Monitoring lignocellulosic bioethanol production processes using Raman spectroscopy

Iversen, Jens Asmus; Ahring, Birgitte Kiær

doi:10.1016/j.biortech.2014.08.068

Cited by 21 publications

(23 citation statements)

References 30 publications

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“…In a more recent publication by Freedman et al, Raman spectroscopy was used to differentiate between phenotypes of E. coli cultures enriched for 1-butanol tolerance 23 . Besides bacterial cells, Raman spectroscopy has also been used to study perfused liver fluids as part of conditioning for organ transplant 24 Cultures of S. cerevisiae [27][28][29] , of E. coli 30 , and of Chinese hamster ovary (CHO) cells 31 , have been monitored in real time by employing Raman spectroscopy and statistical analysis. The general approach is to apply chemometrics in order to develop calibrated models, which are verified using offline methods such as HPLC and GC-MS.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling inClostridium acetobutylicumfermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

Liu

Germane

et al. 2016

SPIE Proceedings

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The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the wellknown Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q 2 Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

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Section: Introductionmentioning

confidence: 99%

Predictive modeling inClostridium acetobutylicumfermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

Liu

Germane

et al. 2016

SPIE Proceedings

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“…Recently, several optical methods, including attenuated total reflectance‐Fourier transform Infrared (ATR‐FTIR) spectroscopy and Raman spectroscopy (RS), have been employed to evaluate the structural changes and composition of LB during pretreatment processes , . RS can be utilized to analyze viscous reaction mixtures without the interference of the water present in aqueous samples.…”

Section: Introductionmentioning

confidence: 99%

“…RS can be utilized to analyze viscous reaction mixtures without the interference of the water present in aqueous samples. In addition, RS has been applied as an alternative for the structural analysis of biomass during and after the pretreatment process and its posterior enzymatic hydrolysis . To date, few reports have quantified component concentrations with the identification of functional groups during biorefinery processes using RS .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, RS has been applied as an alternative for the structural analysis of biomass during and after the pretreatment process and its posterior enzymatic hydrolysis . To date, few reports have quantified component concentrations with the identification of functional groups during biorefinery processes using RS . However, with the recent advances in instruments with higher detector sensitivity and laser performance, the use is possible of RS with a high degree of accuracy, e.g., in the monitoring of enzymatic hydrolysis and yeast fermentation .…”

Section: Introductionmentioning

confidence: 99%

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Physicochemical Characterization of Wheat Straw during a Continuous Pretreatment Process

et al. 2018

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The changes occurring during the depolymerization of lignocellulosic biomasses are not yet fully understood. Synchrotron micro-Fourier transform infrared (m-FTIR), Raman spectroscopy (RS), X-ray diffraction (XRD), and X-ray fluorescence (XRF) were applied for better characterization of wheat straw fibers during a continuous pretreatment process in terms of conditioning (C), extrusion (E), steam explosion (SE), and enzymatic hydrolysis (EH). m-FTIR revealed functional groups as phenylpropanoid polymers, ethers, and aliphatic alcohol. RS detected acetoacetate, methyl and phenol groups after SE. The crystallinity index and silica content were also determined.

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“…Raman spectroscopy has proven to be a useful tool for studying microorganisms and mammalian cells due to its noninvasive nature and compatibility with water . Cultures of various organisms such as S accharomyces cerevisiae , E scherichia coli , and chinese hamster ovary cells have been studied in real time, employing Raman spectroscopy and statistical analysis. The approaches are to apply chemometrics to develop calibrated models, which are verified using off‐line methods such as high‐performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry.…”

Section: Introductionmentioning

confidence: 99%

Real‐time metabolite monitoring of glucose‐fed Clostridium acetobutylicum fermentations using Raman assisted metabolomics

Liu

Gerlach

et al. 2017

J Raman Spectroscopy

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Data obtained from in situ Raman spectroscopy probes and high‐performance liquid chromatography (HPLC) analysis were applied together with chemometrics to build partial least squares models of metabolite concentrations for the industrially relevant organism Clostridium acetobutylicum. Models were built for predominant products (acetic acid, butyric acid, and butanol) of C. acetobutylicum cultures grown on glucose as a substrate. The partial least squares models were then applied to a 3‐day C. acetobutylicum culture for real‐time, quantitative metabolite analysis. The predicted outcomes of new fermentation cultures were validated by analyzing HPLC data from corresponding experiments from these new fermentation cultures. Model predictions showed good correlation with measured data (goodness of fit [R2Y] values of 0.99, and goodness of prediction [Q2Y] values of 0.98 from agitated cultures. Predictive models based upon Raman spectral data are promising tools for characterization of synthetic organisms, guiding process control, and facilitating optimization of culture conditions.

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Monitoring lignocellulosic bioethanol production processes using Raman spectroscopy

Cited by 21 publications

References 30 publications

Predictive modeling inClostridium acetobutylicumfermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

Predictive modeling inClostridium acetobutylicumfermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

Physicochemical Characterization of Wheat Straw during a Continuous Pretreatment Process

Real‐time metabolite monitoring of glucose‐fed Clostridium acetobutylicum fermentations using Raman assisted metabolomics

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