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Published on: January 1, 2016
Experimental designs for solid-phase microextraction method development in bioanalysis: A review.
Giorgio Marrubini1, Stefano Dugheri2, Giovanni Cappelli3
1Department of Drug Sciences, University of Pavia, Via Taramelli 12, Pavia, 27100, Italy.
This review examines experimental design in bioanalytical solid-phase microextraction (SPME) method development. Researchers frequently use basic factorial designs and commercial software, with less frequent use of advanced models.
Area of Science:
- Analytical Chemistry
- Method Development
Background:
- Bioanalytical method development relies on robust experimental design.
- Solid-phase microextraction (SPME) is a key technique in bioanalysis.
- Previous reviews highlighted specific trends in experimental design for SPME.
Purpose of the Study:
- To provide an updated review of experimental design strategies in bioanalytical SPME method development.
- To critically analyze the characteristics and application of different SPME approaches.
- To identify trends and gaps in the use of design of experiments (DoE) from 2009 to 2019.
Main Methods:
- Comprehensive literature search of publications from 2009 to 2019.
- Analysis of experimental designs employed in bioanalytical SPME method development.
- Critical discussion of different SPME approaches and their associated designs.
Main Results:
- Two-level full factorial designs (fewer than 5 factors) are most common for initial screening.
- Rotatable central composite and Box-Behnken designs are popular for response surface methodology.
- Advanced designs like definitive screening and D-Optimal designs were not reported.
- Heavy reliance on commercial software for experimental design, analysis, and reporting was observed.
Conclusions:
- The development of bioanalytical SPME methods predominantly utilizes simpler experimental designs.
- There is a notable underutilization of more complex and efficient design of experiments (DoE) methodologies.
- Future research could benefit from exploring and implementing advanced DoE strategies for optimizing SPME methods.
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