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Calibration of multiplexed fiber-optic spectroscopy
Zeng-Ping Chen1, Li-Jing Zhong, Alison Nordon
1State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, Hunan, P.R. China. zpchen2002@hotmail.com
Analytical Chemistry
|March 9, 2011
Summary
New calibration models address probe variations in multiplexed spectroscopy for bioprocess monitoring. This dual calibration strategy improves accuracy in analyzing spectral data from multiple bioreactors.
Area of Science:
- Analytical Chemistry
- Biotechnology
- Process Analytical Technology (PAT)
Background:
- Biopharmaceutical manufacturing often uses multiple bioreactors in parallel for large-scale production.
- In situ monitoring via multiplexed fiber-optic spectroscopy generates complex spectral data.
- Variations in optical probe properties introduce significant noise, hindering accurate analysis with standard methods.
Purpose of the Study:
- To develop a novel calibration model for analyzing spectral data from multiplexed probes in bioprocesses.
- To mitigate the impact of probe-specific optical variations on spectral data accuracy.
- To enhance the reliability of in situ monitoring in large-scale biomanufacturing.
Main Methods:
- Proposed a new calibration model explicitly incorporating multiplicative parameters for each optical probe.
- Implemented a "dual calibration" strategy to counteract probe-induced spectral variations.
- Validated the model on MIR ternary mixture data and NIR bioprocess data sets.
Main Results:
- The proposed multiplex calibration model effectively mitigated detrimental effects from probe differences.
- Achieved significantly more accurate predictions compared to standard multivariate linear calibration models (e.g., PLS).
- Demonstrated superior performance over empirical preprocessing methods like OSC, SNV, and MSC.
Conclusions:
- The novel calibration model offers a robust solution for analyzing complex spectral data from multiplexed probes.
- This approach improves the accuracy and reliability of in situ bioprocess monitoring.
- Enables more efficient and precise control of large-scale biopharmaceutical manufacturing processes.
