Multiple-sensor predictive chemometrics approach
If only a single quality attribute is monitored such soft sensors are
often based on single sensors, e.g., the monitoring of the product
quantity by UV spectroscopy (Rolinger et al., 2021) or attenuated total
reflection Fourier-transform infrared (ATR‐FTIR) spectroscopy (Rudt et
al., 2019). However, multiple sensors must be combined even if only the
process signals are used that a chromatographic workstation is typically
equipped with, e.g., UV, conductivity and pH probe (Nikita et al.,
2022). If several CQAs need to be modelled in real-time, it is
indispensable to use a multiple sensor approach in order to capture the
different properties of these CQAs (Sauer et al., 2019). Such an
approach can be regarded as data fusion (Borras et al., 2015; Liggins et
al., 2017; Rolinger et al., 2021). A multiple sensor approach has many
advantages, e.g., in the case of sensor saturation where the combination
of different input variables leads to redundancies that can compensate
for the information loss.