Noninvasive method for monitoring ethanol in fermentation processes using fiber-optic near-infrared spectroscopy.
ABSTRACT Short-wavelength near-infrared (SW-near-IR) spectroscopy (700-1100 nm) is used for the determination of ethanol during the time course of a fermentation. Measurements are performed noninvasively by means of a photodiode array spectrometer equipped with a fiber-optic probe placed on the outside of the glass-wall fermentation vessel. Pure ethanol/water and ethanol/yeast/water mixtures are studied to establish the spectral features that characterize ethanol and to show that determination of ethanol is independent of the yeast concentration. Analysis of the second-derivative data is accomplished with multilinear regression (MLR). The standard error of prediction (SEP) of ethanol in ethanol/water solutions is approximately 0.2% over a range of 0-15%; the SEP of ethanol in ethanol/yeast/water solutions is 0.27% (w/w). Results from the mixture experiments are then applied to actual yeast fermentations of glucose to ethanol. By use of a gas chromatographic method for validation, a good correlation is found between the intensity of backscattered light at 905 nm and the actual ethanol. Additional experiments show that a calibration model created for one fermentation can be used to predict ethanol production during the time course of others with a prediction error of 0.4%.
Article: In situ monitoring of an escherichia coli fermentation using a diamond composition ATR probe and mid-infrared spectroscopy[show abstract] [hide abstract]
ABSTRACT: A diamond composition ATR probe was used in situ to obtain IR spectra on replicate Escherichia coli fermentations involving a complex medium. The probe showed excellent stability over a 6-month operating period and was unaffected by either agitation or aeration. The formation of an unknown was observed from IR spectra obtained during the sterilization; subsequent experiments proved this to be a reaction product between yeast extract and the phosphates used as buffer salts. Partial-least-squares-based calibration/prediction models were developed for both glucose and acetate using in-process samples. The resulting models had prediction errors of +/-0.26 and +/-0.75 g/L for glucose and acetic acid, respectively, errors which were statistically equivalent to the estimated experimental errors in the reference measurements. Relative concentration profiles for the unknown formed during sterilization could be generated either by tracking peak height at an independent wavelength or by self-modeling curve resolution of the spectral region overlapping that of glucose. These profiles indicated that this compound was metabolized simultaneously with glucose; upon depletion of the glucose, when the microorganism switched to consumption of acetic acid, utilization continued but at a lower rate. The data presented provide an extensive characterization of the performance characteristics of this in situ analysis and clearly demonstrate its utility not just in the quantitative measurement of multiple known species but in the qualitative evaluation of unknown species.Biotechnology Progress 06/1999; 15(3):529-39. · 2.34 Impact Factor
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ABSTRACT: Fermentation process control is currently limited by its inability to measure parameters such as substrate, product, and biomass concentrations rapidly for consistent on-line feedback. Physical and chemical parameters, such as temperature and pH, currently can be obtained on-line using appropriate sensors. However, to obtain information on the concentration of the substrate, product, and biomass, samples must be taken off-line for measurement. With the use of spectroscopic techniques, real-time monitoring of process constituents such as product and substrate is possible. Spectroscopic techniques are rapid and nondestructive, require minimal or no sample preparation, and can be used to simultaneously assess several constituents in complex matrices. The production of ethanol is the largest fermentation process in terms of production volume and economic value as a result of its prominence in the food, agricultural, and fuel industries. This study attempts to develop an on-line ethanol fermentation monitoring technique using Fourier transform infrared (FTIR) spectroscopy with a flow-through ATR capability. Models developed using multivariate statistics, employed to obtain on-line FTIR measurements, were successfully validated by off-line HPLC analysis and spectrophotometry data. Standard errors of prediction (SEP) values of 0.985 g/L (R2 = 0.996), 1.386 g/L (R2 = 0.998), and 0.546 (R2 = 0.972) were obtained for ethanol, glucose, and OD, respectively. This work demonstrates that FTIR spectroscopy could be used for rapid on-line monitoring of fermentation.Biotechnology Progress 23(2):494-500. · 2.34 Impact Factor
Article: The effect of analyte concentration range on measurement errors obtained by NIR spectroscopy.[show abstract] [hide abstract]
ABSTRACT: Near infrared spectroscopy (NIRS) was employed to quantify five compounds, ammonium, glucose, glutamate, glutamine, and lactate, in conditions similar to those obtained in animal cell cultivations over varying ranges of analyte concentrations. These components represent the primary nutrients and wastes of animal cells for which such noninvasive monitoring schemes are required for development of accurate control schemes. Ideal cultivation conditions involve maintaining concentrations of these components as low as 1 mM each, however, it is not known if measurements of these compounds can be accurately accomplished at such a low level. We have found that NIRS measurements of these analytes over narrow and low (0-1 mM) concentration ranges yield measurement errors of roughly 11% of the concentration range. By contrast, wide concentration ranges (0-30 mM) yield measurement errors of roughly 1.6% of the concentration range. Decreasing the concentration range over which an analyte is quantified in four out of five cases decreases the optimal spectral range by 100 cm(-1) for measurement by partial least squares regression analysis. There appears a similarity in the ratio of (standard error of prediction (SEP)/concentration range) which may provide an estimation of the anticipated SEP to be obtained for measurement over a new concentration range. It was found that for the five analytes evaluated here, the ratio of SEP to concentration range divided by that obtained for a second concentration range is equal to a fairly constant value of 6.6. This relationship was found to be followed reasonably well by an extensive number of measurement results reported in the literature for similar conditions.Talanta 07/2000; 52(3):473-84. · 3.79 Impact Factor