English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In chemical processing and bioprocessing, conventional online sensors arelimited to measure only basic process variables like pressure and temperature,pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration ofother chemical species is more difficult to measure, as it usually requires anat-line or off-line approach. Such approaches are invasive and slow compared toon-line sensing. It is known that different molecules can be distinguished bytheir interaction with monochromatic light, producing different profiles forthe resulting Raman spectrum, depending on the concentration. Given theavailability of reference measurements for the target variable, regressionmethods can be used to model the relationship between the profile of the Ramanspectra and the concentration of the analyte. This work focused on pretreatmentmethods of Raman spectra for the facilitation of the regression task usingMachine Learning and Deep Learning methods, as well as the development of newregression models based on these methods. In the majority of cases, thisallowed to outperform conventional Raman models in terms of prediction errorand prediction robustness.

Machine Learning and Deep Learning methods for predictive modelling from Raman spectra in bioprocessing