  Multivariate data analysis and machine-learning classification become popular
tools to extract features without physical models for complex environments
recognition. For electronic noses, time sampling over multiple sensors must be
a fair compromise between a period sufficiently long to output a meaningful
information pattern, and sufficiently short to minimize training time for
practical applications. Particularly when reactivity's kinetics differ from
thermodynamics' in sensitive materials, finding the best compromise to get the
most from data is not obvious. Here, we investigate on the influence of data
acquisition to improve or alter data clustering for molecular recognition on a
conducting polymer electronic nose. We found out that waiting for the sensors
to reach their steady state is not required for classification, and that
reducing data acquisition down to the first dynamical information suffice to
recognize molecular gases by principal component analysis with the same
materials. Particularly for online inference, this study shows that a good
sensing array is no array of good sensors, and that new figure-of-merits shall
be defined for sensing hardware aiming machine-learning pattern-recognition
rather than metrology.
