A Streptomyces sp. strain TN71 was isolated from Tunisian Saharan soil and selected for its antimicrobial activity against phytopathogenic fungi. In an attempt to increase its anti–Fusarium oxysporum activity, GYM+S (glucose, yeast extract, malt extract and starch) culture medium was selected out of five different production media. Plackett–Burman design (PBD) was used to select yeast extract, malt extract and calcium carbonate (CaCO3) as parameters having significant effects on antifungal activity, and a Box–Behnken design was applied for further optimization. The analysis revealed that the optimum concentrations for the anti–F. oxysporum activity of the tested variables were yeast extract 5.03g/L, malt extract 8.05g/L and CaCO3 4.51g/L. Artificial Neural Networks (ANNs): the Multilayer perceptron (MLP) and the Radial basis function (RBF) were created to predict the anti–F. oxysporum activity. The comparison between experimental and predicted outputs from ANN and Response Surface Methodology (RSM) were studied. The ANN model presents an improvement of 14.73%. To our knowledge, this is the first work reporting the statistical versus artificial intelligence -based modeling for the optimization of bioactive molecules against mycotoxigenic and phytopathogenic fungi.El texto completo de este artículo está disponible en PDF.
Keywords : Streptomyces sp. TN71 strain, Anti–F. oxysporum activity, Response surface methodology, Artificial neural network
Vol 28 - N° 3P. 551-560 - septembre 2018 Regresar al número
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