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Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control
Author(s) -
DuranVillalobos Carlos A.,
Ogonah Olotu,
Melinek Beatrice,
Bracewell Daniel G.,
Hallam Trevor,
Lennox Barry
Publication year - 2021
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.17257
Subject(s) - multivariate statistics , principal component analysis , partial least squares regression , statistical process control , multivariate analysis , yield (engineering) , process (computing) , process analytical technology , data mining , computer science , statistics , biological system , mathematics , artificial intelligence , engineering , work in process , biology , materials science , operations management , metallurgy , operating system
The optimization and control of cell free protein synthesis (CFPS) presents an ongoing challenge due to the complex synergies and nonlinearities that cannot be fully explained in first principle models. This article explores the use of multivariate statistical tools for analyzing data sets collected from the CFPS of Cereulide monoclonal antibodies. During the collection of these data sets, several of the process parameters were modified to investigate their effect on the end‐point product (yield). Through the application of principal component analysis and partial least squares (PLS), important correlations in the process could be identified. For example, yield had a positive correlation with pH and NH 3 and a negative correlation with CO 2 and dissolved oxygen. It was also found that PLS was able to provide a long‐term prediction of product yield. The presented work illustrates that multivariate statistical techniques provide important insights that can help support the operation and control of CFPS processes.