AI-Guided Precision Biofortification of Oyster Mushroom: Optimizing Selenium Uptake, Yield, and Nutritional Quality

Global micronutrient deficiencies, mostly Selenium (Se), present an important public health challenge known as hidden hunger. While Pleurotus ostreatus (Oyster mushroom) is an efficient biological factory for mineral biotransformation, the Biofortification Paradox. The metabolic trade-off between selenium accumulation and biomass yield limits large-scale production. This study introduces the AI-Guided Precision Biofortification (Artificial Intelligence-PB) framework. High-fidelity biochemical data, including amino acid profiling (PTH analysis) and lipid fingerprinting (GC-MS), are integrated into a Multi-Task Deep Learning model. Quantitative analysis revealed a protein dilution effect at high selenium concentrations, with a 5.15% reduction in total nitrogen and significant declines in glutamic and aspartic acids. The AI-PB model identified a Pareto-optimal threshold of 54.3 mg/kg for Na2SeO3. Operating at this level resulted in a 15.2% increase in essential amino acids and maximized organic selenium incorporation while maintaining biological efficiency above 82%. The model demonstrated exceptional predictive accuracy and confirmed the retention of high-value bioactive terpenes, such as squalene, despite selenium-induced oxidative stress. In deconstructing the metabolic windows of mineral toxicity, the AI-PB framework provides a scalable blueprint for Agri-Tech 4.0. This research demonstrates that integrating computational intelligence with fungal biology can resolve long-standing trade-offs in functional food production, turning mushroom cultivation into a high-precision system for the sustainable delivery of mineral elements for global wellness.