User-independent nonlinear modeling using adjusted spline-interpolated knots (UNMASK) and indirect hard modeling for deriving compositions from spectra with background signals

Woehl, J.; Oleksiyuk, Ivan; Bahr, Leo Alexander; Koß, Hans-Jürgen

doi:10.1016/j.chemolab.2023.104851

Cited by 1 publication

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since light is rarely scattered inelastically, the Raman signal is weak and can easily be superimposed by noise or fluorescence signals (McCreery, 2000;Smulko et al, 2014;Vandenabeele, 2013). Fluorescence signals are a common challenge in the evaluation of Raman spectra, but in literature various experimental, instrumental, and computational measures were discussed to overcome this challenge and challenges originating from other background signals (Adami & Kiefer, 2013;Beumers et al, 2018;Cadusch et al, 2013;Esmonde-White et al, 2022;Kögler & Heilala, 2020;Müller et al, 2023;Woehl et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Bioprocess in‐line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM)

Müller,

Börger,

Thien

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

Process in‐line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in‐line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state‐of‐the‐art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non‐invasive in‐line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM‐based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in‐line information as input of a simple on–off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

show abstract