Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/133207
Title: Response surface methodology applied to cyanobacterial EPS production: steps and statistical validations
Authors: Rodrigues, Filipa
Mendonça, Ivana
Faria, Marisa
Gomes, Ricardo
Gómez Pinchetti, Juan Luis 
Ferreira, Artur
Cordeiro, Nereida
UNESCO Clasification: 241707 Algología (ficología)
Keywords: Cyanobacteria
Cyanocohniella rudolphia
EPS production modulation
Response surface methodology
Soluble-extracellular polymeric substances
Issue Date: 2024
Project: CALYPSO (1/MAC/1/1.1/0088)
Journal: Processes 
Abstract: Understanding the impact of variables involved in soluble-extracellular polymeric substance (S-EPS) production processes is crucial for reducing production costs and enhancing sustainability. Response surface methodology (RSM) provides essential tools that assist in developing predicted interactions among process variables for both industrial and non-industrial applications. The present study offers a simple and systematic demonstration of RSM capabilities, focusing on maximizing efficiency and minimizing production costs of S-EPS produced by Cyanocohniella rudolphia. RSM was employed to (1) design the production setup; (2) fit the collected data into a second-order polynomial model; (3) statistically evaluate the model’s validity and the significance of the involved variables; and (4) identify and optimize production variables to enhance output and reduce costs. Focused on four key variables, each at three levels, RSM designed 25 distinct S-EPS production conditions, each with three replicates. Statistical analysis identified the most significant variables affecting S-EPS production as the culture medium/wet biomass ratio, production days, and nitrogen concentration. The model’s validation demonstrated a strong correlation between the predicted and experimental values, with S-EPS production ranging from 70.46 to 228.65 mg/L and a maximum variation of 11.6%. This study demonstrates the effectiveness of RSM in optimizing S-EPS production, with the developed model showing a strong correlation between the variables and the response. The RSM model offers a promising approach for the bioprocessing industry, enhancing productivity and efficiency, minimizing costs, and leading to sustainable, cost-effective practices.
URI: http://hdl.handle.net/10553/133207
ISSN: 2227-9717
DOI: 10.3390/pr12081733
Source: Processes,v. 12 (8), (Agosto 2024)
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