  Optical imaging modalities such as near-infrared spectroscopy (NIRS) and
hyperspectral imaging (HSI) represent a promising alternative for low-cost,
non-invasive, and fast monitoring of functional and structural properties of
living tissue. Particularly, the possibility of extracting the molecular
composition of the tissue from the optical spectra in real-time deems the
spectroscopy techniques as a unique diagnostic tool. However, due to a lack of
paired optical and molecular profiling studies, building a mapping between a
spectral signature and a corresponding set of molecular concentrations is still
an unsolved problem. Furthermore, no established methods exist to streamline
the inference of the biochemical composition from the optical spectrum for
real-time applications such as surgical monitoring. In this paper, we analyse a
technique for fast and accurate inference of changes in the molecular
composition of brain tissue. We base our method on the Beer-Lambert law to
analytically connect the spectra with concentrations and use a deep-learning
approach to significantly speed up the concentration inference compared to
traditional optimization methods. We test our approach on real data obtained
from the broadband NIRS- and HSI-based optical monitoring of brain tissue. The
results demonstrate that the proposed method enables real-time molecular
composition inference while maintaining the accuracy of traditional linear and
non-linear optimization solvers.
