However, the use of these powerful data analysis methodologies for the elucidation of spectral markers with discriminating power, while essential for exploiting the large amount of information contained on spectra of biological samples, comes with a cost. Due to the black-box nature of the 'vibrational signature' obtained by those highly sophisticated ML procedures, it is hard to establish how variations in the experimental process can affect the results of the methodology beyond the ones used in the calibration and test steps. This includes types and batches of instruments, personnel as well as demographics. In turn, this can be an impediment when scaling the methodology from proof-of-concept research to commercial solutions in the real world.