Optimization Approaches to the Determination of Arsenic and Selenium by Hydride Generation and ICP-AES

Abstract

Simplex optimization, factorial design, regression analysis, and canonical analysis are applied to the determination of arsenic and selenium by hydride generation with inductively coupled plasma atomic emission spectrometry. For each element, four factors governing the operation of the plasma (outer, intermediate, and carrier gas flow rates, and rf power) and two chemical factors governing the hydride generation (quantity of acid and of sodium borohydride) were optimized by the simplex algorithm. A mapping study was conducted for the two chemical factors which consisted of a factorial design followed by regression analysis and canonical analysis. The results provided precise locations of the optima and gave information regarding the effects of the two factors.

Multivariate study in chemical vapor generation for simultaneous determination of arsenic, antimony, bismuth, germanium, tin, selenium, tellurium and mercury by inductively coupled plasma optical emission spectrometry

“…On the other hand, multivariate techniques are much more promising in handling such complex situations, and acquire a great deal of information of good statistical quality with a limited number of experiments. Parker et al 25 checked four multivariate methods, factorial design, regression and canonical analysis and SIMPLEX, to optimize the determination of As and Se by CVG-ICP-OES. SIMPLEX optimization was successfully applied to find optimal conditions for CVG coupled with atomic absorption spectrometry.…”

Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks

“…The approaching to the optimum conditions with this new set of experiments will be slower than the previous one, because of the smaller value of Δ. Unfortunately, previous workers using SIMPLEX for similar systems have not informed the number of experiments they needed. , …”

“…In many cases, this method approaches to the goal of maximizing the response but without achieving the optimum value . One of the reasons of the unsuccessful results is that this method does not take into account the interactions between variables. , Furthermore, this method demands a great number of experiments time-consuming process. Some other chemometrics methods can be used for this purpose, and SIMPLEX, for instance, has been previously applied in a successful manner. − The work of Parker et al deserves special mention; the authors checked four methods, factorial design, regression and canonical analysis, and SIMPLEX, to optimize the determination of As and Se through HG-ICP-AES …”

“…5 One of the reasons of the unsuccessful results is that this method does not take into account the interactions between variables. 5,6 Furthermore, this method demands a great number of experiments time-consuming process. Some other chemometrics methods can be used for this purpose, and SIMPLEX, for instance, has been previously applied in a successful manner.…”

“…Some other chemometrics methods can be used for this purpose, and SIMPLEX, for instance, has been previously applied in a successful manner. [6][7][8] The work of Parker et al deserves special mention; the authors checked four methods, factorial design, regression and canonical analysis, and SIMPLEX, to optimize the determination of As and Se through HG-ICP-AES. 6 Artificial neural networks (ANNs) applications to chemistry have been increased in the past few years through the publication of many articles and textbooks.…”