Optimization of whey fermentation in a jar fermenter

Moresi, Mauro; Colicchio, Alberto; Sansovini, Fabio

doi:10.1007/bf00504483

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…In particular, it was shown that a feed scheme using lactose concentrations greater than 30 g dm-3 results in lower biomass yields and greater accumulation of metabolic products, while with feed concentrations of lactose as low as 1 5 g d m -3 it is easier to satisfy the microbial oxygen demand rate and minimise the aforementioned inhibitory effect, thus confirming previous results performed batchwise in a laboratory-fermenter under similar oxygen transfer rates. 4 In conclusion, since the lactose feed scheme was found to be of crucial importance in obtaining high biomass yields, such a mathematical model might be used to optimise the feed scheme and oxygen transfer on the assumption that cell growth is limited by neither oxygen nor nitrogen availability and controlled by lactose level only, by carrying out a cost/benefit analysis aimed at minimising yeast production …”

Section: Discussionmentioning

confidence: 99%

Kinetics of continuous whey fermentation by Kluyveromyces fragilis

Moresi

Trunfio²,

Parente

1990

J of Chemical Tech & Biotech

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Section: Discussionmentioning

confidence: 99%

Kinetics of continuous whey fermentation by Kluyveromyces fragilis

Moresi

Trunfio²,

Parente

1990

J of Chemical Tech & Biotech

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“…RSM is suitable for identifying the effect of individual variables and for efficiently seeking the conditions for an optimum response in a multivariable system (Montgomery, 2001). This method has been successfully applied to optimize fermentation processes (Maddox and Reichert, 1997;Chen, 1981;Grothe et al, 1999;Ratnam et al, 2003;Casas López et al, 2004), vegetable oil bioconversion (Cheynier et al, 1983), biomass production (Moresi et al, 1980), and numerous other processes (Panda et al, 1999;Ellaiah et al, 2002;Adinarayaan and Suren, 2005). Effects of temperature, pH, and enzyme dosage on sago starch hydrolysis using bacterial a-amylase, b-glucanase, and pullulanase have been previously studied using statistical design of experiment (Gorinstein et al, 1994).…”

Section: Introductionmentioning

confidence: 96%

Enzyme-Mediated Production of Sugars From Sago Starch: Statistical Process Optimization

Wee

Annuar

Ibrahim

et al. 2011

Chemical Engineering Communications

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Glucoamylase (c-amylase, EC 3.2.1.3) from Aspergillus niger was used to hydrolyze the soluble sago starch to reducing sugars without any major pretreatment of the substrate. A 2 L stirred tank reactor was used for the hydrolysis. The effects of pH, temperature, agitation speed, substrate concentration, and enzyme concentration on the reaction were investigated in order to maximize both the initial reaction velocity v and the final product yield Y p=s . A response surface methodology central composite design was used for the optimization. A maximum Y p=s of 0.58 g Á g À1 and a high v of 0.50 mmoles Á L À1 Á min À1 were predicted by the response surface at the identified optimal conditions (61 C, a substrate concentration of 0.1% (w=v, g=100 mL), an enzyme concentration of 0.2 U Á mL À1 ). The pH and agitation speed did not significantly affect the production of sugars. The subsequent validation experiments under the above-specified optimal conditions confirmed a maximum conversion rate and yield combination of 0.51 AE 0.07 mmoles Á L À1 Á min À1 and 0.60 AE 0.08 g Á g À1 .

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“…It aids the process by identifying the effect of individual variables and thus seeking the optimum conditions for them. RSM has displayed successful applications in various processes like optimisation of alcoholic fermentation , optimisation of vegetable oil bioconversion , biomass production and enhancement of enzyme production . The RSM design applies the central composite rotatable design (CCRD) and estimation of coefficients for a model equation is one of its applications.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of fermentation process for production of black rice wine and evaluation of its phenolic and volatile compounds

2018

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A statistical model was developed to describe the fermentation process using Saccharomyces cerevisiae ATCC 9763 in the production of black rice (cv. Poireton) wine from Manipur, India. Fermentation process parameters were optimised by response surface methodology and experiments were conducted according to a three‐level and three‐variable central composite rotatable design. The combination of pH, temperature and time was the most promising for achieving a high ethanol yield. With the use of the developed quadratic model equation, alcohol production (11.1%, v/v) was obtained in a batch fermentation process at the optimum operating condition of pH (6.96), temperature (26.2°C) and incubation time (74 h). GC–MS analysis of the wine revealed volatile aromatic compounds, namely 1‐methyl‐2‐pyrrolidine ethanol, propionic acid, 3‐mercapto and ethyl ester. © 2018 The Institute of Brewing & Distilling

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Optimization of whey fermentation in a jar fermenter

Cited by 24 publications

References 15 publications

Kinetics of continuous whey fermentation by Kluyveromyces fragilis

Kinetics of continuous whey fermentation by Kluyveromyces fragilis

Enzyme-Mediated Production of Sugars From Sago Starch: Statistical Process Optimization

Optimisation of fermentation process for production of black rice wine and evaluation of its phenolic and volatile compounds

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