Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.

Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.

This article presents data associated with the extraction of sodium alginate from waste Sargassum seaweed in the Caribbean utilizing an optimization approach using Response Surface Methodology [1]. A Box-Behnken (BBD) Response Surface Methodology using Design Expert 10.0.3 software on the alkaline e...

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Bibliographic Details
Main Authors: Akeem Mohammed, Arianne Rivers, David.C. Stuckey, Keeran Ward
Format: Article
Language:English
Published: Elsevier 2020-08-01
Series:Data in Brief
Subjects:
Pelagic sargassum
Response surface methodology
Sodium alginate
Extraction
Optimization
Box-behnken design
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340920307319
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spelling doaj-8dda63009a7445f992e58cb2da18d7052020-11-25T03:27:48ZengElsevierData in Brief2352-34092020-08-0131105837Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.Akeem Mohammed0Arianne Rivers1David.C. Stuckey2Keeran Ward3Department of Chemical Engineering, University of the West Indies, St. Augustine, Trinidad and TobagoDepartment of Chemical Engineering, University of the West Indies, St. Augustine, Trinidad and TobagoDepartment of Chemical Engineering, Imperial College London, London SW72AZ, UKDepartment of Chemical Engineering, University of the West Indies, St. Augustine, Trinidad and Tobago; Corresponding author.This article presents data associated with the extraction of sodium alginate from waste Sargassum seaweed in the Caribbean utilizing an optimization approach using Response Surface Methodology [1]. A Box-Behnken (BBD) Response Surface Methodology using Design Expert 10.0.3 software on the alkaline extraction process was used. Data consists of the effects of 4 process variables (temperature, extraction time, alkali concentration and excess volume of alkali: dried seaweed) on the yield of sodium alginate. The model was validated, and extracts were characterization using High Performance Liquid Chromatography (HPLC), Gel Permeation Chromatography (GPC), Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR). The data illustrates the applicability of our model in potentially valorizing this waste product into a valuable resource. Furthermore, our methodology can be applied to other macroalgae for efficient extraction of sodium alginate of commercial quality.http://www.sciencedirect.com/science/article/pii/S2352340920307319Pelagic sargassumResponse surface methodologySodium alginateExtractionOptimizationBox-behnken design
collection DOAJ
language English
format Article
sources DOAJ
author Akeem Mohammed
Arianne Rivers
David.C. Stuckey
Keeran Ward
spellingShingle Akeem Mohammed
Arianne Rivers
David.C. Stuckey
Keeran Ward
Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
Data in Brief
Pelagic sargassum
Response surface methodology
Sodium alginate
Extraction
Optimization
Box-behnken design
author_facet Akeem Mohammed
Arianne Rivers
David.C. Stuckey
Keeran Ward
author_sort Akeem Mohammed
title Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
title_short Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
title_full Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
title_fullStr Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
title_full_unstemmed Datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
title_sort datasets on the optimization of alginate extraction from sargassum biomass using response surface methodology.
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2020-08-01
description This article presents data associated with the extraction of sodium alginate from waste Sargassum seaweed in the Caribbean utilizing an optimization approach using Response Surface Methodology [1]. A Box-Behnken (BBD) Response Surface Methodology using Design Expert 10.0.3 software on the alkaline extraction process was used. Data consists of the effects of 4 process variables (temperature, extraction time, alkali concentration and excess volume of alkali: dried seaweed) on the yield of sodium alginate. The model was validated, and extracts were characterization using High Performance Liquid Chromatography (HPLC), Gel Permeation Chromatography (GPC), Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR). The data illustrates the applicability of our model in potentially valorizing this waste product into a valuable resource. Furthermore, our methodology can be applied to other macroalgae for efficient extraction of sodium alginate of commercial quality.
topic Pelagic sargassum
Response surface methodology
Sodium alginate
Extraction
Optimization
Box-behnken design
url http://www.sciencedirect.com/science/article/pii/S2352340920307319
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