Antibacterial Activity of a Novel Oligosaccharide from against subsp. .

The present study aims to characterize and predict models for antibacterial activity of a novel oligosaccharide from against subsp. using an adaptive neuro-fuzzy inference system and an artificial neural network. The mathematical predication models were used to determine the optimal conditions to produce oligosaccharide and determine the relationship between the factors (pH, temperature, and time). The characteristics of the purified antibacterial agent were determined using ultraviolet spectroscopy (UV/Vis), infrared spectroscopy (FT-IR), nuclear magnetic resonance spectroscopy (H- and C-NMR) and mass spectrometry (MS). The best performances for the model were 39.45 and 35.16 recorded at epoch 1 for Erw and EMCC 1687, respectively. The coefficient (R) of the training was more than 0.90. The highest antimicrobial production was recorded after 9 days at 25 °C and a pH of 6.2, at which more than 17 mm of the inhibition zone was obtained. The mass spectrum of antimicrobial agent (peak at R.T. = 3.433 of fraction 6) recorded two molecular ion peaks at = 703.70 and = 338.30, corresponding to molecular weights of 703.70 and 338.30 g/mol, respectively. The two molecular ion peaks matched well with the molecular formulas CHNO and CHO, respectively, which were obtained from the elemental analysis result. A novel oligosaccharide from with potential activity against EMCC 1687 and Erw was successfully isolated, purified, and characterized.