Acidogenic Fermentation of Brewers’ Spent Grain Monitored through Two-Dimensional Fluorescence Spectroscopy

Eliana C. Guarda, Eunice Costa, Cátia Gil, Catarina L. Amorim, Cláudia F. Galinha, Anouk F. Duque, Paula M. L. Castro, Maria A. M. Reis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Biological systems are commonly controlled and monitored through offline and time-consuming tools, which often impairs an effective and real-time response to counteract system disturbances. The feasibility of using two-dimensional (2D) fluorescence spectroscopy as a non-invasive, non-destructive, and real-time procedure to monitor the acidogenic fermentation of brewer’s spent grain (BSG) in a granular sludge reactor was evaluated. For that, the effect of pH fluctuations on the system response was used as a model to ascertain the 2D fluorescence spectroscopy applicability to monitor the process performance, namely, to predict the fermentation products (FP) and the soluble protein (SProt) concentrations in the effluent stream through mathematical analysis. The pH fluctuations over the course of the reactor’s operation altered the granules’ microbiome composition, leading to different effluent FP profiles. Fluorescence excitation-emission matrices (EEMs) were used with projection to latent structures (PLS) modeling to predict the FP and SProt concentrations in the effluent with average errors below 0.75 and 0.43 g L-1, respectively. Both models were able to capture the tendency of the data even when the accuracy of prediction was not so high. The combined approach of using 2D fluorescence spectroscopy and mathematical analysis seemed promising for real-time monitoring of the acidogenic fermentation of complex substrates.

Original languageEnglish
Pages (from-to)7398-7406
Number of pages9
JournalACS Sustainable Chemistry and Engineering
Issue number19
Publication statusPublished - 15 May 2023


  • 2D fluorescence spectroscopy
  • fluorescence excitation-emission matrices
  • microbiome dynamics
  • multivariate regression
  • organic acids
  • waste valorization


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