Get the most out of your data—and don’t get lostThere are several software packages on the market for multivariate analysis (MVA) and DoE. Bioprocess data can be imported into these tools for evaluation. The packages use statistical methods to find the best combination of input factors, to describe their effect on the examined output factors, and to help us to understand how the parameters interact. They thus provide a starting point for further iterations.
One popular modeling approach is principal component analysis (PCA), a method for identifying a smaller number of uncorrelated variables, called "principal components", from a large set of data. The goal is to explain the maximum amount of variance with the fewest principal components.
Another approach, used more often, is the partial least squares (PLS) method, which reduces the predictors to a smaller set of uncorrelated components and performs least squares regression on them, rather than the original data. Both approaches are introduced in .