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Multivariate Data Analysis for Advancing the Interpretation of Bioprocess Measurement and Monitoring Data

Lookup NU author(s): Professor Jarka Glassey


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The advances in measurement techniques, the growing use of high-throughput 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.

Publication metadata

Author(s): Glassey J

Publication type: Book Chapter

Publication status: Published

Book Title: Measurement, Monitoring, Modelling and Control of Bioprocesses

Year: 2013

Volume: 132

Pages: 167-191

Print publication date: 05/01/2013

Series Title: Advances in Biochemical Engineering/Biotechnology

Publisher: Springer-Verlag

Place Published: Berlin


DOI: 10.1007/10_2012_171

Library holdings: Search Newcastle University Library for this item

ISBN: 9783642368370