New Biological and Chemical Insights into Optimization of Chamomile Extracts by Using Artificial Neural Network (ANN)...

Area of Science:

• Pharmacognosy
• Phytochemistry
• Biotechnology

Background:

• Chamomile is a globally consumed medicinal plant with widespread traditional and modern pharmaceutical applications.
• Efficient extraction of desired components from chamomile requires optimization of key process parameters.
• Artificial Neural Networks (ANN) offer a powerful modeling approach for process optimization.

Purpose of the Study:

• To optimize chamomile extraction parameters for maximizing total phenolic compounds (TPC) yield.
• To utilize an Artificial Neural Network (ANN) model for predicting optimal extraction conditions.
• To evaluate the biological activity and potential applications of the optimized chamomile extract.

Main Methods:

• Process parameter optimization using an Artificial Neural Network (ANN) model.
• Inputs: solid-to-solvent ratio, microwave power, and extraction time.
• Outputs: yield of total phenolic compounds (TPC).

Main Results:

• Optimized extraction conditions determined: 1:80 solid-to-solvent ratio, 400 W microwave power, 30 min extraction time.
• ANN model accurately predicted TPC yield, which was experimentally validated.
• The optimized chamomile extract exhibited a rich composition and significant biological activity.

Conclusions:

• The study successfully optimized chamomile extraction using ANN modeling, achieving high TPC yield.
• The optimized extract demonstrates potential as a growth medium for probiotics.
• This research highlights the value of statistical modeling in improving plant extraction techniques.