A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination

析因实验 实验设计 功能(生物学) Box-Behnken设计 分析物 数学 生物系统 分式析因设计 响应面法 基质(化学分析) 计算机科学 统计 化学 色谱法 进化生物学 生物
作者
Cléber Galvão Novaes,Sérgio L.C. Ferreira,João Honorato Santos Neto,Fernanda A. de Santana,Lindomar A. Portugal,Héctor C. Goicoechea
出处
期刊:Current Analytical Chemistry [Bentham Science Publishers]
卷期号:12 (2): 94-101 被引量:34
标识
DOI:10.2174/1573411011666150722220335
摘要

This paper presents a comparison between a multiple response function (MR) proposed for optimization of analytical strategies involving multi-element determinations with the desirability function D, which was proposed by Derringer and Suich in 1980. The MR function is established by the average of the sum of the normalized responses for each analyte considering the highest value of these. This comparison was performed during the optimization of an spectrometer for quantification of six elements using inductively coupled plasma optical emission spectrometry (ICP OES). Four instrumental factors were studied (auxiliary gas flow rate, plasma gas flow rate, nebulizer gas flow rate and radio frequency power). A (24) two-level full factorial design and a Box Behnken matrix were developed to evaluate the performance of the two multiple response functions. The results found demonstrated great similarity in the interpretations obtained considering the effect values of the factors calculated using the two-level full factorial design employing the two multiple responses. Also a Box Behnken design was performed to compare the applicability of the two multiple response functions in quadratic models. The results achieved demonstrated high correlation (0.9998) between the regression coefficients of the two models. Also the response surfaces obtained showed great similarity in terms of formats and experimental conditions found for the studied factors. Thus, the multiple response (MR) is presented as a simple tool, easy to manipulate, efficient and very helpful for application in analytical procedures involving multi-response. An overview of applications of this function in several multivariate optimization tools as well as in various analytical techniques is presented. Keywords: Experimental design, desirability function D, ICP OES, multiple response function.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
好运小熊发布了新的文献求助10
刚刚
asdfqwer应助smh采纳,获得10
2秒前
2秒前
yunfengwang完成签到,获得积分10
2秒前
3秒前
NARIN发布了新的文献求助10
3秒前
小蘑菇应助果冻呀采纳,获得10
4秒前
科研通AI6.2应助excellence采纳,获得10
5秒前
Jon完成签到,获得积分10
6秒前
傲娇的凡完成签到,获得积分10
6秒前
希望天下0贩的0应助heija采纳,获得30
7秒前
Shiyao_Yuan发布了新的文献求助10
9秒前
姜恒发布了新的文献求助10
9秒前
AHA完成签到,获得积分10
9秒前
好运小熊完成签到,获得积分10
9秒前
10秒前
111完成签到,获得积分10
11秒前
DennyClock完成签到,获得积分10
11秒前
Cpp完成签到 ,获得积分10
12秒前
13秒前
13秒前
yunfengwang发布了新的文献求助10
14秒前
Qiancheni完成签到,获得积分10
15秒前
123完成签到,获得积分10
16秒前
星辰大海应助Sisyphus采纳,获得10
18秒前
growl发布了新的文献求助10
18秒前
19秒前
hyper883发布了新的文献求助10
19秒前
可爱的函函应助小飞123采纳,获得10
21秒前
23秒前
笑点低的以亦完成签到,获得积分10
25秒前
26秒前
26秒前
Cheryl完成签到,获得积分10
26秒前
Ahui完成签到,获得积分10
28秒前
Romeo发布了新的文献求助10
29秒前
29秒前
诚心的香水完成签到,获得积分10
30秒前
曾斯诺发布了新的文献求助10
30秒前
科研通AI6.1应助Shiyao_Yuan采纳,获得10
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359325
求助须知:如何正确求助?哪些是违规求助? 8173258
关于积分的说明 17213936
捐赠科研通 5414420
什么是DOI,文献DOI怎么找? 2865433
邀请新用户注册赠送积分活动 1842799
关于科研通互助平台的介绍 1690973