Strategies for improving fermentation medium performance: a review

Kennedy, Max J.; Krouse, Donal

doi:10.1038/sj.jim.2900755

Cited by 272 publications

(164 citation statements)

References 106 publications

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“…When the ANN models were compared to the general MLR models (Eqs. 5,6), biomass data were predicted with higher R 2 by the ANN model (Tables 5, 7). The performance of ANN models in terms of R 2 are better than those of RSM models, which were performed beforehand.…”

Section: Ann Modelingmentioning

confidence: 97%

“…The modeling of experimental or real-time data with artificial neural network (ANN) techniques has been applied in many research fields, such as biotechnology, pharmaceutical, medicine and food [1][2][3][4][5][6][7][8]. In the biotechnology field, ANN modeling is a commonly applied approach for carrying out estimations, control studies, classification analysis, and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

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Tarı

Ünlütürk

et al. 2009

J Ind Microbiol Biotechnol

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Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-offit of the regression and ANN models was measured by the R 2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 value of 0.83, whereas the regression models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 of 0.69.

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Section: Ann Modelingmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

Tokatlı

Tarı

Ünlütürk

et al. 2009

J Ind Microbiol Biotechnol

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show abstract

“…However, the screening approach applied, in which a single factor was changed while keeping all other factors constant, is unsuitable for identifying interactions among environmental factors. A full factorial design, on the other hand, is effective in assessing the contribution of a single environmental factor on the response studied as well as possible interaction effects between these factors (Lundstedt et al, 1998;Kennedy & Krouse, 1999). In this type of design, each factor is considered at two or more levels and the experiments are Table 3.…”

Section: Analysis Of the Interaction Effects Between Environmental Famentioning

confidence: 99%

The effect of environmental conditions on extracellular protease activity in controlled fermentations of Aspergillus niger

et al. 2009

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Proteolytic degradation by host proteases is one of the key issues in the application of filamentous fungi for non-fungal protein production. In this study the influence of several environmental factors on the production of extracellular proteases of Aspergillus niger was investigated systematically in controlled batch cultures. Of all factors investigated in a series of initial screening experiments, culture pH and nitrogen concentration in particular strongly affected extracellular protease activities. For instance, at a culture pH of 4, protease activity was higher than at pH 5, and protease activity increased with increasing concentrations of ammonium as nitrogen source. Interestingly, an interdependence was observed for several of the factors studied. These possible interaction effects were investigated further using a full factorial experimental design. Amongst others, the results showed a clear interaction effect between nitrogen source and nitrogen concentration. Based on the observed interactions, the selection of environmental factors to reduce protease activity is not straightforward, as unexpected antagonistic or synergistic effects occur. Furthermore, not only were the effects of the process parameters on maximum protease activity investigated, but five other protease-related phenotypes were studied as well, such as maximum specific protease activity and maximum protease productivity. There were significant differences in the effect of the environmental parameters on the various protease-related phenotypes. For instance, pH significantly affected final levels of protease activity, but not protease productivity. The results obtained in this study are important for the optimization of A. niger for protein production.

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“…Apart from selecting a productive strain, designing an appropriate fermentation medium is of crucial importance to improve the efficiency of ethanol production [7]. Because the synthetic medium is usually composed of known amounts of pure chemicals, it is possible to determine the specific requirements for growth and product formation by systematically adding or eliminating components from the formulation, which also results in more stability and reproducible culture conditions [8].…”

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confidence: 99%

Optimization of a synthetic medium for ethanol production by xylose-fermenting Zymomonas mobilis using response surface methodology

Dong

Zhao

et al. 2012

Chin. Sci. Bull.

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A synthetic medium for ethanol production by recombinant xylose-fermenting Zymomonas mobilis was optimized using Plackett-Burman (PB) design and response surface methodology (RSM). The effects of 19 medium components were investigated by PB design. Eight of these components were determined to have significant effects on ethanol production. The statistical model was constructed via central composite design (CCD) using five selected variables, including xylose, trisodium citrate, choline chloride, pyridoxine, and thiamine. The validity of the developed model was verified. The ethanol concentration in the optimized medium was 6.7% higher than in the RM medium. The optimized medium was then simplified to give the final synthetic medium (S2), in which the ethanol concentration was 20.7% higher than in the RM medium. Xylose and trisodium citrate were the key medium components influencing ethanol production, while calcium pantothenate was the only growth factor required by recombinant Z. mobilis. Ethanol production and growth response were directly proportional to the logarithm of the concentration of trisodium citrate in the range of 0.1-1.6 g/L.recombinant Zymomonas mobilis, synthetic medium, ethanol production, response surface methodology, trisodium citrate, xylose Citation:Dong H N, Zhao X M, Ma Y Y, et al. Optimization of a synthetic medium for ethanol production by xylose-fermenting Zymomonas mobilis using response surface methodology.

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Strategies for improving fermentation medium performance: a review

Cited by 272 publications

References 106 publications

Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235

The effect of environmental conditions on extracellular protease activity in controlled fermentations of Aspergillus niger

Optimization of a synthetic medium for ethanol production by xylose-fermenting Zymomonas mobilis using response surface methodology

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