Fermentative Production and Characterization of Carboxymethyl Bacterial Cellulose Using Date Syrup

In this study, static batch fermentation was used for bacterial cellulose production in date syrup solution (Bx. 10%) at 28°C using Gluconacetobacter. xylinus (PTCC 1734). The physicochemical properties of standard Sigma CMC and the produced carboxymethyl bacterial cellulose (CMBC) were studied using FT-IR spectroscopy, X-ray diffractometry (XRD) and Scanning Electron Microscopy (SEM). According to the FT-IR spectra the bands at 1664 and 1431 cm-1 indicate that carboxylic acid groups and carboxylate groups exist on the surface. The SEM imaging of CMBC and CMC carried out in magnification of 1K. Comparing the SEM imaging obviously showed that the ribbon shape in CMC remained but the length of ribbons became shorter while that shape changed to flake shape for CMBC. Determination of the area under XRD patterns demonstrated that the crystallinity amount of CMC was more than that for CMBC (51.08% and 81.84% for CMBC and CMC, respectively).

Investigation of Physicochemical Properties of the Bacterial Cellulose Produced by Gluconacetobacter xylinus from Date Syrup

Bacterial cellulose, a biopolysaccharide, is produced by the bacterium, Gluconacetobacter xylinus. Static batch fermentation for bacterial cellulose production was studied in sucrose and date syrup solutions (Bx. 10%) at 28 °C using G. xylinus (PTCC, 1734). Results showed that the maximum yields of bacterial cellulose (BC) were 4.35 and 1.69 g/l00 ml for date syrup and sucrose medium after 336 hours fermentation period, respectively. Comparison of FTIR spectrum of cellulose with BC indicated appropriate coincidence which proved that the component produced by G. xylinus was cellulose. Determination of the area under X-ray diffractometry patterns demonstrated that the crystallinity amount of cellulose (83.61%) was more than that for the BC (60.73%). The scanning electron microscopy imaging of BC and cellulose were carried out in two magnifications of 1 and 6K. Results showed that the diameter ratio of BC to cellulose was approximately 1/30 which indicated more delicacy of BC fibers relative to cellulose.

Fermentative Production of Dextran using Food Industry Wastes

Dextran is a D-glucose polymer which is produced by Leuconostoc mesenteroides grown in a sucrose-rich media. The organism was obtained from the Persian Type Culture Collection (PTCC) and was transferred in MRS broth medium at 30°C and pH 6.8 for 24 h. After preparation of inoculums, organisms were inoculated into five liquid fermentation media containing either molasses or cheese whey or different combinations of cheese whey and molasses. After certain fermentation period, the produced dextran was separated and dried. Dextran yield was calculated and significant differences in different media were observed. Furthermore, FT-IR analysis was performed and the results showed that there were no significant differences in the produced dextran structures.

Identification of Optimum Parameters of Deep Drawing of a Cylindrical Workpiece using Neural Network and Genetic Algorithm

Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.