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Response surface methodology as a tool for modelling galacto-oligosaccharide production

Published online by Cambridge University Press:  20 September 2017

Claudia I. Vénica ,
Carina V. Bergamini  and
María C. Perotti
Claudia I. Vénica*
Affiliation:
Instituto de Lactología Industrial-Universidad Nacional del Litoral/Consejo Nacional de Investigaciones Científicas y Técnicas (INLAIN-UNL/CONICET), Santiago del Estero 2829, S3000AOM Santa Fe, Argentina
Carina V. Bergamini
Affiliation:
Instituto de Lactología Industrial-Universidad Nacional del Litoral/Consejo Nacional de Investigaciones Científicas y Técnicas (INLAIN-UNL/CONICET), Santiago del Estero 2829, S3000AOM Santa Fe, Argentina
María C. Perotti
Affiliation:
Instituto de Lactología Industrial-Universidad Nacional del Litoral/Consejo Nacional de Investigaciones Científicas y Técnicas (INLAIN-UNL/CONICET), Santiago del Estero 2829, S3000AOM Santa Fe, Argentina
*
*For correspondence; e-mail: clauvenica@fiq.unl.edu.ar
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Abstract

The experiments reported in this research paper describe the effects of β-galactosidase enzyme dose and cheese whey amount, on the maximum concentration and yield of galacto-oligosaccahride (GOS) and reaction time. The experimental plan was based on central composite rotational design (CCRD) and modelled by response surface methodology (RSM). The results indicate that the proposed mathematical models could adequately describe the concentration and yield of GOS and the reaction time within the limits of the factors that are being investigated. The variance analysis shows high values of coefficients of determination (>0·97) while no significant lack of fit was evident. Hence, the models could be employed to select reaction conditions applied in the manufacture of products enriched in bioactive compounds with high value-added.

Keywords

Galacto-oligosaccharidesβ-galactosidase enzymemilk-whey mixturescentral composite designresponse surface methodology
Type
Research Article
Information
Journal of Dairy Research , Volume 84 , Issue 4 , November 2017 , pp. 464 - 470
Copyright
Copyright © Hannah Research Foundation 2017 

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