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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Bibliographic Details
Authors: 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
Format: article
Status:Published version
Publication Date:2017
Country:Brasil
Institution:Universidade Federal do Rio Grande do Norte (UFRN)
Repository:Repositório Institucional da UFRN
Language:English
OAI Identifier:oai:repositorio.ufrn.br:123456789/45189
Online Access:https://repositorio.ufrn.br/handle/123456789/45189
Access Level:Open access
Keyword:Principal component analysis
Least squares-support vector machine
Genetic algorithm
Aqueous two-phase system
Invertase
Description
Summary: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)

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