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

Q1 Environmental Science

Bioresource Technology Reports Pub Date : 2025-10-08 DOI:10.1016/j.biteb.2025.102342

Apurva Kadia, Urvish Chhaya

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Abstract

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.

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优化财团AKUC-1对甲酚生物修复的潜力：一种统计方法与基于人工智能的预测相结合

生物降解是一种经济可行、环境友好、可持续的去除环境基质中有机污染物的方法。这项工作探索了AKUC-1联盟在甲酚异构体混合物（邻甲酚、间甲酚和对甲酚）生物修复方面的潜力。该联合体的微生物组分析显示厚壁菌门和芽孢杆菌属的优势。为了确定关键参数并提高甲酚异构体的降解效率，在机器学习工具支持向量机（SVM）的支持下，利用响应面法-中心复合设计（RSM-CCD）对AKUC-1进行了统计优化。方差分析证实了RSM-CCD模型的准确性，f值为56.76，p值小于0.0001，表明模型具有稳健性。在最佳条件下，在3.50 g L−1蔗糖、0.0075 g L−1 FeCl3和0.0075 g L−1 CaCl2的条件下，该菌体对600 ppm甲酚异构体的降解率为82.54%，是未优化培养基的2.58倍。

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