Optimization of Biodegradation of Common Bean Biomass for Fermentation Using Trichoderma asperellum WNZ-21 and Artificial Neural Networks

Abstract

This study showcases a promising approach to sustainably unlocking plant biomass residues by combining biodegradation with artificial intelligence to optimize the process. Specifically, we utilized the definitive screening design (DSD) and artificial neural networks (ANNs) to optimize the degradation of common bean biomass by the endophytic fungus Trichoderma asperellum WNZ-21. The optimized process yielded a fungal hydrolysate rich in 12 essential and non-essential amino acids, totaling 18,298.14 μg/g biomass. GC-MS analysis revealed four potential novel components not previously reported in microbial filtrates or plants and seven components exclusive to plant sources but not reported in microbial filtrates. The hydrolysate contained phenolic, flavonoid, and tannin compounds, as confirmed by FT-IR analysis. High-resolution transmission electron microscopy depicted structures resembling amino acid micelles and potential protein aggregates. The hydrolysate exhibited antioxidant, antibacterial, and anticancer properties and innovatively induced apoptotic modulation in the MCF7 cancer cell line. These findings underscore the potential of ANN-optimized fermentation for various applications, particularly in anticancer medicine due to its unique composition and bioactivities. The integration of the DSD and ANNs presents a novel technique for biomass biodegradation, warranting the valorization of plant biomass and suggesting a further exploration of the new components in the fungal hydrolysate. This approach represents the basic concept for exploring other biomass sources and in vivo studies.