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. 2017 Jan;40(1):99-113.
doi: 10.1007/s00449-016-1678-6. Epub 2016 Sep 15.

Modelling the growth kinetics of Kocuria marina DAGII as a function of single and binary substrate during batch production of β-Cryptoxanthin

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Modelling the growth kinetics of Kocuria marina DAGII as a function of single and binary substrate during batch production of β-Cryptoxanthin

Ruchira Mitra et al. Bioprocess Biosyst Eng. 2017 Jan.

Abstract

In the present investigation, growth kinetics of Kocuria marina DAGII during batch production of β-Cryptoxanthin (β-CRX) was studied by considering the effect of glucose and maltose as a single and binary substrate. The importance of mixed substrate over single substrate has been emphasised in the present study. Different mathematical models namely, the Logistic model for cell growth, the Logistic mass balance equation for substrate consumption and the Luedeking-Piret model for β-CRX production were successfully implemented. Model-based analyses for the single substrate experiments suggested that the concentrations of glucose and maltose higher than 7.5 and 10.0 g/L, respectively, inhibited the growth and β-CRX production by K. marina DAGII. The Han and Levenspiel model and the Luong product inhibition model accurately described the cell growth in glucose and maltose substrate systems with a R 2 value of 0.9989 and 0.9998, respectively. The effect of glucose and maltose as binary substrate was further investigated. The binary substrate kinetics was well described using the sum-kinetics with interaction parameters model. The results of production kinetics revealed that the presence of binary substrate in the cultivation medium increased the biomass and β-CRX yield significantly. This study is a first time detailed investigation on kinetic behaviours of K. marina DAGII during β-CRX production. The parameters obtained in the study might be helpful for developing strategies for commercial production of β-CRX by K. marina DAGII.

Keywords: Batch fermentation; Binary substrate; Kinetics; Kocuria marina DAGII; Mathematical modelling; β-Cryptoxanthin.

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