Experimental designs and their recent advances in set-up, data interpretation, and analytical applications

Area of Science:

• Chemistry
• Chemical Engineering
• Statistics

Background:

• Experimental design is crucial for efficient scientific research.
• Traditional methods may not always be optimal for complex problems.
• Need for advanced strategies in data interpretation and experimental setup.

Purpose of the Study:

• To review and discuss the setup and data interpretation of various experimental designs.
• To explore advanced screening designs like D-optimal and supersaturated designs.
• To present advanced data interpretation methods and analytical applications.

Main Methods:

• Discussion of screening, response surface, and mixture designs.
• Inclusion of D-optimal and supersaturated designs for screening.
• Adaptation of Dong's algorithm and estimation of factor effects from supersaturated designs.

Main Results:

• Comprehensive overview of experimental design methodologies.
• Demonstration of advanced techniques for efficient data analysis.
• Highlighting applications in separation science and process optimization.

Conclusions:

• Advanced experimental designs and data interpretation methods enhance research efficiency.
• These techniques are valuable for optimization in formulation, product, and process development.
• The review provides a guide for applying these methods in analytical chemistry and beyond.