  Hyperspectral imaging (HSI) is an optical technique that processes the
electromagnetic spectrum at a multitude of monochromatic, adjacent frequency
bands. The wide-bandwidth spectral signature of a target object's reflectance
allows fingerprinting its physical, biochemical, and physiological properties.
HSI has been applied for various applications, such as remote sensing and
biological tissue analysis. Recently, HSI was also used to differentiate
between healthy and pathological tissue under operative conditions in a surgery
room on patients diagnosed with brain tumors. In this article, we perform a
statistical analysis of the brain tumor patients' HSI scans from the HELICoiD
dataset with the aim of identifying the correlation between reflectance spectra
and absorption spectra of tissue chromophores. By using the principal component
analysis (PCA), we determine the most relevant spectral features for intra- and
inter-tissue class differentiation. Furthermore, we demonstrate that such
spectral features are correlated with the spectra of cytochrome, i.e., the
chromophore highly involved in (hyper) metabolic processes. Identifying such
fingerprints of chromophores in reflectance spectra is a key step for automated
molecular profiling and, eventually, expert-free biomarker discovery.
