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

机译:使用响应表面方法从Sargassum生物量优化藻酸盐萃取的数据集。

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摘要

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.
机译:本文介绍了利用响应面方法的优化方法从加勒比地区的废Sargassum海藻中提取藻酸钠的数据相关[1]。使用碱性提取过程的设计专家10.0.3软件的Box-Behnken(BBD)响应表面方法。数据包括4个过程变量(温度,提取时间,碱浓度和碱的过量体积的碱:干海藻)的效果。验证了该模型,并采用高效液相色谱(HPLC),凝胶渗透色谱(GPC),傅里叶变换红外光谱(FTIR)和核磁共振(NMR)提取物。这些数据说明了我们模型的适用性,使得潜在的储存该废物产品变为有价值的资源。此外,我们的方法可以应用于其他大理石,以有效提取商业质量的藻酸钠。

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