Application of response surface methodology in optimization of lactic acid fermentation of radish: effect of addition of salt, additives and growth stimulators

Abstract

Lactic acid fermentation of radish was conducted using various additive and growth stimulators such as salt (2 %-3 %), lactose, MgSO 4 +MnSO 4 and Mustard (1 %, 1.5 % and 2 %) to optimize the process. Response surface methodology (Design expert, Trial version 8.0.5.2) was applied to the experimental data for the optimization of process variables in lactic acid fermentation of radish. Out of various treatments studied, only the treatments having ground mustard had an appreciable effect on lactic acid fermentation. Both linear and quadratic terms of the variables studied had a significant effect on the responses studied. The interactions between the variables were found to contribute to the response at a significant level. The best results were obtained in the treatment with 2.5 % salt, 1.5 % lactose, 1.5 % (MgSO 4 + MnSO 4 ) and 1.5 % mustard. These optimized concentrations increased titrable acidity and LAB count, but lowered pH. The second-order polynomial regression model determined that the highest titrable acidity (1.69), lowest pH (2.49) and maximum LAB count (10×10 8 cfu/ml) would be obtained at these concentrations of additives. Among 30 runs conducted, run 2 has got the optimum concentration of salt-2.5 %, lactose-1.5 %, MgSO 4 +MnSO 4 -1.5 % and mustard-1.5 % for lactic acid fermentation of radish. The values for different additives and growth stimulators optimized in this study could successfully be employed for the lactic acid fermentation of radish as a postharvest reduction tool and for product development.

Performance of Biomass and Exopolysaccharide Production from the Medicinal Mushroom Ganoderma lucidum in a New Fabricated Air-L-Shaped Bioreactor (ALSB)

“…Ahmad [32] conducted a study to optimise the medium components for the fermentation of Lentinus squarrosulus in just 20 runs of experiments for three independent variables, similar to [23]. The main use of RSM is to reduce the number of experimental runs while producing results that are better than those obtained using the one-factor-at-a-time method with graphical representation [33]. By performing a quadratic regression on the experiments, the interactions between the independent variables were identified and their significance to each other was studied, as shown in Tables 3 and 4.…”

Large-scale production of tauroursodeoxycholic acid products through fermentation optimization of engineered Escherichia coli cell factory

“…Taking the advantage of interactive analysis of RSM, we then tried to optimize the carbon and nitrogen sources favorable for bacterial growth including glucose, glycerol, tryptone, and yeast extract [29, 30] based on M9 medium using cell density and product conversion efficiency as two responses. RSM is one of the most popular methods applied in biotechnology for fermentation optimizations [31–33]. It provides an alternative way to analyze systems where the mathematical relationship between the parameters and the responses are unknown and to reflect the complex nonlinear relationships between independent variables and responses of the system [34].…”