  Hyperspectral unmixing aims at decomposing a given signal into its spectral
signatures and its associated fractional abundances. To improve the accuracy of
this decomposition, algorithms have included different assumptions depending on
the application. The goal of this study is to develop a new unmixing algorithm
that can be applied for the calibration of multi-point scintillation dosimeters
used in the field of radiation therapy. This new algorithm is based on a
non-negative matrix factorization. It incorporates a partial prior knowledge on
both the abundances and the endmembers of a given signal. It is shown herein
that, following a precise calibration routine, it is possible to use partial
prior information about the fractional abundances, as well as on the
endmembers, in order to perform a simplified yet precise calibration of these
dosimeters. Validation and characterization of this algorithm is made using
both simulations and experiments. The experimental validation shows an
improvement in accuracy compared to previous algorithms with a mean spectral
angle distance (SAD) on the estimated endmembers of 0.0766, leading to an
average error of $(0.25 \pm 0.73)$ % on dose measurements.
