Drift Compensation of Gas Sensor Array Data by Common Principal Component Analysis A. Ziyatdinov a,b , S. Marco e,b,f , A. Chaudry c , K. Persaud d , P. Caminal a,b , A. Perera a,b a Centre de Recerca en Enginyeria Biom` edica. Universitat Polit` enica de Catalunya, Pau Gargallo 5, 08028 Barcelona, Spain b Centro de Investigaci´ on Biom´ edica en Red en Bioingenier´ ıa, Biomateriales y Nanomedicina (CIBER-BBN) c Protea Ltd, 11 Mallard Court, Mallard Way, Crewe Business Park, Crewe, Cheshire, CWA 6ZQ, UK d School of Chemical Engineering and Analytical Science, The University of Manchester, PO Box 88, Sackville St, Manchester, M60 1QD, UK e Institute for Bioengineering of Catalonia (IBEC), Baldiri Reixac, 13, 08028 Barcelona, Spain f Departament d’Electr` onica, Universitat de Barcelona, Mart´ ı i Franqu´ es 1, 08028-Barcelona Abstract A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a Principal Component Analysis (PCA) of a reference gas. The proposed new method – employing no specific refer- ence gas, but information from all gases –has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7-months including three gases at different concentrations for an array of 17 polymeric sensors. Preprint submitted to Sensors and Actuators: B October 6, 2009