Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mitra, Ruchira, Dutta, Debjani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
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
_version_ 1783346023659208704
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
work_keys_str_mv AT mitraruchira growthprofilingkineticsandsubstrateutilizationoflowcostdairywasteforproductionofbcryptoxanthinbykocuriamarinadagii
AT duttadebjani growthprofilingkineticsandsubstrateutilizationoflowcostdairywasteforproductionofbcryptoxanthinbykocuriamarinadagii