Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks

•Modelling multinutrient interactions in microalgal growth using neural networks.•Feedforward backpropagation neural network works well with two training algorithms.•Garson’s algorithm is a valuable tool to analyze relative importance of nutrients. This study examines the use of artificial neural ne...

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Bibliographic Details
Published in:Bioresource technology 2013-10, Vol.146, p.682-688
Main Authors: López-Rosales, L., Gallardo-Rodríguez, J.J., Sánchez-Mirón, A., Contreras-Gómez, A., García-Camacho, F., Molina-Grima, E.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural network
Artificial neural networks
Back propagation
Bayes Theorem
Biological and medical sciences
Culture
Culture Media
Databases, Factual
Dinoflagellate
Dinoflagellates
Dinoflagellida - growth & development
Fermentation
Food
Fundamental and applied biological sciences. Psychology
Growth modelling
Microalga
Modelling
Neural networks
Neural Networks (Computer)
Nutrients
Photobioreactors
Protoceratium reticulatum
Regression Analysis
Reproducibility of Results
Temperature
Vitamins - metabolism
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Summary:•Modelling multinutrient interactions in microalgal growth using neural networks.•Feedforward backpropagation neural network works well with two training algorithms.•Garson’s algorithm is a valuable tool to analyze relative importance of nutrients. 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.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2013.07.141