(untitled) (Asian Journal of Plant Biology, 2022)
Dyes plays an important role in our everyday life. From manufacturing plastics, paints, textile and even pills contain traces of dye used. With the ever-increasing demand for dye with growing world populations, the use of synthetic dyes has grown linearly. Bioremediation of dyes using microorganisms is on the rise. The ability to accurately predict the rate of bioremediation relies upon the gathering of the accurate rate of decolourisation, which is often inaccurately obtained by natural logarithm transformation of the decolourisation process over time. In this instance, a nonlinear regression of the curve needs to be carried out utilising available rate models. Hence, various primary models such as modified Logistic, modified Gompertz, modified logistics, modified Richards, modified Schnute, Baranyi-Roberts, Buchanan-3-phase, von Bertalanffy and the Huang models were utilized to fit the specific decolourisation rate. Several models failed to converge and was omitted and only Huang, Baranyi-Roberts, modified Gompertz, modified Richards and modified Logistics were able to model the data while other models failed to converge and were omitted. The best model based on statistical analysis was Baranyi Roberts with the highest value for Adjusted Coefficient of Determination and the lowest values for RMSE, AICc, HQC and BIC and the closest value to 1.0 for accuracy and bias factors. The Baranyi-Roberts fitted curve was found to conform to normality tests and is adequate to be used to fit the experimental data. The parameters obtained from this exercise can be utilized for further secondary modelling exercise to gleam information on how substrate (dye) affect the rate of decolourisation of the substrate.