Otimização da produção de Endotoxinas de Bacillus thuringiensis subsp. kurstaki usando metodología de Superfície de Resposta de Mezclas Ternárias com Efluentes Industriais

MOREIRA GA, BECCARIA A, GOICOECHEA HC.

Tucuman

Jornada; XIII Jornadas de Jóvenes Investigadores; 2005

AUGM

A mixture design coupled with data analysis by means of both response surface methodology (RSM) and artificial neural networks (ANNs) followed by multiple response optimization through a Desirability function (D) was applied to the production of parasporal crystals named d-endotoxins from Bacillus thuringiensis var. kurstaki. The composition of a culture medium was defined by testing three regional effluents: sugar cane molasses, milky effluent and beer wastewater, through a mixture design. Both RSM and ANNs accomplished the goal pursued in this work, by predicting the optimal mixture of the effluents. ANNs provided more reliable results due to the complexity of the models to be fitted and the presence of a high experimental noise. The optimal selected blend was: artificial milky effluent 14.8 (74%), beer wastewater 5.2 (26%) and sugar cane molasses 0.0 (0%).