Work

Bayesian inference on fermentation kinetics: Comparative analysis with frequentist approach

Year: 2026

Type: article

Abstract: • Construct a Bayesian hierarchical regression model with modified hyperbolic secant function. • Compare Bayesian approach to frequentist counterpart on fermentation data. • Priors ensure biological plausibility where frequentist estimates fail. • Bayesian inference resolves convergence issues in sparse data scenarios. This study explores the application of Bayesian inference to fermentation kinetic modeling, focusing on two representative batch systems: glycerol-glucose co-fermentation for 1,3-propanediol (1,3-PDO) production and dark fermentation for biohydrogen production. Modified hyperbolic secant functions were implemented to model metabolite concentrations obtained from above-noted experiments. Then, the performance of a Bayesian hierarchical model to that of frequentist nonlinear least squares fitting was compared. The results indicate that while the frequentist approach demonstrates high computational efficiency and suitability for short-term prediction, the Bayesian approach offers significant advantages in terms of parameter interpretability, robustness under limited data, and uncertainty quantification. Overall, Bayesian methods enhance the construction of interpretable, stable, and data-efficient fermentation kinetic models.

Source: Bioresource Technology

DOI: 10.1016/j.biortech.2026.134012

Authors: Xiang Li, Yen-Han Lin

Institution: University of Saskatchewan

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,880