Multivariate data analysis for advancing the interpretation of bioprocess measurement and monitoring data

Abstract

The advances in measurement techniques, the growing use of highthroughput screening and the exploitation of 'omics' measurements in bioprocess development and monitoring increase the need for effective data pre-processing and interpretation. The multi-dimensional character of the data requires the application of advanced multivariate data analysis (MVDA) tools. An overview of both linear and non-linear MVDA tools most frequently used in bioprocess data analysis is presented. These include principal component analysis (PCA), partial least squares (PLS) and their variants as well as various types of artificial neural networks (ANNs). A brief description of the basic principles of each of the techniques is given with emphasis on the possible application areas within bioprocessing and relevant examples. © Springer-Verlag Berlin Heidelberg 2012.