Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM

Least Squares-Support Vector Machine (LS-SVM) was used to predict data of Baker’s yeast invertase purification using PEG/MgSO4 Aqueous Two Phase-System (ATPS). Experiments were carried out changing the average molecular mass and percentage of PEG, pH, percentage of MgSO4 as well as of raw extract in...

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Principais autores:	Souza, Domingos Fabiano de Santana, Padilha, Carlos Eduardo de Araújo, Oliveira Junior, Sergio Dantas, Oliveira, Jackson Araújo de, Macedo, Gorete Ribeiro de, Santos, Everaldo Silvino dos
Formato:	article
Idioma:	English
Publicado em:	Elsevier
Assuntos:	Principal component analysis Least squares-support vector machine Genetic algorithm Aqueous two-phase system Invertase
Endereço do item:	https://repositorio.ufrn.br/handle/123456789/45189
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spelling	ri-123456789-451892023-02-06T18:49:21Z Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM Souza, Domingos Fabiano de Santana Padilha, Carlos Eduardo de Araújo Oliveira Junior, Sergio Dantas Oliveira, Jackson Araújo de Macedo, Gorete Ribeiro de Santos, Everaldo Silvino dos Principal component analysis Least squares-support vector machine Genetic algorithm Aqueous two-phase system Invertase Least Squares-Support Vector Machine (LS-SVM) was used to predict data of Baker’s yeast invertase purification using PEG/MgSO4 Aqueous Two Phase-System (ATPS). Experiments were carried out changing the average molecular mass and percentage of PEG, pH, percentage of MgSO4 as well as of raw extract in order to observe the percentage of yield (% Yield) and Purification Factor (PF) at the bottom phase. The Principal Component Analysis (PCA) was used to eliminate the less significant input variables on the % Yield as well as on the PF. The generalization capacity evaluation for these two parameters has shown that the model generated by the LS-SVM (R2 = 0.974; 0.932) approach has given the best performance than partial least squares (R2 = 0.960; 0.926), base radial neural network (R2 = 0.874; 0.687) and multi-layer perceptron (R2 = 0.911; 0.652). Also, a bi-objective optimization has been carried out using the previously adjusted models in order to obtain a set of input data producing higher % Yield for the enzymatic activity (448.34%) as well as for the PF (8.45) 2021-12-06T18:06:41Z 2021-12-06T18:06:41Z 2017-01 article PADILHA, Carlos Eduardo de Araújo; OLIVEIRA JÚNIOR, Sérgio Dantas; SOUZA, Domingos Fabiano de Santana; OLIVEIRA, Jackson Araújo de; MACEDO, Gorete Ribeiro de; SANTOS, Everaldo Silvino dos. Baker’s yeast invertase purification using Aqueous Two Phase System—Modeling and optimization with PCA/LS-SVM. Food And Bioproducts Processing, [S.L.], v. 101, p. 157-165, jan. 2017. Elsevier BV. http://dx.doi.org/10.1016/j.fbp.2016.11.004. Disponível em <https://www.sciencedirect.com/science/article/abs/pii/S0960308516301559?via%3Dihub>. Acesso em 05 nov. 2021. 0960-3085 https://repositorio.ufrn.br/handle/123456789/45189 10.1016/j.fbp.2016.11.004 en Elsevier
institution	Repositório Institucional
collection	RI - UFRN
language	English
topic	Principal component analysis Least squares-support vector machine Genetic algorithm Aqueous two-phase system Invertase
spellingShingle	Principal component analysis Least squares-support vector machine Genetic algorithm Aqueous two-phase system Invertase Souza, Domingos Fabiano de Santana Padilha, Carlos Eduardo de Araújo Oliveira Junior, Sergio Dantas Oliveira, Jackson Araújo de Macedo, Gorete Ribeiro de Santos, Everaldo Silvino dos Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
description	Least Squares-Support Vector Machine (LS-SVM) was used to predict data of Baker’s yeast invertase purification using PEG/MgSO4 Aqueous Two Phase-System (ATPS). Experiments were carried out changing the average molecular mass and percentage of PEG, pH, percentage of MgSO4 as well as of raw extract in order to observe the percentage of yield (% Yield) and Purification Factor (PF) at the bottom phase. The Principal Component Analysis (PCA) was used to eliminate the less significant input variables on the % Yield as well as on the PF. The generalization capacity evaluation for these two parameters has shown that the model generated by the LS-SVM (R2 = 0.974; 0.932) approach has given the best performance than partial least squares (R2 = 0.960; 0.926), base radial neural network (R2 = 0.874; 0.687) and multi-layer perceptron (R2 = 0.911; 0.652). Also, a bi-objective optimization has been carried out using the previously adjusted models in order to obtain a set of input data producing higher % Yield for the enzymatic activity (448.34%) as well as for the PF (8.45)
format	article
author	Souza, Domingos Fabiano de Santana Padilha, Carlos Eduardo de Araújo Oliveira Junior, Sergio Dantas Oliveira, Jackson Araújo de Macedo, Gorete Ribeiro de Santos, Everaldo Silvino dos
author_facet	Souza, Domingos Fabiano de Santana Padilha, Carlos Eduardo de Araújo Oliveira Junior, Sergio Dantas Oliveira, Jackson Araújo de Macedo, Gorete Ribeiro de Santos, Everaldo Silvino dos
author_sort	Souza, Domingos Fabiano de Santana
title	Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
title_short	Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
title_full	Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
title_fullStr	Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
title_full_unstemmed	Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
title_sort	baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with pca/ls-svm
publisher	Elsevier
publishDate	2021
url	https://repositorio.ufrn.br/handle/123456789/45189
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Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM

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