Computational tool for optimizing the essential oils utilization in inhibiting the bacterial growth

Area of Science:

• Natural Products Chemistry
• Computational Biology
• Microbiology

Background:

• Essential oils (EOs) are increasingly vital natural products in medicine, pharmaceuticals, and food industries.
• EOs exhibit antimicrobial, antioxidant, and anti-inflammatory properties, but their complex composition poses challenges for optimal application.
• Determining the most effective EO for specific bacterial inhibition is a significant research hurdle.

Purpose of the Study:

• To introduce a novel computational tool for optimizing essential oil (EO) applications.
• To address the challenge of EO complexity and bacterial inhibition efficacy determination.
• To facilitate the selection of potent EOs for various applications.

Main Methods:

• The study employed a computational approach combining rough set theory for data reduction.
• Particle swarm optimization was utilized for optimizing the selection of effective EOs.
• The developed Essential Oil Reduction and Optimization Tool (EOROT) was applied to analyze EO data.

Main Results:

• The Essential Oil Reduction and Optimization Tool (EOROT) was successfully developed and applied.
• The tool was tested on 24 distinct essential oils.
• The efficacy of these EOs against 17 different bacterial species was evaluated.

Conclusions:

• The developed computational tool offers a systematic method for optimizing EO usage.
• This approach aids in identifying EOs with significant antibacterial potential.
• The study provides a foundation for enhanced utilization of natural products in various industries.