Statistical versus artificial intelligence -based modeling for the optimization of antifungal activity against Fusarium oxysporum using Streptomyces sp. strain TN71

Publication date: Available online 26 July 2018

Source: Journal de Mycologie Médicale

Author(s): S. Smaoui, K. Ennouri, A. Chakchouk-Mtibaa, I. Sellem, K. Bouchaala, I. Karray-Rebai, L. Mellouli

Abstract

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 5.03 g/L, malt extract 8.05 g/L and CaCO₃ 4.51 g/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.