Bioengineering for polycyclic aromatic hydrocarbon degradation by Mycobacterium litorale: Statistical and artificial neural network (ANN) approach

Abstract

The study deals with the modeling for enhancing fluoranthene biodegradation using a conventional process-centric approach response surface methodology, and a comparatively newer, data-centric approach artificial neural network. The study deals with the comparison of two models for enhancing fluoranthene biodegradation using Mycobacterium litorale. The study involves step wise optimization protocol incorporating screening of medium components. The variables of interest were CaCl2, KH2PO4 and, NH4NO3, screened based on Plackett-Burman model. The second step involves the CCD matrix, resulting in 51.21% degradation on the 3rd day with R2 value 0.9882. The non-linear multivariate ANN has model predicted 51.28% degradation with 0.9987 R2 value. The root mean square error and mean absolute percentage error values were found to be 0.3234 and 0.5715, respectively. The entire approach has resulted in 51.28% degradation on 3rd day as compared to an unoptimized degradation (26.37%) on 7th day. The values obtained by ANN network were more precise, reliable and reproducible, compared to the conventional RSM model, proving the superiority of ANN model over RSM model. The study thus widens the current understanding of the scientific community for the fabrication, forecasting precisely simulated biological process for green technology.