Abstract
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.
Original language | English |
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Title of host publication | 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538685068 |
DOIs | |
Publication status | Published - 15 Apr 2019 |
Event | 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Lisbon, Portugal Duration: 22 Feb 2019 → 23 Feb 2019 |
Publication series
Name | 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings |
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Conference
Conference | 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 22/02/19 → 23/02/19 |
Bibliographical note
Publisher Copyright:© ENBENG 2019. All Rights Reserved.