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Hybrid modeling of ultrasound-assisted extraction of phytochemicals from Himalayan Ganoderma lucidum using artificial
Sonali Khanal1, T Jayasree Joshi2, Pratibha Thakur3
1School of Bioengineering and Food Technology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
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.