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المؤلفEnnouri, Karim
المؤلفBen Ayed, Rayda
المؤلفTriki, Mohamed Ali
المؤلفOttaviani, Ennio
المؤلفMazzarello, Maura
المؤلفHertelli, Fathi
المؤلفZouari, Nabil
تاريخ الإتاحة2021-01-27T11:06:56Z
تاريخ النشر2017
اسم المنشور3 Biotech
المصدرScopus
الرقم المعياري الدولي للكتاب2190572X
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s13205-017-0799-1
معرّف المصادر الموحدhttp://hdl.handle.net/10576/17531
الملخصThe 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.
اللغةen
الناشرSpringer Verlag
الموضوعArtificial neural networks
Bacillus thuringiensis
Bootstrap method
Delta-endotoxins
Multiple linear regression
Proteases
العنوانMultiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis
النوعArticle
رقم العدد3
رقم المجلد7
dc.accessType Abstract Only


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