Abstract (EN):
In this work, bioprocess monitoring based on spectral data is improved when compared to commonly applied chemometric tools, by merging nonparametric modeling, biological and process a priori knowledge into a hybrid semi-parametric model. This particular semi-parametric structure comprises a nonparametric submodel inspired by a NPLS structure, as NPLS has been reported to be successful for dealing with massive numbers of highly correlated spectral data. The method was applied to Bordetella pertussis cultivations equipped with a Near-InfraRed (NIR) probe, showing that estimates of metabolite concentrations are improved when compared to those obtained through classical chemometric modeling, as expressed by lower mean square errors, better calibration properties and a higher statistical confidence.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
6