Hybrid modeling of ultrasound-assisted extraction of phytochemicals from Himalayan Ganoderma lucidum using artificial neural network-genetic algorithm.
8 affiliations
- 1School of Bioengineering and Food Technology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
- 2Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721303, India.
- 3ICFRE-Himalayan Forest Research Institute, Shimla 171013, India.
- 4School of Biological and Environmental Science, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
- 5Faculty of Materials Science and Technology, VSB-Technical University of Ostrava, Ostrava-Poruba 708 00, Czech Republic.
- 6Magan Centre for Applied Mycology, Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom.
- 7Centre for Sustainable Rural Development, Indian Institute of Technology Roorkee, Roorkee 247667, India.
- 8Department of Engineering Technology, Cullen College of Engineering, University of Houston, Sugarland, TX 77479, USA.
Abstract
Objective
Ganoderma lucidum is a bioactive-rich traditional Chinese medicine widely recognized for its nutraceutical and pharmaceutical applications. This study optimized the ultrasound-assisted extraction (UAE) from Himalayan G. lucidum, applying artificial-neural network (ANN) modelling coupled with genetic algorithm (GA) optimization to enhance efficiency.Methods
The UAE extraction was performed under various process parameters, including treatment duration (i ₁, 5-15 min), ultrasonic amplitude (i ₂, 30%-80%), and solvent concentration (i ₃, 40%-80%), to optimize the yield (w/w%, O ₁), total triterpene content [mg/g, dry weight (dw), O ₂], 2,2-diphenyl-1-picrylhydrazyl scavenging activity (%, O ₃) and total phenolic content (mg/g dw, O ₄).Results
The ANN-GA approach accurately modeled the complex non-linear relationships between input and output parameters, identifying optimal conditions as 5 min (i ₁), 45.63% (i ₂) and 80% (i ₃). Experimental validations under these conditions resulted in (7.63 ± 0.2) %, (34.11 ± 0.01) mg/g dw, (41.55 ± 0.01) % and (10.98 ± 0.01) mg/g dw for O ₁, O ₂, O ₃, and O ₄ respectively. Compared to response surface methodology (RSM) approach, which served only as a benchmark, the ANN-GA approach demonstrated superior predictive accuracy and robustness in optimization.Conclusion
The ANN-GA approach provides an effective and robust strategy for optimizing herbal extraction processes. This study demonstrates the applicability of ANN-GA as an advanced optimization tool for herbal extraction processes and highlights G. lucidum as a valuable source of natural antioxidants and triterpenes with potential therapeutic relevance.
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BibTeX
@article{khanal2026hybrid,
title = {{Hybrid modeling of ultrasound-assisted extraction of phytochemicals from Himalayan Ganoderma lucidum using artificial neural network-genetic algorithm.}},
author = {Sonali Khanal and T Jayasree Joshi and Pratibha Thakur and Rachna Verma and Ashwani Tapwal and Dinesh Kumar and Vinod Kumar},
journal = {Chinese herbal medicines},
year = {2026},
month = aug,
doi = {10.1016/j.chmed.2026.08.018},
url = {https://europepmc.org/article/MED/42948392},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Hybrid modeling of ultrasound-assisted extraction of phytochemicals from Himalayan Ganoderma lucidum using artificial neural network-genetic algorithm.
AU - Sonali Khanal
AU - T Jayasree Joshi
AU - Pratibha Thakur
AU - Rachna Verma
AU - Ashwani Tapwal
AU - Dinesh Kumar
AU - Vinod Kumar
PY - 2026
DA - 2026/08/29
JO - Chinese herbal medicines
DO - 10.1016/j.chmed.2026.08.018
UR - https://europepmc.org/article/MED/42948392
AB - Objective Ganoderma lucidum is a bioactive-rich traditional Chinese medicine widely recognized for its nutraceutical and pharmaceutical applications. This study optimized the ultrasound-assisted extraction (UAE) from Himalayan G. lucidum, applying artificial-neural network (ANN) modelling coupled with genetic algorithm (GA) optimization to enhance efficiency.Methods The UAE extraction was performed under various process parameters, including treatment duration (i ₁, 5-15 min), ultrasonic amplitude (i ₂, 30%-80%), and solvent concentration (i ₃, 40%-80%), to optimize the yield (w/w%, O ₁), total triterpene content [mg/g, dry weight (dw), O ₂], 2,2-diphenyl-1-picrylhydrazyl scavenging activity (%, O ₃) and total phenolic content (mg/g dw, O ₄).Results The ANN-GA approach accurately modeled the complex non-linear relationships between input and output parameters, identifying optimal conditions as 5 min (i ₁), 45.63% (i ₂) and 80% (i ₃). Experimental validations under these conditions resulted in (7.63 ± 0.2) %, (34.11 ± 0.01) mg/g dw, (41.55 ± 0.01) % and (10.98 ± 0.01) mg/g dw for O ₁, O ₂, O ₃, and O ₄ respectively. Compared to response surface methodology (RSM) approach, which served only as a benchmark, the ANN-GA approach demonstrated superior predictive accuracy and robustness in optimization.Conclusion The ANN-GA approach provides an effective and robust strategy for optimizing herbal extraction processes. This study demonstrates the applicability of ANN-GA as an advanced optimization tool for herbal extraction processes and highlights G. lucidum as a valuable source of natural antioxidants and triterpenes with potential therapeutic relevance.
SN - 1674-6384
SN - 2589-3610
ER -