  In chemical processing and bioprocessing, conventional online sensors are
limited to measure only basic process variables like pressure and temperature,
pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of
other chemical species is more difficult to measure, as it usually requires an
at-line or off-line approach. Such approaches are invasive and slow compared to
on-line sensing. It is known that different molecules can be distinguished by
their interaction with monochromatic light, producing different profiles for
the resulting Raman spectrum, depending on the concentration. Given the
availability of reference measurements for the target variable, regression
methods can be used to model the relationship between the profile of the Raman
spectra and the concentration of the analyte. This work focused on pretreatment
methods of Raman spectra for the facilitation of the regression task using
Machine Learning and Deep Learning methods, as well as the development of new
regression models based on these methods. In the majority of cases, this
allowed to outperform conventional Raman models in terms of prediction error
and prediction robustness.
