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AuthorEnnouri, Karim
AuthorBen Ayed, Rayda
AuthorTriki, Mohamed Ali
AuthorOttaviani, Ennio
AuthorMazzarello, Maura
AuthorHertelli, Fathi
AuthorZouari, Nabil
Available date2021-01-27T11:06:56Z
Publication Date2017
Publication Name3 Biotech
ResourceScopus
ISSN2190572X
URIhttp://dx.doi.org/10.1007/s13205-017-0799-1
URIhttp://hdl.handle.net/10576/17531
AbstractThe aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R2) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort. , Springer-Verlag GmbH Germany.
Languageen
PublisherSpringer Verlag
SubjectArtificial neural networks
Bacillus thuringiensis
Bootstrap method
Delta-endotoxins
Multiple linear regression
Proteases
TitleMultiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis
TypeArticle
Issue Number3
Volume Number7
dc.accessType Abstract Only


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