Feature extraction on three way enose signals

When enose signals are analysed, the signal processing phase plays an important role in the quality of the end results. With the aim of getting more reliable information, the proposal of incorporating the whole transitories of each gas sensor simultaneously recorded, to build a three-way dataset seems to be a good option. But, anyway, this strategy must be accompanied by suitable signal processing/feature extraction of the data in order to achieve stable solutions.In this work the possibilities of the use of parallel factor analysis (PARAFAC) as a data compression technique suitable to deal with trilinear 3D data arrays are shown. To exemplify its performance, a quantitative case focused on food analysis has been selected. The results obtained point out the suitability of the technique to achieve a good predictive ability by using a simple inverse least squares (ILS) calibration onto a set of synthetic samples.