ArticleBioprocess and biosystems engineering2019
On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development.
Article in Bioprocess and biosystems engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bioprocess Engineering for Cultivated Meat.Advances in biochemical engineering/biotechnology · 2026Review
- Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production.Biotechnology for biofuels and bioproducts · 2025Article
- Development of automated metabolite control using mid-infrared probe for bioprocesses and vaccine manufacturing.Journal of industrial microbiology & biotechnology · 2024Article
- Process engineering of natural killer cell-based immunotherapy.Trends in biotechnology · 2023Review
- rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing.Bioengineering (Basel, Switzerland) · 2023Review
- Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production.Bioresources and bioprocessing · 2021Article
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Authors and funding
3 authors.
Funding
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Abstract
NIR spectroscopy is a non-destructive tool for in-situ, on-line bioprocess monitoring. One of its most frequent applications is the determination of metabolites during cultivation, especially glucose. Previous studies have usually investigated the applicability of Near Infrared (NIR) spectroscopy at one bioreactor scale but the effect of scale up was not explored. In this study, the complete scale up from shake flask (1 L) through 20 L, 100 L and 1000 L up to 5000 L bioreactor volume level was monitored with on-line NIR spectroscopy. The differences between runs and scales were examined using principal component analysis. The bioreactor runs were relatively similar regardless of scales but the shake flasks differed strongly from bioreactor runs. The glucose concentration throughout five 5000 L scale bioreactor runs were predicted by partial least squares regression models that were based on pre-processed spectra of bioreactor runs and combinations of them. The model that produced the lowest error of prediction (4.18 mM on a 29 mM concentration range) for all five runs in the prediction set was based on the combination of 20 L and 100 L data. This result demonstrated the capabilities and the limitations of an NIR system for glucose monitoring in mammalian cell cultivations.
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