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Thursday, July 6 • 5:35pm - 5:40pm
Multivariate statistics for PAT data analysis: short overview of existing R packages and methods

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Keywords: chemoinformatics, multivariate data analysis, time series data
Process analytical technology (PAT) is defined as a system for designing, analyzing, and controlling pharmaceutical manufacturing processes through timely measurements (i.e., during processing) of critical quality and performance attributes. PAT poses many data analysis challenges, such as appropriate techniques for data preprocessing, quantification and integration with the external information (e.g. DoE factors). Currently available R packages in the public repositories allow for integrated analysis and implementation of analytic pipelines in the industrial setting. In this presentation we will focus on a specific application of using infrared (IR) spectroscopy technology for synthesis reaction monitoring and multivariate analysis of IR spectra by using matrix factorization techniques (e.g. principal component analysis, factor analysis for bicluster acquisition, non-negative matrix factorization, time series factor analysis and curve resolution). Unlike a supervised partial least squares technique - which is commonly used in chemometrics - this is a set of unsupervised techniques implemented in R which allow to extract and explore the most essential information in the IR spectra. The ability to extract this type of information reduces the task of monitoring about 600 highly correlated points per spectrum to monitoring a few independent factor scores only. For the proof of concept, the scores from several matrix factorization methods are compared to the known compound concentrations and the differences and commonalities of the different approaches are discussed.


Thursday July 6, 2017 5:35pm - 5:40pm
4.01 Wild Gallery