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Advisor(s)
Abstract(s)
Towards the optimization and control of bioprocesses, as the culture step of recombinant microorganisms, it is crucial to develop high-throughput monitoring techniques enabling the acquisition of a large data sets of information, as the ones based on mid infrared (MIR) spectroscopy. To maximize the knowledge associated to this this new large-scale data, it is also relevant to apply machine learning techniques, as Partial Least-Squares Discriminant Analysis (PLS-DA). In the present work, Principal Component Analysis followed by PLS-DA were applied to discriminate different growth phases of recombinant Saccharomyces cerevisiae along the production of an heterologous protein, conducted in bioreactor and monitored by high-throughput MIR spectroscopy. It was possible to derive PLS-DA models enabling to discriminate, from the MIR spectra, the yeast cells according to its metabolic status associated to the culture growth phase with an accuracy, sensitivity, and specificity between 83% and 100%. The optimised PLS-DA presented very low calibration errors, of 97% and 100% based on a cross-validation and an independent data-set, respectively. In conclusion, it was possible to build a PLS-DA model discriminating the cells metabolic status that will promote the knowledge of the bioprocess and future better control and optimization procedures.
Description
Keywords
Saccharomyces cerevisiae Partial Least-Squares Discriminant Analysis (PLS-DA) MIR spectroscopy
Pedagogical Context
Citation
SAMPAIO, Pedro N.; CALADO, Cecília R. C. – Classification of recombinant Saccharomyces cerevisiae cells using PLS-DA modelling based on MIR spectroscopy. In 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG). Lisbon, Portugal: IEEE, 2019. ISBN 978-1-5386-8506-8. Pp. 1-4
Publisher
Institute of Electrical and Electronics Engineers