(untitled) (Journal of Environmental Bioremediation and Toxicology, 2024)
The microbial detoxification process of molybdenum reduction to molybdenum blue directly correlates with bacterial development during Mo-blue production. The reduction process can be modeled mathematically to determine essential kinetic parameters, which include the specific Mo-blue production rate and theoretical maximum Mo-blue generation, as well as the possible effects of high molybdenum concentrations on the lag phase of reduction. Applying linearization techniques including natural logarithmic transformations remains common, but these methods deliver imprecise results that produce only approximate values for specific parameters such as the specific growth rate or specific Mo-blue production rate. This research introduced a complete range of nonlinear growth and models to study Mo-blue production from Burkholderia sp. strain Dr.Y27. The research incorporated nine growth models, including the modified Gompertz and modified Logistic. The modified logistic model demonstrated the highest fit to the Mo-blue production curve of Burkholderia sp. strain Dr.Y27 based on multiple statistical performance criteria which included root-mean-square error (RMSE), Marquardt percent standard deviation (MPSD), adjusted coefficient of determination (adjR²), bias factor (BF), accuracy factor (AF), Bayesian information criterion (BIC), Hannan-Quinn criterion (HQC), and the corrected Akaike information criterion (AICc). The Multi-Objective Optimization by Ratio Analysis or MOORA approach based on Ratio Analysis was applied to enhance model selection and is the first method used to find the best model for primary modeling of bacterial growth or Mo-blue production rate. The fitted model generated essential parameters to serve as a solid foundation for developing additional models that describe how environmental variables and substrate concentrations affect Mo-blue production.