On data collection time by an electronic nose

We use electronic nose data of odor measurements to build machine learning classication 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 classication 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 classication performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shufing cross-validation approach for determining the reported models’ average accuracy and standard deviation.