Response surface methodology (RSM) as a tool for optimization in analytical chemistry

Bezerra, Marcos Almeida; Santelli, Ricardo Erthal; Oliveira, Eliane Padua; Villar, Leonardo Silveira; Escaleira, Luciane Amélia

doi:10.1016/j.talanta.2008.05.019

Cited by 6,075Optimal Experimental Design MethodsSpectroscopy and Chemometric AnalysesAnalytical Methods in Pharmaceuticals

Response surface methodology (RSM) as a tool for optimization in analytical chemistry

Bezerra, Marcos Almeida, Santelli, Ricardo Erthal, Oliveira, Eliane Padua, Villar, Leonardo Silveira, Escaleira, Luciane Amélia·2008·Talanta

DOI 10.1016/j.talanta.2008.05.019

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Abstract

A review about the application of response surface methodology (RSM) in the optimization of analytical methods is presented. The theoretical principles of RSM and steps for its application are described to introduce readers to this multivariate statistical technique. Symmetrical experimental designs (three-level factorial, Box-Behnken, central composite, and Doehlert designs) are compared in terms of characteristics and efficiency. Furthermore, recent references of their uses in analytical chemistry are presented. Multiple response optimization applying desirability functions in RSM and the use of artificial neural networks for modeling are also discussed.

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RSM is considered the most relevant multivariate statistical technique for analytical optimization.
“To solve these problems, response surface methodology (RSM), a multivariate statistic technique, has been considered the most relevant in analytical optimization.”
From Polysaccharides from macroalgae: Recent advances, innovative technologies and challenges in extraction and purification (2017) · cites this paper as Bezerra et al., 2008
Traditional one-variable-at-a-time optimization is time- and cost-consuming and does not account for interactive effects among variables.
“This optimization technique is time-and cost-consuming and does not include the interactive effects among the variables studied”
From Polysaccharides from macroalgae: Recent advances, innovative technologies and challenges in extraction and purification (2017) · cites this paper as Bezerra et al., 2008

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Authors: Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A.
Affiliations: Universidade Federal Fluminense (BR)
Corresponding author: Bezerra, Marcos Almeida
ORCID: Bezerra, Marcos Almeida
Santelli, Ricardo Erthal
Venue: Talanta
Publisher: Elsevier BV
Published: 2008-09-15
Volume: 76
Issue: 5
Pages: 965-977
ISSN: 0039-9140 (ISSN-L)
DOI: 10.1016/j.talanta.2008.05.019
OpenAlex: W2088376206
Cited by: 6,075
References: 70
Citations by year: 2026: 495 · 2025: 482 · 2024: 467 · 2023: 468 · 2022: 412 · 2021: 538 · 2020: 597 · 2019: 535 · 2018: 436 · 2017: 393 · 2016: 305 · 2015: 284 · 2014: 229 · 2013: 158 · 2012: 115
FWCI: 79.31
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Talanta (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Physical Sciences › Chemistry › Analytical Chemistry › Analytical Methods in Pharmaceuticals
Keywords: response surface methodology, analytical method optimization, experimental design, Box-Behnken design, central composite design, Doehlert design, factorial design, multi-response optimization, desirability function, artificial neural networks, multivariate analysis
MeSH terms: Analysis of Variance, Chemistry Techniques, Analytical, Humans, Reproducibility of Results, Neural Networks, Computer

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Response surface methodology (RSM) as a tool for optimization in analytical chemistry

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