Monitoring fermentation processes using in‐process measurements of different orders

Svendsen, Carina; Skov, Thomas; Berg, Frans van den

doi:10.1002/jctb.4483

Cited by 14 publications

(16 citation statements)

References 33 publications

(54 reference statements)

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“…Another model that takes into consideration the inhibitory effect of the biomass on the specific growth rate is Luong model: (22) where K i is the inhibitory constant and S max is the substrate concentration at which the microorganisms are in a stationary growth phase.…”

Section: Contois Modelmentioning

confidence: 99%

“…The models developed for these cases consequently have various domains of applications but none of them include the whole oenological aspects of the process. The majority of the models are of "knowledge-based" models type and they take into consideration a great number of phenomena that have an important effect on the kinetics of the process fermentation [22].…”

Section: Study Case-modeling Of a White Wine Alcoholic Fermentation Pmentioning

confidence: 99%

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Current State and Perspective in the Models Applicable to Oenology

Şipoş¹

2018

Grapes and Wines - Advances in Production, Processing, Analysis and Valorization

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Modeling, simulation, and control of the alcoholic fermentation of grape juice into wine are still not a totally resolved problem. A model that makes it possible to predict alcoholic fermentation development would be a valuable instrument to its technical and economical implications. Considering the field of bioprocess used in food production at the industrial scale, the chapter will be centered on models applicable to oenology. On the first part, the chapter proposes the different approaches that have been taken: "knowledge-based" models, non-physiological mathematical descriptions, behavior prediction models, and empirical models. The second part will deal with a nonlinear model for white wine alcoholic fermentation process which, besides the detailed kinetic model, involves equations corresponding to the physiological phases of yeast cells, the inhibitory effect of ethanol, heat transfer equations, and the dependence of kinetic parameters on temperature.

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Section: Contois Modelmentioning

confidence: 99%

Section: Study Case-modeling Of a White Wine Alcoholic Fermentation Pmentioning

confidence: 99%

Current State and Perspective in the Models Applicable to Oenology

Şipoş¹

2018

Grapes and Wines - Advances in Production, Processing, Analysis and Valorization

View full text Add to dashboard Cite

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“…For direct analysis of the liquid phase, near-infrared (NIR) spectroscopy is much more widely applied in bioprocess and fermentation monitoring [21][22][23][24] due to the much weaker absorption profile of water in this region. However, the spectral features observed in NIR spectroscopy are overtone or combination transitions that are also weaker, typically by at least an order of magnitude compared with the corresponding fundamental transitions, reducing the inherent sensitivity.…”

Section: Introductionmentioning

confidence: 99%

On-line analysis and in situ pH monitoring of mixed acid fermentation by Escherichia coli using combined FTIR and Raman techniques

2020

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We introduce an experimental setup allowing continuous monitoring of bacterial fermentation processes by simultaneous optical density (OD) measurements, long-path FTIR headspace monitoring of CO2, acetaldehyde and ethanol, and liquid Raman spectroscopy of acetate, formate, and phosphate anions, without sampling. We discuss which spectral features are best suited for detection, and how to obtain partial pressures and concentrations by integrations and least squares fitting of spectral features. Noise equivalent detection limits are about 2.6 mM for acetate and 3.6 mM for formate at 5 min integration time, improving to 0.75 mM for acetate and 1.0 mM for formate at 1 h integration. The analytical range extends to at least 1 M with a standard deviation of percentage error of about 8%. The measurement of the anions of the phosphate buffer allows the spectroscopic, in situ determination of the pH of the bacterial suspension via a modified Henderson-Hasselbalch equation in the 6–8 pH range with an accuracy better than 0.1. The 4 m White cell FTIR measurements provide noise equivalent detection limits of 0.21 μbar for acetaldehyde and 0.26 μbar for ethanol in the gas phase, corresponding to 3.2 μM acetaldehyde and 22 μM ethanol in solution, using Henry’s law. The analytical dynamic range exceeds 1 mbar ethanol corresponding to 85 mM in solution. As an application example, the mixed acid fermentation of Escherichia coli is studied. The production of CO2, ethanol, acetaldehyde, acids such as formate and acetate, and the changes in pH are discussed in the context of the mixed acid fermentation pathways. Formate decomposition into CO2 and H2 is found to be governed by a zeroth-order kinetic rate law, showing that adding exogenous formate to a bioreactor with E. coli is expected to have no beneficial effect on the rate of formate decomposition and biohydrogen production.

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“…The next set of papers discusses applications of the various analytical tools towards creating PAT applications for upstream unit operations such as microbial fermentation and mammalian cell culture. The contribution from Ashton et al investigates the applicability of UV resonance Raman (UVRR) spectroscopy as a probe for residual cellular DNA and RNA in mammalian cell culture medium.…”

mentioning

confidence: 99%

“…Variations in DNA and RNA UVRR spectral profiles of medium‐cellular footprint samples were identified and related to time of harvest and increased cell lysis that is associated with a loss in cell viability. Svendsen et al present a case study where a lactic fermentation process is monitored by different techniques (BRIX, NIR and Fluorescence Spectroscopy) providing different data structures (zero‐, first‐ and second‐order measurements structures). Multivariate data analysis (PCA and PARAFAC) has then been applied on the first‐ and second‐order datasets.…”

mentioning

confidence: 99%