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Kinetic model selection to describe the growth curve of Arthrospira ( Spirulina ) maxima in autotrophic cultures
Author(s) -
Escalante Froylán ME,
ReynaAngeles Karen Alejandra,
VillafañaRojas Juan,
AguilarGarnica Efrén
Publication year - 2017
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.5136
Subject(s) - photobioreactor , arthrospira , maxima , selection (genetic algorithm) , biochemical engineering , autotroph , cyanobacteria , biology , biological system , computer science , ecology , biomass (ecology) , engineering , artificial intelligence , art , genetics , art history , performance art , bacteria
BACKGROUND Currently, microbial products are gaining worldwide importance. Arthrospira ( Spirulina ) maxima is one of the most widely studied microorganisms whose industrial importance is due to its ability to accumulate high amounts of good‐quality proteins; as a consequence, research efforts of many health food and aquaculture industries are focused on the development of tools to optimize the commercial exploitation of this microalgae (cyanobacteria). Kinetic models can appropriately describe the patterns of growth and product formation, which are necessary for any biotechnological process using microorganisms. Some primary, secondary or even more complex kinetic models have been used to depict the growth of microalgae, but to the best of our knowledge, little has been done to choose the most appropriate one. RESULTS The present contribution addresses the selection of a kinetic model to describe the growth curve of Arthrospira maxima by considering Akaike's weights as a measure that simultaneously considers the goodness‐of‐fit and the complexity of the model (i.e. number of parameters). Specifically, Haldane‐based, Monod‐based, and logistic kinetic models are considered as candidates to describe the growth of A. maxima under autotrophic conditions. CONCLUSION The logistic model, which is the most parsimonious, should be selected to describe the growth of A. maxima within a photobioreactor either in continuous light operation or in dark/light cycles at several light intensities. The selection of this model will allow implementation of simpler monitoring, control, and optimization schemes. © 2016 Society of Chemical Industry