Near-Infrared Spectroscopic Determination of Acetate, Ammonium, Biomass, and Glycerol in an Industrial <i>Escherichia Coli</i> Fermentation

Hall, Jeffrey W.; McNeil, Brian; Rollins, M. J.; Draper, I.; Thompson, B. G.; Macaloney, Graeme

doi:10.1366/0003702963906726

Cited by 110 publications

(99 citation statements)

References 13 publications

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“…Sample spectra acquired with the NIR sensor are shown in Figure 2. It is noted that the spectra do not provide information in a clear manner, as NIR spectra are characterized by overlapping absorption bands and light scattering differences caused by increases in cell concentration (Hall et al, 1996, Nishii et al, 2012. To enhance spectral features and to compensate for baseline shifts and light scattering differences, the original spectra need to be subjected to pretreatments before being used to construct calibration models (Finn et al, 2006).…”

Section: Resultsmentioning

confidence: 99%

Real time and in situ Near-Infrared Spectroscopy (Nirs) for Quantitative Monitoring of Biomass, Glucose, Ethanol and Glycerine concentrations in an alcoholic fermentation

Nascimento

Macêdo

Santos

et al. 2017

Braz. J. Chem. Eng.

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-This study investigates the application of NIR spectroscopy for real-time monitoring of process parameters in alcoholic fermentations. For this purpose, fermentation batches using Saccharomyces cerevisiae were carried out and monitored in situ by an immersed NIRS probe. Samples were collected throughout the fermentations, and concentrations of biomass, glucose, ethanol and glycerin were analyzed by reference methods and multivariate data analyses such as PCA and PLSR. PCA was used to investigate data variability and to select samples for calibration and for external validation sets. A PLSR model for biomass yielded R 2 and RMSEP values of 0.99 and 0.276 g.L -1 , respectively. For glucose, the carbon source, the PLSR model yielded an R 2 of 0.95 and an RMSEP of 7.14 g.L -1 . The PLSR model for ethanol was characterized by an R 2 of 0.95 and an RMSEP of 0.06 g.L -1 . For glycerin, a highly accurate PLSR model with an R 2 of 0.98 and an RMSEP of 0.116 g.L-1 was obtained. These results indicate that an in situ NIRS probe is suitable for real-time monitoring of important parameters in alcoholic fermentations.

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Section: Resultsmentioning

confidence: 99%

Real time and in situ Near-Infrared Spectroscopy (Nirs) for Quantitative Monitoring of Biomass, Glucose, Ethanol and Glycerine concentrations in an alcoholic fermentation

Nascimento

Macêdo

Santos

et al. 2017

Braz. J. Chem. Eng.

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“…Hall et al 12 monitored the concentrations of acetate and ammonia and the cell density in Escherichia coli fermentations. Monitoring errors over large concentration ranges were approximately 7 mM for each analyte with an MPE of 1-3%.…”

Section: %mentioning

confidence: 99%

Matrix-Enhanced Calibration Procedure for Multivariate Calibration Models with Near-Infrared Spectra

1998

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A novel method is introduced for developing calibration models for the spectroscopic measurement of chemical concentrations in an aqueous environment. To demonstrate this matrix-enhanced calibration procedure, we developed calibration models to quantitate glucose and glutamine concentrations in an insect cell culture medium that is a complex mixture of more than 20 components, with three components that manifest significant concentration changes. Accurate calibration models were generated for glucose and glutamine by using a calibration data set composed of 60 samples containing the analytes dissolved in an aqueous buffer along with as few as two samples of the analytes dissolved in culture medium. Standard errors of prediction were 1.0 mM for glucose and 0.35 mM for glutamine. The matrix-enhanced method was also applied to culture medium samples collected during the course of a second bioreactor run. Addition of three culture medium samples to a buffer calibration reduced glucose prediction errors from 3.8 mM to 1.0 mM; addition of two culture medium samples reduced glutamine prediction errors from 1.6 mM to 0.76 mM. Results from this study suggest that spectroscopic calibration models can be developed from a relatively simple set of samples provided that some account for variations in the sample matrix.

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“…The analytical utility of NIR spectroscopy is established for measuring millimolar levels of various biological species in simulated matrices Burmeister et al, 1998;Burmeister and Arnold, 1999) and natural matrices from clinical chemistry (Hazen et al, 1998), fermentation (Cavinato et al, 1990;Ge et al, 1994;Hall et al, 1996;Yano et al, 1998), and cell culture Lewis et al, 2000;McShane and CoteÂ , 1998;Riley et al, 1997Riley et al, , 1998aRiley et al, , 1998bZhou et al, 1995) systems. The chemical information originates from overtone and combination transitions associated with fundamental C±H, O±H, and N±H vibrations.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

Rhiel

Cohen

Murhammer

et al. 2001

Biotech & Bioengineering

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An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800-to 4200-cm )1 combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.

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Near-Infrared Spectroscopic Determination of Acetate, Ammonium, Biomass, and Glycerol in an Industrial Escherichia Coli Fermentation

Cited by 110 publications

References 13 publications

Real time and in situ Near-Infrared Spectroscopy (Nirs) for Quantitative Monitoring of Biomass, Glucose, Ethanol and Glycerine concentrations in an alcoholic fermentation

Real time and in situ Near-Infrared Spectroscopy (Nirs) for Quantitative Monitoring of Biomass, Glucose, Ethanol and Glycerine concentrations in an alcoholic fermentation

Matrix-Enhanced Calibration Procedure for Multivariate Calibration Models with Near-Infrared Spectra

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

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