Kinetic analysis for batch ethanol fermentation of Saccharomyces cerevisiae

Nanba, Akira; Tsuchiya, Yoshinobu; Nagai, Shiro

doi:10.1016/0385-6380(87)90088-4

Cited by 25 publications

(13 citation statements)

References 10 publications

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“…Caro et al also developed a similar mechanistic model for grape must fermentation which took into account other microbial functions, such as respiration and synthesis of products other than ethanol (8). Nanba et al developed a kinetic wine fermentation model that incorporated yeast sensitivity to ethanol and temperature effects of yeast cell growth parameters (21). Other researchers have also developed more empirical models within an enological context (4,13,16).…”

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confidence: 99%

Temperature-Dependent Kinetic Model for Nitrogen-Limited Wine Fermentations

Coleman¹,

Fish

Block

2007

Appl Environ Microbiol

104

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A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35°C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Mententype constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35°C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.The progress of a wine fermentation is determined by the concentration of residual sugar. Problem fermentations occur when the completion of sugar utilization exceeds 7 to 10 days (sluggish fermentations) or when sugar utilization ceases with more than 0.4% wt/vol residual sugar still present in the wine (stuck fermentations) (5). Sluggish and stuck wine fermentations are often associated with musts that contain inadequate nutrients. The primary nutrient associated with these problem fermentations is nitrogen. The minimum concentration of required nitrogen in order to complete fermentation may be dependent upon other factors, such as temperature and initial sugar concentration. More comprehensive reviews of all known factors that lead to problem fermentations are available (1,5,6,14); however, in this work, to predict problem fermentations, we concentrated on developing a comprehensive model that includes the three main factors of temperature and initial nitrogen and sugar concentrations.These three variables are well-known critical factors for determining the kinetics of wine fermentations. Ough and Amerine performed extensive studies to characterize the effects of temperature on cell growth, sugar utilization, and ethanol production (25,26). Others have studied yeast (Saccharomyces cerevisiae) sensitivity to ethanol at various temperatures (22,27,28). In addition, insufficient nitrogen has been well documented as an important factor that leads to stuck and sluggish fermentations (1,2,5,14,15,2...

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confidence: 99%

Temperature-Dependent Kinetic Model for Nitrogen-Limited Wine Fermentations

Coleman¹,

Fish

Block

2007

Appl Environ Microbiol

104

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“…b Determined by fitting calculated results with experimental data (Curto et al, 1995b;Luong, 1985;Nanba et al, 1987). c Estimated on basis of the reference data (Curto et al, 1995b;Luong, 1985;Nanba et al, 1987). relevant enzyme activity at t = 0 is larger at a certain time point than that without any change in the same enzyme activity. In contrast, a negative instantaneous BR indicator means that the desired metabolite concentration changed in response to an increase in a relevant enzyme activity at t = 0 is smaller at a certain time point than that without any change in the same enzyme activity.…”

Section: Kinetic Ordersmentioning

confidence: 99%

“…All parameter values other than the values marked with a and b are given in the references (Curto et al, 1995b;Luong, 1985;Nanba et al, 1987). b Determined by fitting calculated results with experimental data (Curto et al, 1995b;Luong, 1985;Nanba et al, 1987).…”

Section: Kinetic Ordersmentioning

confidence: 99%

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Identification of bottleneck enzymes with negative dynamic sensitivities: Ethanol fermentation systems as case studies

Sriyudthsak

Shiraishi

2010

Journal of Biotechnology

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“…It is known that metabolic activities, especially specific growth rate, decreases by a first-order process. 23 As this model may be applicable to change in colour, a specific rate of oxidation of pigment ( ) is assumed to be a first-order reaction:…”

Section: Theoretical Considerationsmentioning

confidence: 99%