title: Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks.
abstract: This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain.
authors.forename: L
authors.initials: L
authors.lastname: López-Rosales
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain.
authors.forename: J J
authors.initials: JJ
authors.lastname: Gallardo-Rodríguez
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Sánchez-Mirón
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain.
authors.forename: A
authors.initials: A
authors.lastname: Contreras-Gómez
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain. Electronic address: fgarcia@ual.es.
authors.forename: F
authors.initials: F
authors.lastname: García-Camacho
authors.affiliation: Chemical Engineering Area, University of Almería, 04120 Almería, Spain.
authors.forename: E
authors.initials: E
authors.lastname: Molina-Grima
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003470:Culture Media; D016208:Databases, Factual; D004141:Dinoflagellida / Q000254:growth & development*; D005285:Fermentation; D005502:Food; D016571:Neural Networks, Computer; D058433:Photobioreactors; D012044:Regression Analysis; D015203:Reproducibility of Results; D013696:Temperature; D014815:Vitamins / Q000378:metabolism
chemical_list: D003470:Culture Media; D014815:Vitamins
keywords: Artificial neural network; Dinoflagellate; Growth modelling; Microalga