Optimization studies on batch extraction of phenolic compounds from Azadirachta indica using genetic algorithm and...

Area of Science:

• Natural Product Chemistry
• Phytochemistry
• Biotechnology

Background:

• Phenolic compounds are vital secondary metabolites with significant biological activity and medicinal applications.
• These compounds are widely distributed in plant species, including Azadirachta indica.
• Efficient extraction methods are crucial for isolating these valuable compounds.

Purpose of the Study:

• To optimize the solid-liquid batch extraction of total phenolic compounds (TPC) from Azadirachta indica leaves.
• To investigate the influence of key process parameters on TPC yield.
• To apply machine learning techniques for predicting TPC and optimizing extraction parameters.

Main Methods:

• Utilized a Taguchi L16 experimental design with factors: extraction time, temperature, particle size, and solid-to-solvent ratio.
• Employed machine learning algorithms, including Support Vector Regression (SVR) and Random Forest Method (RFM), for TPC prediction.
• Applied a Genetic Algorithm (GA) to determine the optimal extraction conditions for maximum TPC yield.

Main Results:

• Particle size was identified as the most influential factor, with an inverse relationship to TPC yield.
• Extraction temperature, time, and solid-to-solvent ratio showed a direct impact on TPC yield.
• Optimized parameters predicted a maximum TPC of 23.039 mg GAE/g using particle size of 0.15 mm, 40 min extraction time, 1:25 g/mL solid-to-solvent ratio, and 55°C temperature.
• Support Vector Regression (SVR) demonstrated higher accuracy in predicting TPC yield compared to the Random Forest Method (RFM).

Conclusions:

• The study successfully optimized phenolic compound extraction from Azadirachta indica leaves.
• Machine learning, particularly SVR and GA, proved effective in predicting TPC and optimizing extraction parameters.
• The findings provide a foundation for efficient industrial-scale extraction of valuable phenolic compounds from medicinal plants.