testing can generally be divided into detectors, chromatographs, and electronic noses. They differ in operating principle, sensitivity, measurement range, response time, and selectivity [61][62][63][64]. The individual measurement solutions are discussed in more details later in this paper.…”