Lactic Acid Bacteria Biomass Monitoring in Highly Conductive Media by Permittivity Measurements

Arnoux, A.S.; Preziosi-Belloy, Laurence; Esteban, Geoffrey; Teissier, Philippe; Ghommidh, C.

doi:10.1007/s10529-005-1781-2

Cited by 35 publications

(20 citation statements)

References 11 publications

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“…There are extensive reports of its use in monitoring bacterial, fungal, and animal cultures, with most investigators reporting satisfactory correlations between the permittivity predicted viable biomass and standard offline measurements during the growth phase of a culture. [18][19][20][21][22][23][24] Permittivity has been particularly advantageous in monitoring the biomass of cultures that are otherwise difficult to characterize, such as those with high amounts of insoluble matter, 18 heavily flocculating cultures, 25 and spore-forming cultures. 26 In mammalian cultures, permittivity has been used for biomass monitoring, 27,28 specific rate calculations, 29 and bioreactor control.…”

Section: Eðf þ ¼mentioning

confidence: 99%

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy

Opel

Amanullah

2010

Biotechnology Progress

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Dielectric spectroscopy was used to analyze typical batch and fed-batch CHO cell culture processes. Three methods of analysis (linear modeling, Cole-Cole modeling, and partial least squares regression), were used to correlate the spectroscopic data with routine biomass measurements [viable packed cell volume, viable cell concentration (VCC), cell size, and oxygen uptake rate (OUR)]. All three models predicted offline biomass measurements accurately during the growth phase of the cultures. However, during the stationary and decline phases of the cultures, the models decreased in accuracy to varying degrees. Offline cell radius measurements were unsuccessfully used to correct for the deviations from the linear model, indicating that physiological changes affecting permittivity were occurring. The beta-dispersion was analyzed using the Cole-Cole distribution parameters Deltaepsilon (magnitude of the permittivity drop), f(c) (critical frequency), and alpha (Cole-Cole parameter). Furthermore, the dielectric parameters static internal conductivity (sigma(i)) and membrane capacitance per area (C(m)) were calculated for the cultures. Finally, the relationship between permittivity, OUR, and VCC was examined, demonstrating how the definition of viability is critical when analyzing biomass online. The results indicate that the common assumptions of constant size and dielectric properties used in dielectric analysis are not always valid during later phases of cell culture processes. The findings also demonstrate that dielectric spectroscopy, while not a substitute for VCC, is a complementary measurement of viable biomass, providing useful auxiliary information about the physiological state of a culture.

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Section: Eðf þ ¼mentioning

confidence: 99%

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy

Opel

Amanullah

2010

Biotechnology Progress

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“…Online DE spectroscopy has mainly been used for the monitoring of biomass concentrations in cultivations with unicellular organisms [2,3,6,21,26,28,36]. In filamentous systems, good correlations between biomass concentration and capacitance have been obtained online in the exponential, transition, and stationary growth phases [24,31].…”

Section: Introductionmentioning

confidence: 99%

Introducing process analytical technology (PAT) in filamentous cultivation process development: comparison of advanced online sensors for biomass measurement

Rønnest

Stocks

Lantz

et al. 2011

J Ind Microbiol Biotechnol

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The recent process analytical technology (PAT) initiative has put an increased focus on online sensors to generate process-relevant information in real time. Specifically for fermentation, however, introduction of online sensors is often far from straightforward, and online measurement of biomass is one of the best examples. The purpose of this study was therefore to compare the performance of various online biomass sensors, and secondly to demonstrate their use in early development of a filamentous cultivation process. Eight Streptomyces coelicolor fed-batch cultivations were run as part of process development in which the pH, the feeding strategy, and the medium composition were varied. The cultivations were monitored in situ using multi-wavelength fluorescence (MWF) spectroscopy, scanning dielectric (DE) spectroscopy, and turbidity measurements. In addition, we logged all of the classical cultivation data, such as the carbon dioxide evolution rate (CER) and the concentration of dissolved oxygen. Prediction models for the biomass concentrations were estimated on the basis of the individual sensors and on combinations of the sensors. The results showed that the more advanced sensors based on MWF and scanning DE spectroscopy did not offer any advantages over the simpler sensors based on dual frequency DE spectroscopy, turbidity, and CER measurements for prediction of biomass concentration. By combining CER, DE spectroscopy, and turbidity measurements, the prediction error was reduced to 1.5 g/l, corresponding to 6% of the covered biomass range. Moreover, by using multiple sensors it was possible to check the quality of the individual predictions and switch between the sensors in real time.

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“…The Lb. casei investigation showed fairly linear correlation of both probe responses up to an optical density of 1.5 [4]. The correlation was not linear in the B. thuringiensis process, where maximum optical density was around 20 [49,50].…”

Section: Optical Probesmentioning

confidence: 88%

“…However, the change in the calibration equation is expected, if the individual probe was changed between the experiments or the probe geometry changed. The Biomass System by Fogale Nanotech yielded an oZine calibration of 0.096 § 0.008 pF cm ¡1 (g l ¡1 ) ¡1 and an online correlation of 0.090 § 0.005 pF cm ¡1 (g l ¡1 ) ¡1 for Lactobacillus casei [4], but the measurements were done at a frequency so high (5.7 MHz) that the -dispersion theory does not support the reliability of the results. Commercial electrodes have exhibited polarization problems, when the biomass concentration is low and when the conductivity is high [70].…”

Section: Dielectric Spectroscopymentioning

confidence: 95%

Biomass measurement online: the performance of in situ measurements and software sensors

Kiviharju

Salonen

Moilanen

et al. 2008

J Ind Microbiol Biotechnol

150

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Biomass measurement is one of the most critical measurements in biotechnological processes. The technologies developed for the measurement of biomass in situ have developed over the years. Because it has been over 10 years since the last review concentrating on practical issues concerning biomass measurements, it is time to evaluate recent developments in the Weld. This review concentrates on the applications of dielectric spectroscopy, optical density, infrared spectroscopy, and Xuorescence for in situ measurement of biomass. The advantages oVered by these methods and an economic way of estimating biomass concentration, the software sensors, are considered.

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Lactic Acid Bacteria Biomass Monitoring in Highly Conductive Media by Permittivity Measurements

Cited by 35 publications

References 11 publications

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy

Introducing process analytical technology (PAT) in filamentous cultivation process development: comparison of advanced online sensors for biomass measurement

Biomass measurement online: the performance of in situ measurements and software sensors

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