  The motivation of this work is the use of non-invasive and low cost
techniques to obtain a faster and more accurate diagnosis of systemic sclerosis
(SSc), rheumatic, autoimmune, chronic and rare disease. The technique in
question is Near Infrared Spectroscopy (NIRS). Spectra were acquired from three
different regions of hand's volunteers. Machine learning algorithms are used to
classify and search for the best optical wavelength. The results demonstrate
that it is easy to obtain wavelength bands more important for the diagnosis. We
use the algorithm RFECV and SVC. The results suggests that the most important
wavelength band is at 1270 nm, referring to the luminescence of Singlet Oxygen.
The results indicates that the Proximal Interphalangeal Joints region returns
better accuracy's scores. Optical spectrometers can be found at low prices and
can be easily used in clinical evaluations, while the algorithms used are
completely diffused on open source platforms.
