Suppr超能文献

通过乳化破坏引发的提取的最优化的 D-最优混合设计用于微波诱导等离子体光学发射光谱法测定食用植物油中的多元素。

D-optimal mixture design for the optimization of extraction induced by emulsion breaking for multielemental determination in edible vegetable oils by microwave-induced plasma optical emission spectrometry.

机构信息

Universidade Federal Do Recôncavo da Bahia, Centro de Ciências Exatas e Tecnológicas, Campus Universitário de Cruz Das Almas, (CEP 44380-000), Cruz Das Almas, Bahia, Brazil.

Universidade Federal da Bahia, Instituto de Química, Departamento de Química Analítica, Campus Universitário de Ondina, (CEP 40170-115), Salvador, Bahia, Brazil.

出版信息

Talanta. 2020 Nov 1;219:121218. doi: 10.1016/j.talanta.2020.121218. Epub 2020 May 29.

Abstract

A sample pretreatment based on an extraction process by emulsion breaking for multi-element determination in edible oils was developed. The determination of eight trace elements (Al, Ba, Cu, Cr, P, Ni, Ti, and Zn) was carried out by microwave-induced plasma optical emission spectrometry (MIP OES) after the extraction procedure. A D-optimal mixture experimental design was used to obtain the best experimental conditions for the extraction induced by emulsion breaking (EIEB). The proportion of HNO solution, Triton X-100 solution and sample was evaluated in a multivariate manner. The best recovery efficiency was obtained with 1.0 mL of 30% (v/v) HNO, 1.0 mL of 30% (w/v) Triton-X 100 and 3.0 mL of the sample. The precisions, established as the relative standard deviation (RSD, %), were better than 2.5% for all analytes. The developed method was applied to the analysis of commercial vegetable oils with low limits of detection and good precision.

摘要

建立了一种基于破乳萃取的样品前处理方法,用于食用油中多种元素的测定。采用微波诱导等离子体光学发射光谱法(MIP OES),在萃取后对八种痕量元素(Al、Ba、Cu、Cr、P、Ni、Ti 和 Zn)进行了测定。采用 D-最优混合实验设计,获得了破乳诱导萃取(EIEB)的最佳实验条件。以多元方式评估了 HNO 溶液、Triton X-100 溶液和样品的比例。采用 1.0 mL 30%(v/v)HNO、1.0 mL 30%(w/v)Triton-X 100 和 3.0 mL 样品,可获得最佳的回收率效率。所有分析物的精密度(以相对标准偏差(RSD,%)表示)均优于 2.5%。该方法应用于商业植物油的分析,具有较低的检出限和良好的精密度。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验