Y. Umezawa, P. Bühlmann, K. Umezawa, K. Tohda, and S. Amemiya, Potentiometric selectivity coefficients of ion-selective electrodes, Pure and Applied Chemistry, vol.72, pp.1851-2082, 2000.

L. T. Duarte and C. Jutten, Blind Source Separation of a Class of Nonlinear Mixtures, Proc. of, pp.4666-4707, 2007.
DOI : 10.1007/978-3-540-74494-8_6

URL : https://hal.archives-ouvertes.fr/hal-00173300

S. Moussaoui, D. Brie, A. Mohammad-djafari, and C. Carteret, Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4133-4145, 2006.
DOI : 10.1109/TSP.2006.880310

URL : https://hal.archives-ouvertes.fr/hal-00121602

C. Févotte and S. J. Godsill, A Bayesian Approach for Blind Separation of Sparse Sources, IEEE Transactions on Audio, Speech and Language Processing, vol.14, issue.6, pp.2174-2188, 2006.
DOI : 10.1109/TSA.2005.858523

C. P. Robert, The Bayesian Choice, 2007.
DOI : 10.1007/978-1-4757-4314-2

W. R. Gilks, S. Richardson, and D. Spiegelhalter, Markov chain Monte Carlo in practice, 1995.

A. Taleb and C. Jutten, Source separation in post-nonlinear mixtures, IEEE Transactions on Signal Processing, vol.47, issue.10, pp.2807-2820, 1999.
DOI : 10.1109/78.790661

S. M. Kay, Fundamentals of statistical signal processing: estimation theory, 1993.

P. Gründler, Chemical sensors: an introduction for scientists and engineers, 2007.

L. T. Duarte, R. Suyama, R. R. Attux, F. J. Zuben, and J. M. Romano, Blind Source Separation of Post-nonlinear Mixtures Using Evolutionary Computation and Order Statistics, Proc. of, pp.3889-66, 2006.
DOI : 10.1007/11679363_9

E. Bakker, E. Pretsch, and P. Bühlmann, Selectivity of Potentiometric Ion Sensors, Analytical Chemistry, vol.72, issue.6, pp.1127-1133, 2000.
DOI : 10.1021/ac991146n