  Several experimental measurements are expressed in the form of
one-dimensional profiles, for which there is a scarcity of methodologies able
to classify the pertinence of a given result to a specific group. The
polarization curves that evaluate the corrosion kinetics of electrodes in
corrosive media are an application where the behavior is chiefly analyzed from
profiles. Polarization curves are indeed a classic method to determine the
global kinetics of metallic electrodes, but the strong nonlinearity from
different metals and alloys can overlap and the discrimination becomes a
challenging problem. Moreover, even finding a typical curve from replicated
tests requires subjective judgement. In this paper we used the so-called
multi-q approach based on the Tsallis statistics in a classification engine to
separate multiple polarization curve profiles of two stainless steels. We
collected 48 experimental polarization curves in aqueous chloride medium of two
stainless steel types, with different resistance against localized corrosion.
Multi-q pattern analysis was then carried out on a wide potential range, from
cathodic up to anodic regions. An excellent classification rate was obtained,
at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and
both potential ranges, respectively, using only 2% of the original profile
data. These results show the potential of the proposed approach towards
efficient, robust, systematic and automatic classification of highly non-linear
profile curves.

Computational Engineering, Finance, and Science;Computer Vision and Pattern Recognition