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Model-aided targeted volatile fatty acid production from food waste using a defined co-culture microbial community

Authors
  • Regueira López, Alberte
  • Turunen, R.
  • Vuoristo, K.S.
  • Carballa, M.
  • Lema, J.M.
  • Uusitalo, J.
  • Mauricio-Iglesias, M.
Publication Date
Jan 01, 2023
Source
Ghent University Institutional Archive
Keywords
Language
English
License
Green
External links

Abstract

The production of volatile fatty acids (VFA) is gainingmomentumdue to their central role in the emerging carboxylate platform. Particularly, the production of the longest VFA (from butyrate to caproate) is desired due to their increased economic value and easier downstream processing. While the use of undefined microbial cultures is usually preferred with organic waste streams, the use of defined microbial co-culture processes could tackle some of their drawbacks such as poor control over the process outcome, which often leads to low selectivity for the desired products. However, the extensive experimentation needed to design a co-culture system hinders the use of this technology. In this work, a workflow based on the combined use of mathematical models and wet experimentation is proposed to accelerate the design of novel bioprocesses. In particular, a co-culture consisting of Pediococcus pentosaceus and Megaphaera cerevisiae is used to target the production of high-value odd- and even-carbon VFA. An unstructured kinetic model was developed, calibrated and used to design experiments with the goal of increasing the selectivity for the desired VFA, whichwere experimentally validated. In the case of even-carbon VFA, the experimental validation showed an increase of 38% in caproate yield and, in the case of enhanced odd-carbon VFA experiments, the yield of butyrate and caproate diminished by 62% and 94%, respectively, while propionate became one of the main end products and valerate yield value increased from 0.007 to 0.085 g(valearte) per g(consumed) (sugar). The workflow followed in this work proved to be a sound tool for bioprocess design due to its capacity to explore and design new experiments in silico in a fast way and ability to quickly adapt to new scenarios.

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