Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

Food Science and Technology (Campinas)

Vitor Badiale Furlong¹  Renato Dutra Pereira Filho¹  Ana Cláudia Margarites¹  Pâmela Guder Goularte¹  Jorge Alberto Vieira Costa¹ 

[1] ,Federal University of Rio Grande School of Chemistry and Food Engineering Laboratory of Biochemical EngineeringRio Grande RS ,Brazil

关键词: black-box;    cellular concentration;    predictive microbiology;    black-box;    concentração celular;    microbiologia preditiva;   

DOI  :  10.1590/S0101-20612013000500021

来源: SciELO

【 摘 要 】

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

【 授权许可】

CC BY   
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