Test for the presence of autocorrelation in the modified Gompertz model used in the fitting the growth of sludge microbes on PEG 600

Abstract

Polyethylene glycols (PEGs), are nephrotoxic, and are employed in numerous industrial sectors. Their biodegradation by microbes could be a potential tool for bioremediation. A lot of bacterial growth reports overlook primary modelling despite the fact that modelling exercises can expose important parameters. Earlier, we have employed several growth models to model the growth of sludge microbes on PEG 600. We found out that the modified Gompertz model via nonlinear regression utilizing the least square method was the most effective model to describe the growth curve. Nonlinear regression using the least square method generally utilizes the assumption that data points do not depend on each other or the value of a data point is not dependent on the value of preceding or proceeding data points or do not exhibit autocorrelation. In this work, the Durbin–Watson statistic to check for the presence of autocorrelation in the growth model was carried out.