MATEC Web Conf.
Volume 333, 2021The 18th Asian Pacific Confederation of Chemical Engineering Congress (APCChE 2019)
|Number of page(s)||4|
|Published online||08 January 2021|
Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis
Biotechnology and Food Chemistry Course Program, School of Regional Innovation and Social Design Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido, 090-8507, Japan.
2 Department of Biotechnology and Environmental Chemistry, Graduate School of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido, 090-8507, Japan.
* Corresponding author: email@example.com
Natural media are often used for various commercial bioprocesses by manufacturers to cut raw material cost. However, the components of the raw materials varies between lot-to-lots and brand-to-brands. The varieties of raw materials influence to the cell growths and materials productivities, and results in unstable production across batches in manufacturing processes. To ensure the quality of raw materials among batches, it is necessary to perform a laboratory screening to purchasing the optimal one, and ensure a desirable performance in industrial process. To solve the serious problems in bioprocesses, it is developing that a modelling methodology using composition of raw materials, named us “substratome”, obtained by non-targeted metabolomicslike methods can estimate the cell growth and bio-productions. Here, we will present that two model studies:  Escherichia coli growths have been estimated from hydrophilic components in yeast extract obtained by gas chromatography-mass spectrometry (GC-MS), and  bioethanol production have been estimated by the volatile components in corncob and corn stover hydrolysates obtained by GC-MS; by partial least square regression (PLS-R). Additionally, we will present preliminary results to solve the same issues by using artificial intelligence.
© The Authors, published by EDP Sciences, 2021
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