Response surface designs for experiments in bioprocessing.
Many processes in the biological industries are studied using response surface methodology. The use of biological materials, however, means that run-to-run variation is typically much greater than that in many experiments in mechanical or chemical engineering and so the designs used require greater replication. The data analysis which is performed may involve some variable selection, as well as fitting polynomial response surface models. This implies that designs should allow the parameters of the model to be estimated nearly orthogonally. A class of three-level response surface designs is introduced which allows all except the quadratic parameters to be estimated orthogonally, as well as having a number of other useful properties. These subset designs are obtained by using two-level factorial designs in subsets of the factors, with the other factors being held at their middle level. This allows their properties to be easily explored. Replacing some of the two-level designs with fractional replicates broadens the class of useful designs, especially with five or more factors, and sometimes incomplete subsets can be used. It is very simple to include a few two- and four-level factors in these designs by excluding subsets with these factors at the middle level. Subset designs can be easily modified to include factors with five or more levels by allowing a different pair of levels to be used in different subsets.
Gilmour
Steven G
SG
School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK. s.g.gilmour@qmul.ac.uk
D001672:Biocompatible Materials; D001698:Biomedical Engineering / Q000706:statistics & numerical data*; D001699:Biometry; D001709:Biotechnology; D015233:Models, Statistical; D013499:Surface Properties
D001672:Biocompatible Materials