Application of Multivariate Techniques in Optimization of Spectroanalytical Methods

Abstract

The present article describes fundamentals and applications of multivariate techniques used for the optimization of analytical procedures and systems involving spectroanalytical methods such as flame atomic absorption spectrometry (FAAS), electrothermal atomic absorption spectrometry (ETAAS), inductively coupled plasma optical emission spectrometry (ICP OES), and inductively coupled plasma mass spectrometry (ICP-MS), considering the main steps of a chemical analysis. This way, applications of experimental designs in optimization of sampling systems, digestion procedures, preconcentration procedures, instrumental parameters of quantification steps of analytical methods, and robustness tests have been summarized in this work.

A preconcentration system for determination of copper and nickel in water and food samples employing flame atomic absorption spectrometry

“…In the recent years the multivariate techniques have been used for optimization of analytical methods [20][21][22][23][24][25]. Several review papers [24][25][26] have been published at this subject.…”

Full Factorial Design Approach to Hg(II) Adsorption onto Hydrogels

“…From the P value defined as the lowest level of significance leading to rejection of the null hypothesis [5], it appears that the small P values (<0.05) mean that not all the main effects, two-way interactions, and three-way interactions are zero at the 5 % significance level. Four-way interactions are insignificant ( pvalues > 0.05) at the 5 % significance level.…”

“…where y is the response, x i and x j are coded variables, β's are regression coefficients, and ε is a random error [3][4][5][6][7][8].…”

Cercis siliquastrum Tree Leaves as an Efficient Adsorbent for Removal of Ag(I): Response Surface Optimization and Characterization of Biosorption

“…CCD is an RSM, which used frequently as a multivariate optimization approach in various fields of chemistry [31][32][33][34][35]. Several superimposed designs are merged to construct the CCD; a full factorial design (2 k ), a star design (axial points 2k), and replicates at center points C 0 , where k denotes the number of factors and C 0 replicates at center points.…”

“…Therefore, the total number of experiments (N) in CCD equals to N ¼ 2 k þ 2k þ C 0 . CCD inherently has both orthogonality and rotatability resources [21][22][23][24]35]. Orthogonality refers to the matrix design where its elements are orthogonal with each other (correlation coefficients between them are zero except for interactions) [36].…”