Online Scent Classification by Ion-mobility Spectrometry Sequences
For ion-mobility spectrometry (IMS)-based electronic noses (eNose) the samples of scents are markedly time-dependent, with a transient phase and a highly volatile stable phase in certain conditions. At the same time, the samples depend on various environmental factors, such as temperature and humidity. This makes fast classification of the scents challenging. The present aim was to develop and test an algorithm for online scent classification that mitigates these dependencies by using both baseline measurements and sequences of samples for classification. A classifier based on the K nearest neighbors approach was derived. The classifier is able to use measurements from both transient and stable phase, yields a label for the analyzed scent, and information on the trustworthiness of the returned label. In order to eliminate correlation in the multivariate measurement data, principal component analysis was applied to the data. The classifier was tested with four food scents, each presented in two different ways to the IMS. By using baseline measurements, the misclassification rate was reduced from 20.0% to 13.3%. A second experiment showed that the used IMS type experiences device heterogeneity.