The novel EOS835 electronic nose and data analysis for evaluating coffee ripening

We present an analysis of roasted coffee ripening performed by the novel Electronic Olfactory System EOS835, manufactured by the Italian company Sacmi Imola s.c.a.r.l., which is based on thin film semiconductor metal oxide gas sensors. We focused our analysis on: (1) exploratory data analysis for systematically investigating the outcomes of different sampling conditions and therefore selecting advantageous settings; (2) feature selection for improving classification performance and ranking the contribution of the different sensors and feature types. Exploratory analysis, via the successive generation of PCA plots, showed that the main factors influencing discrimination between diverse ripening times are headspace generation time (HGT, i.e. time elapsed between vial filling and measurement performing) and sample preparation. A relatively long HGT (18 h) allows to follow the ripening progression of the coffee blend over time and to correctly classify the best coffee ripening (as determined by an expert taster). In forming the feature vector, we added a feature calculated in the phase space to the standard features. Feature selection showed that, the phase space feature consistently lead to improved classification and that, of the three sensor types constituting the array, the two indium–tin oxide sensors perform better for our application.