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Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII
The dairy industry produces enormous amount of cheese whey containing the major milk nutrients, but this remains unutilized all over the globe. The present study investigates the production of β-cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodolog...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083662/ https://www.ncbi.nlm.nih.gov/pubmed/30109058 http://dx.doi.org/10.1098/rsos.172318 |
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author | Mitra, Ruchira Dutta, Debjani |
author_facet | Mitra, Ruchira Dutta, Debjani |
author_sort | Mitra, Ruchira |
collection | PubMed |
description | The dairy industry produces enormous amount of cheese whey containing the major milk nutrients, but this remains unutilized all over the globe. The present study investigates the production of β-cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and an artificial neural network (ANN) approach were implemented to obtain the maximum β-CRX yield. Significant factors, i.e. yeast extract, peptone, cheese whey and initial pH, were the input variables in both the optimizing studies, and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with a feed-forward back-propagation algorithm. Experimental values of β-CRX yield (17.14 mg l(−1)) and biomass (5.35 g l(−1)) were compared and ANN predicted values (16.99 mg l(−1) and 5.33 g l(−1), respectively) were found to be more accurate compared with RSM predicted values (16.95 mg l(−1) and 5.23 g l(−1), respectively). Detailed kinetic analysis of cellular growth, substrate consumption and product formation revealed that growth inhibition took place at substrate concentrations higher than 12% (v/v) of cheese whey. The Han and Levenspiel model was the best fitted substrate inhibition model that described the cell growth in cheese whey with an R(2) and MSE of 0.9982% and 0.00477%, respectively. The potential importance of this study lies in the development, optimization and modelling of a suitable cheese whey supplemented medium for increased β-CRX production. |
format | Online Article Text |
id | pubmed-6083662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-60836622018-08-14 Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII Mitra, Ruchira Dutta, Debjani R Soc Open Sci Engineering The dairy industry produces enormous amount of cheese whey containing the major milk nutrients, but this remains unutilized all over the globe. The present study investigates the production of β-cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and an artificial neural network (ANN) approach were implemented to obtain the maximum β-CRX yield. Significant factors, i.e. yeast extract, peptone, cheese whey and initial pH, were the input variables in both the optimizing studies, and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with a feed-forward back-propagation algorithm. Experimental values of β-CRX yield (17.14 mg l(−1)) and biomass (5.35 g l(−1)) were compared and ANN predicted values (16.99 mg l(−1) and 5.33 g l(−1), respectively) were found to be more accurate compared with RSM predicted values (16.95 mg l(−1) and 5.23 g l(−1), respectively). Detailed kinetic analysis of cellular growth, substrate consumption and product formation revealed that growth inhibition took place at substrate concentrations higher than 12% (v/v) of cheese whey. The Han and Levenspiel model was the best fitted substrate inhibition model that described the cell growth in cheese whey with an R(2) and MSE of 0.9982% and 0.00477%, respectively. The potential importance of this study lies in the development, optimization and modelling of a suitable cheese whey supplemented medium for increased β-CRX production. The Royal Society Publishing 2018-07-11 /pmc/articles/PMC6083662/ /pubmed/30109058 http://dx.doi.org/10.1098/rsos.172318 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Engineering Mitra, Ruchira Dutta, Debjani Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title | Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title_full | Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title_fullStr | Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title_full_unstemmed | Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title_short | Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII |
title_sort | growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by kocuria marina dagii |
topic | Engineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083662/ https://www.ncbi.nlm.nih.gov/pubmed/30109058 http://dx.doi.org/10.1098/rsos.172318 |
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