ENTERPRISE AI ANALYSIS
Artificial intelligence-assisted optimization of extraction enhances the biological activity of Phylloporia ribis
Leveraging advanced AI to revolutionize extraction methodologies and unlock novel biological applications.
Executive Impact Summary
This research details the application of AI-assisted optimization to enhance the extraction efficiency and biological activity of Phylloporia ribis. By leveraging Artificial Neural Networks (ANN) and Genetic Algorithms (GA) alongside traditional Response Surface Methodology (RSM), we achieved superior yields of phenolic compounds and significantly improved antioxidant, anticholinesterase, and antiproliferative activities. This represents a substantial leap in optimizing natural product extraction for pharmaceutical and biotechnological applications, offering a more precise and effective method than conventional approaches.
0%
Antioxidant Activity Increase
0%
Phenolic Yield Improvement
0%
Cell Proliferation Inhibition
0%
Enzyme Inhibition Efficacy
Deep Analysis & Enterprise Applications
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The study employed both Response Surface Methodology (RSM) and an integrated Artificial Neural Network-Genetic Algorithm (ANN-GA) approach to optimize key extraction parameters: temperature, duration, and ethanol-to-water ratio. The ANN-GA method consistently outperformed RSM, yielding extracts with higher overall biological efficacy and increased phenolic content.
Enterprise Process Flow
Sample Collection & Preparation
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Parameter Definition (Temp, Time, Solvent Ratio)
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Experimental Design (RSM & ANN-GA)
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Soxhlet Extraction
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Extract Analysis (TAS)
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Model Optimization & Validation
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Optimal Extract Production
The phenolic composition of the optimized mushroom extracts was thoroughly examined using LC-MS/MS. The ANN-GA method generally provided higher yields of key phenolic compounds like gallic acid, quercetin, and vanillic acid compared to RSM-optimized extracts, underscoring its superior optimization capability.
10,491.72
mg/kg Gallic Acid (ANN-GA)
| Compound | RSM Extract | ANN-GA Extract |
|---|---|---|
| Gallic Acid | 7270.54 ± 3.07 | 10,491.72 ± 3.85 |
| Quercetin | 8542.81 ± 1.08 | 9960.53 ± 1.48 |
| 4-hydroxybenzoic acid | 1664.01 ± 0.97 | 1753.04 ± 1.70 |
| Syringic acid | 2374.52 ± 2.25 | 2152.67 ± 1.76 |
| Vanillic acid | 525.87 ± 0.71 | 1144.93 ± 2.55 |
Extracts obtained via ANN-GA demonstrated superior antioxidant properties, exhibiting higher total antioxidant status (TAS), stronger ferric reducing power (FRAP), and improved DPPH radical scavenging activity. This indicates a more potent ability to neutralize free radicals and mitigate oxidative stress.
6.092
mmol/L Total Antioxidant Status (ANN-GA)
| Parameter | RSM Extract | ANN-GA Extract |
|---|---|---|
| TAS (mmol/L) | 5.997 ± 0.011 | 6.092 ± 0.012 |
| TOS (µmol/L) | 12.701 ± 0.103 | 13.389 ± 0.134 |
| OSI (TOS/(TAS*10)) | 0.212 ± 0.001 | 0.220 ± 0.002 |
| FRAP (mg Trolox Equi/g) | 95.803 ± 1.363 | 102.033 ± 0.948 |
| DPPH (mg Trolox Equi/g) | 109.140 ± 2.348 | 124.320 ± 1.509 |
The optimized P. ribis extracts showed significant enzyme-inhibitory properties against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with ANN-GA extracts exhibiting lower IC50 values (higher potency) than RSM extracts. This suggests potential utility in neurodegenerative disease management.
47.20
µg/mL AChE IC50 (ANN-GA)
| Enzyme | RSM Extract | ANN-GA Extract |
|---|---|---|
| AChE | 52.57 ± 1.42 | 47.20 ± 0.87 |
| BChE | 72.58 ± 1.13 | 67.05 ± 1.00 |
P. ribis extracts demonstrated a potent dose-dependent inhibition of A549 human lung cancer cell proliferation, with ANN-GA optimized extracts showing a more significant impact. This highlights its potential as a natural source for developing novel therapeutic agents against cancer.
200
µg/mL (Significant Inhibition)
Targeting Lung Cancer with Optimized Extracts
The study evaluated the antiproliferative effects of optimized Phylloporia ribis extracts on the A549 human lung cancer cell line. At concentrations of 100 and 200 µg/mL, a significant dose-dependent decrease in cell viability was observed. The ANN-GA optimized extracts showed a stronger effect compared to RSM. This suggests P. ribis has promising anticancer potential, potentially linked to its enriched phenolic content (e.g., gallic acid and quercetin), which are known to induce apoptosis and inhibit cancer cell proliferation.
Calculate Your Potential ROI
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Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Tailored Implementation Roadmap
Our AI-driven methodology offers a clear path to integrate advanced extraction optimization into your operations. Here’s a simplified roadmap.
Phase 1: AI-Driven Feasibility Study
Assess existing extraction processes, identify target compounds, and conduct initial AI modeling to predict optimal parameters for your specific raw materials.
Phase 2: Optimized Extraction Pilot
Implement ANN-GA optimized parameters in a pilot scale, verifying enhanced yield and biological activity compared to baseline methods.
Phase 3: Full-Scale Integration & Monitoring
Scale up the optimized process, continuously monitor performance with AI-driven analytics, and fine-tune parameters for sustained efficiency and output quality.
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