Mutagenesis enhances gellan gum production by a novel Sphingomonas spp.: upstream optimization, kinetic modeling, and structural and physico-functional evaluation.

Gellan gum (GG) has gained tremendous attention owing to its diversified applications. However, its high production and hence market cost are still a bottleneck in its widespread utilization. In the present study, high GG producing mutant of Sphingomonas spp.

Advances in fermentative production, purification, characterization and applications of gellan gum.

Multiple microbial exopolysaccharides have been reported in recent decade with their structural and functional features. Gellan gum (GG) is among these emerging biopolymers with versatile properties. Low production yield, high downstream cost, and abundant market demand have made GG a high cost material.

Cross-linked β-Mannanase Aggregates: Preparation, Characterization, and Application for Producing Partially Hydrolyzed Guar Gum.

β-Mannanase capable of hydrolyzing β-1,4-linkages in guar gum was immobilized as cross-linked enzyme aggregates (M-CLEAs). The aggregation and cross-linking process was optimized by response surface methodology (RSM) for maximum activity. The resulting M-CLEAs were characterized by FTIR, DSC, SEM, and SDS-PAGE.

Combined effect of attrition and ultrasound on the disruption of Pseudomonas putida for the efficient release of arginine deiminase.

Disruption of Pseudomonas putida KT2440 by ultrasound treatment in a bath sonicator, in presence of the glass beads, was carried out for the release of arginine deiminase (ADI) and the results were compared with that of by Dyno-mill. The release of ADI depended mainly on the bead size and cellmass concentration being disrupted in bead mill. Nearly 23 U mL ADI was released when slurry with a cell-mass concentration of 250 g L was disintegrated for 9 min with 80% bead loading (0.

Ultrasonic disruption of Pseudomonas putida for the release of arginine deiminase: Kinetics and predictive models.

The responses of the ultrasound-mediated disruption of Pseudomonas putida KT2440 were modelled as the function of biomass concentration in the cell suspension; the treatment time of sonication; the duty cycle and the acoustic power of the sonicator. For the experimental data, the response surface (RSM), the artificial neural network (ANN) and the support vector machine (SVM) models were compared for their ability to predict the performance parameters. The satisfactory prediction of the unseen data of the responses implied the proficient generalization capabilities of ANN.