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Artificial intelligence-assisted optimization of extraction enhances the biological activity of Phylloporia ribis

Summary

Scientists optimized how to extract beneficial compounds from Phylloporia ribis mushrooms using artificial intelligence, finding that an AI-assisted method produced extracts with stronger antioxidant power and cancer-fighting properties than traditional statistical approaches. The optimized extracts showed promise in fighting free radicals, potentially supporting brain health against Alzheimer’s disease, and slowing cancer cell growth. This research demonstrates how combining mushroom extraction with modern AI technology could lead to more effective natural medicines.

Background

Phylloporia ribis is a largely understudied medicinal mushroom species with limited data on its bioactive profile and extraction efficiency. Medicinal mushrooms contain bioactive components with antioxidant and anti-inflammatory properties that require optimized extraction processes to maximize therapeutic efficacy and yield.

Objective

To optimize the extraction of Phylloporia ribis using both Response Surface Methodology (RSM) and an integrated Artificial Neural Network-Genetic Algorithm (ANN-GA) approach, and to assess the biological functions of the resulting extracts.

Results

ANN-GA optimization identified ideal parameters of 57.473°C, 9.418 hours, and 45.114% ethanol/water ratio, producing extracts with higher phenolic content (gallic acid: 10,491.72 mg/kg, quercetin: 9,960.53 mg/kg) and superior antioxidant activity compared to RSM-optimized extracts. ANN-GA extracts demonstrated stronger FRAP and DPPH radical scavenging activities, enhanced cholinesterase inhibition, and dose-dependent antiproliferative effects on cancer cells.

Conclusion

The ANN-GA method proved superior to traditional RSM in optimizing P. ribis extraction, enhancing phenolic compound recovery and biological efficacy. These findings suggest ANN-GA is a promising tool for maximizing the yield and bioactivity of medicinal mushroom extracts, with potential applications in pharmaceutical and nutraceutical development.
  • Published in:Scientific Reports,
  • Study Type:Experimental Optimization Study,
  • Source: PMID: 41271878, DOI: 10.1038/s41598-025-25130-0
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