Fundamentals of Design of Experiments and Optimization: Data Modeling in Response Surface Methodology

CHIAPPINI, FABRICIO A.; AZCARATE, SILVANA M.; TEGLIA, CARLA M.; GOICOECHEA, HÉCTOR C.

Introduction to Quality by Design in Pharmaceutical Manufacturing and Analytical Development

Springer

Año: 2023; p. 67 - 89

The experimental data collected according to a proper statistical designconstitute the input for data modeling, which represents the last step in responsesurface methodology (RSM). Data modeling consists in applying a set of statisticalmethods that enable the analyst to thoroughly study the relation between experimental factors and responses. Essentially, this task is carried out by buildingempirical models, which are then used to make predictions and investigate possibleoptimal experimental regions. In this chapter, important concepts of multivariatestatistics related to data modeling in RSM are introduced. In particular, models basedon multiple linear regression, MLR (parametric), and artificial neural networks,ANN (non-parametric), are presented, which are the two most important modelingapproaches in RSM. Additionally, relevant issues regarding model validation, outlier diagnosis, prediction, and interpretation are discussed, and mathematicalmethods for single and multiple response optimization are briefly described. Finally,some of the most popular software for RSM implementation are summarized.