KINETIC MODELING OF LACTIC ACID PRODUCTION FROM BATCH SUBMERGED FERMENTATION OF CHEESE WHEY

1999 ◽  
Vol 42 (6) ◽  
pp. 1791-1800 ◽  
Author(s):  
M. S. A. Tango ◽  
A. E. Ghaly
Keyword(s):  
Lactic Acid ◽  
Kinetic Modeling ◽  
Submerged Fermentation ◽  
Acid Production ◽  
Cheese Whey ◽  
Lactic Acid Production

scholarly journals Bioreactor Scale-Up and Kinetic Modeling of Lactic Acid and Biomass Production by Enterococcus faecalis SLT13 during Batch Culture on Hydrolyzed Cheese Whey

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Manel Ziadi ◽  
Sana M’Hir ◽  
Abdelkarim Aydi ◽  
Moktar Hamdi
Keyword(s):  
Lactic Acid ◽  
Enterococcus Faecalis ◽  
Batch Culture ◽  
Kinetic Modeling ◽  
Acid Production ◽  
Cheese Whey ◽  
Lactic Acid Production ◽  
Sugar Consumption ◽  
Sugar Utilization ◽  
Substrate Limitation

Kinetic modeling of biomass and lactic acid production by Enterococcus faecalis SLT13 have been developed during batch culture in M17 and Hydrolyzed Cheese Whey (HCW) in 2 L and 20 L bioreactors. The specific growth rate μmax was higher in 20 L bioreactor (1.09 h−1); however, the maximum specific lactic acid production rate qpmax and maximum specific sugar utilization rate qsmax were higher in 2 L bioreactor. Biomass and sugar utilization were affected by lactic acid inhibition in HCW. No effects of substrate inhibition have been observed. Substrate limitation of biomass has been observed on HCW in 20 L bioreactor; the substrate limitation constant for biomass Ksx was 4.229 g/L. Substrate limitation of sugar consumption has been observed on M17 in 2 L bioreactor; the substrate limitation constant for sugar consumption Kss was 2.73 g/L. Compared to experimental data, the model provided good predictions for biomass, sugar consumption, and lactic acid production.

Lactic Acid Production from Cheese Whey by Immobilized Bacteria

2005 ◽  
Vol 122 (1-3) ◽  
pp. 0529-0540 ◽  
Author(s):  
Abolghasem Shahbazi ◽  
Michele R. Mims ◽  
Yebo Li ◽  
Vestal Shirley ◽  
Salam A. Ibrahim ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Acid Production ◽  
Cheese Whey ◽  
Lactic Acid Production ◽  
Immobilized Bacteria

Inhibitory Activity of Cheese Whey Fermented with Kefir Grains

2011 ◽  
Vol 74 (1) ◽  
pp. 94-100 ◽  
Author(s):  
A. LONDERO ◽  
R. QUINTA ◽  
A. G. ABRAHAM ◽  
R. SERENO ◽  
G. DE ANTONI ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Lactic Acid ◽  
Acid Production ◽  
Cheese Whey ◽  
Lactic Acid Production ◽  
Reaction Products ◽  
Salmonella Enterica Serovar Enteritidis ◽  
Fermentation Products ◽  
E Coli ◽  
Kefir Grains

We investigated the chemical and microbiological compositions of three types of whey to be used for kefir fermentation as well as the inhibitory capacity of their subsequent fermentation products against 100 Salmonella sp. and 100 Escherichia coli pathogenic isolates. All the wheys after fermentation with 10% (wt/vol) kefir grains showed inhibition against all 200 isolates. The content of lactic acid bacteria in fermented whey ranged from 1.04 × 107 to 1.17 × 107 CFU/ml and the level of yeasts from 2.05 × 106 to 4.23 × 106 CFU/ml. The main changes in the chemical composition during fermentation were a decrease in lactose content by 41 to 48% along with a corresponding lactic acid production to a final level of 0.84 to 1.20% of the total reaction products. The MIC was a 30% dilution of the fermentation products for most of the isolates, while the MBC varied between 40 and 70%, depending on the isolate. The pathogenic isolates Salmonella enterica serovar Enteritidis 2713 and E. coli 2710 in the fermented whey lost their viability after 2 to 7 h of incubation. When pathogens were deliberately inoculated into whey before fermentation, the CFU were reduced by 2 log cycles for E. coli and 4 log cycles for Salmonella sp. after 24 h of incubation. The inhibition was mainly related to lactic acid production. This work demonstrated the possibility of using kefir grains to ferment an industrial by-product in order to obtain a natural acidic preparation with strong bacterial inhibitory properties that also contains potentially probiotic microorganisms.

LACTIC ACID PRODUCTION FROM CHEESE WHEY BY BIFIDOBACTERIUM LONGUM

2006 ◽  
Vol 49 (4) ◽  
pp. 1263-1267 ◽  
Author(s):  
Y. Li ◽  
A. Shahbazi ◽  
S. Coulibaly
Keyword(s):  
Lactic Acid ◽  
Acid Production ◽  
Cheese Whey ◽  
Lactic Acid Production ◽  
Bifidobacterium Longum

scholarly journals Kitchen Refuse: a Novel Substrate for L (+) -Lactic Acid Production by Rhizopus oryzae in Submerged Fermentation

2005 ◽  
Vol 6 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Suthasinee PRANEETRATTANANON ◽  
Minato WAKISAKA ◽  
Yoshihito SHIRAI ◽  
Vichien KITPREECHAVANICH
Keyword(s):  
Lactic Acid ◽  
Submerged Fermentation ◽  
Acid Production ◽  
Rhizopus Oryzae ◽  
Lactic Acid Production

scholarly journals Modification of the Luedeking and Piret Model with a Delay Time Parameter for Biotechnological Lactic Acid Production

2021 ◽  
Author(s):  
María Carla Groff ◽  
Gustavo Scaglia ◽  
Oscar A. Ortiz ◽  
Sandra E. Noriega
Keyword(s):  
Lactic Acid ◽  
Time Delay ◽  
Submerged Fermentation ◽  
Acid Production ◽  
Lactic Acid Production ◽  
Time Lag ◽  
Product Formation ◽  
Delay Model ◽  
Growth Environment ◽  
Experimental Kinetics

Abstract Objectives To obtain a mathematical model that adequately describes the time lag between biomass generation and lactic acid production of lactic fermentations. Methods Seven experimental kinetics from other research works were studied to validate our proposal: four studies of Fungal Submerged Fermentation and three cases of Bacterial Submerged Fermentation, including the data recollected by Luedeking and Piret. Results We introduce a modification to the Luedeking and Piret model that consist in the introduction of a time delay parameter in the model, this parameter would account for the lag time that exists between the production of biomass and lactic acid. It is possible to determine this time delay in a simple way by approximating the biomass and product formation considering that they behave as a first order plus dead time system. The duration of this phenomenon, which is not described with the classical Luedeking and Piret model, is a function of microorganism physiology (ease of biomass growth), environment (nutrients) and type of inoculum. Conclusion The Luedeking and Piret with delay model applications reveal an increase of the R2 in all cases, evidencing the quality of fit and the simplicity of the method proposed. These model would improve the accuracy of bioprocess scaling up.

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