ABSTRACT
Bioprocess development is both costly and time-consuming due to the complexity of cell culture, various sources of variability in both input materials and process parameters, and control of organism-mediated production. As a result, it is critical to use advanced technologies such as process analytical technology (PAT) and statistical methods to gain deep understanding of the process. Multivariate analysis techniques, such as multiple regression model, principal component analysis (PCA), and partial least square (PLS), provide a set of statistical tools that can be effectively used to identify key material attributes and process parameters for process development, process optimization, and control. These statistical methods not only have the advantage of assessing individual effects and their interactions but also provide solutions to issues related to multidimensionality, missing data, and collinearity among variables under investigation. This chapter introduces a set of multivariate analysis methods for bioprocess development. The use of these methods is illustrated through several real-life examples.
