Optimizing potential of consortium AKUC-1 for cresol bioremediation: A statistical approach complemented with AI-based prediction

Biodegradation is an economically feasible, environmentally friendly, and sustainable method for removing organic pollutants from environmental matrices. This work explores the potential of consortium AKUC-1 for bioremediation of cresol isomer mixtures (ortho, meta-, and para-cresol). Microbiome profiling of the consortium revealed dominance of the phylum Firmicutes and the genus Bacillus. To identify key parameters and improve the degradation efficiency of cresol isomers, AKUC-1 was statistically optimized using Response Surface Methodology — Central Composite Design (RSM-CCD), with support from a machine learning tool, support vector machine (SVM). Analysis of Variance confirmed the accuracy of the RSM-CCD model, with a significant F-value of 56.76 and a p-value of less than 0.0001, indicating the model's robustness. Under optimal conditions, with 3.50 g L⁻¹ sucrose, 0.0075 g L⁻¹ FeCl₃, and 0.0075 g L⁻¹ CaCl₂, the consortium degraded 82.54 % of 600 ppm cresol isomers, which is 2.58 times higher than in unoptimized media.