RESUMO
The present study aims to characterize and predict models for antibacterial activity of a novel oligosaccharide from Streptomyces californics against Erwinia carotovora subsp. carotovora 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 (1H- and 13C-NMR), and mass spectrometry (MS). The best performances for the model were 39.45 and 35.16 recorded at epoch 1 for E. carotovora Erw5 and E. carotovora EMCC 1687, respectively. The coefficient (R2) 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 m/z = 703.70 and m/z = 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 C29H53NO18 and C14H26O9, respectively, which were obtained from the elemental analysis result. A novel oligosaccharide from Streptomyces californics with potential activity against E. carotovora EMCC 1687 and E. carotovora Erw5 was successfully isolated, purified, and characterized.
