Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM
| dc.contributor.author | Souza, Domingos Fabiano de Santana | |
| dc.contributor.author | Padilha, Carlos Eduardo de Araújo | |
| dc.contributor.author | Oliveira Junior, Sergio Dantas | |
| dc.contributor.author | Oliveira, Jackson Araújo de | |
| dc.contributor.author | Macedo, Gorete Ribeiro de | |
| dc.contributor.author | Santos, Everaldo Silvino dos | |
| dc.date.accessioned | 2021-12-06T18:06:41Z | |
| dc.date.available | 2021-12-06T18:06:41Z | |
| dc.date.issued | 2017-01 | |
| dc.description.resumo | 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) | pt_BR |
| dc.identifier.citation | 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. | pt_BR |
| dc.identifier.doi | 10.1016/j.fbp.2016.11.004 | |
| dc.identifier.issn | 0960-3085 | |
| dc.identifier.uri | https://repositorio.ufrn.br/handle/123456789/45189 | |
| dc.language | en | pt_BR |
| dc.publisher | Elsevier | pt_BR |
| dc.subject | Principal component analysis | pt_BR |
| dc.subject | Least squares-support vector machine | pt_BR |
| dc.subject | Genetic algorithm | pt_BR |
| dc.subject | Aqueous two-phase system | pt_BR |
| dc.subject | Invertase | pt_BR |
| dc.title | Baker’s yeast invertase purification using aqueous two phase system—modeling and optimization with PCA/LS-SVM | pt_BR |
| dc.type | article | pt_BR |
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