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Dong X, Yan X, Wan Y, Gao D, Jiao J, Wang H, Qu H. Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method. Biotechnol Bioeng 2024; 121:1831-1845. [PMID: 38454569 DOI: 10.1002/bit.28688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/18/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.
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Affiliation(s)
- Xiaoxiao Dong
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Yuxiang Wan
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Dong Gao
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Jingyu Jiao
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Haibin Wang
- Hisun Biopharmaceutical Co. Ltd., Hangzhou, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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Dzurendova S, Olsen PM, Byrtusová D, Tafintseva V, Shapaval V, Horn SJ, Kohler A, Szotkowski M, Marova I, Zimmermann B. Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms. Microb Cell Fact 2023; 22:261. [PMID: 38110983 PMCID: PMC10729511 DOI: 10.1186/s12934-023-02268-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/10/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. RESULTS The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94-0.99 and 0.89-0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. CONCLUSIONS The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.
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Affiliation(s)
- Simona Dzurendova
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway
| | - Pernille Margrethe Olsen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Dana Byrtusová
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway
| | - Valeria Tafintseva
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway
| | - Volha Shapaval
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway
| | - Svein Jarle Horn
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
| | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway
| | - Martin Szotkowski
- Institute of Food Science and Biotechnology, Faculty of Chemistry, Brno University of Technology, Purkyňova 464/118, Brno, 61200, Czech Republic
| | - Ivana Marova
- Institute of Food Science and Biotechnology, Faculty of Chemistry, Brno University of Technology, Purkyňova 464/118, Brno, 61200, Czech Republic
| | - Boris Zimmermann
- Faculty of Science and Technology, Norwegian University of Life Sciences, Drøbakveien 31, P.O. Box 5003, 1432, Ås, Norway.
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Zainal PW, Syukri D, Fahmy K, Imaizumi T, Thammawong M, Tsuta M, Nagata M, Nakano K. Lipidomic Profiling to Assess the Freshness of Stored Cabbage. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02422-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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