ArticleSensors (Basel, Switzerland)2022
Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Raman spectroscopy enables non-destructive quantification of nitrate in C3 and C4 plants.Frontiers in plant science · 2026Article
- Calibration Transfer Across Instrument Vendors for Bioprocess Raman Monitoring.The AAPS journal · 2025Article
- Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.Bioprocess and biosystems engineering · 2025Article
- Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells.Metabolic engineering communications · 2024Review
- Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning.Scientific reports · 2023Article
- Halfway to Automated Feeding of Chinese Hamster Ovary Cells.Sensors (Basel, Switzerland) · 2023Article
- Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives.Metabolomics : Official journal of the Metabolomic Society · 2023Review
- Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha.Journal of biological engineering · 2023Article
- Innovative methodology for flexible monitoring of various bioprocesses by using pre-existing Raman data coupled with automated transfer learning technique.Biotechnology progressArticle
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
Abstract
Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.