Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production
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| Publicat a: | Biotechnology for Biofuels and Bioproducts vol. 18 (2025), p. 1 |
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| Altres autors: | , , |
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Nature Publishing Group
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| 001 | 3216563324 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2731-3654 | ||
| 022 | |a 1754-6834 | ||
| 024 | 7 | |a 10.1186/s13068-025-02662-1 |2 doi | |
| 035 | |a 3216563324 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 113017 |2 nlm | ||
| 100 | 1 | |a Tillman, Zofia | |
| 245 | 1 | |a Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis. These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation.ResultsIn this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix.ConclusionWe have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS. | |
| 653 | |a Zymomonas mobilis | ||
| 653 | |a Spectrometers | ||
| 653 | |a Xylose | ||
| 653 | |a Samples | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Metabolism | ||
| 653 | |a Sampling | ||
| 653 | |a Feasibility studies | ||
| 653 | |a Monitoring | ||
| 653 | |a Infrared spectra | ||
| 653 | |a Glucose | ||
| 653 | |a Fermentation | ||
| 653 | |a Process control | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Experiments | ||
| 653 | |a Infrared spectroscopy | ||
| 653 | |a Glycerol | ||
| 653 | |a Butanediol | ||
| 653 | |a Process controls | ||
| 653 | |a Real time | ||
| 653 | |a Lignocellulose | ||
| 653 | |a Software | ||
| 653 | |a Economic importance | ||
| 653 | |a Regression models | ||
| 653 | |a Laboratories | ||
| 653 | |a Spectral signatures | ||
| 653 | |a Yeast | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Low cost | ||
| 653 | |a Acetoin | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Near infrared radiation | ||
| 653 | |a Signal to noise ratio | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Peterson, Darren J | |
| 700 | 1 | |a Dowe, Nancy | |
| 700 | 1 | |a Wolfrum, Ed | |
| 773 | 0 | |t Biotechnology for Biofuels and Bioproducts |g vol. 18 (2025), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3216563324/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3216563324/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3216563324/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |