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Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy Read chapter Read first chapter

2013 | OriginalPaper | Chapter

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

Author : Jarka Glassey

Published in: Measurement, Monitoring, Modelling and Control of Bioprocesses

Publisher: Springer Berlin Heidelberg

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Abstract

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.

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previous chapter Applying Mechanistic Models in Bioprocess Development
next chapter Design of Pathway-Level Bioprocess Monitoring and Control Strategies Supported by Metabolic Networks
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Metadata
Title
Multivariate Data Analysis for Advancing the Interpretation of Bioprocess Measurement and Monitoring Data
Author
Jarka Glassey
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Measurement, Monitoring, Modelling and Control of Bioprocesses

Print ISBN: 978-3-642-36837-0

Electronic ISBN: 978-3-642-36838-7

Copyright Year: 2013

DOI
https://doi.org/10.1007/10_2012_171

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