Artificial Intelligence and Extraction of Bioactive Compounds: The Case of Rosemary and Pressurized Liquid Extraction
Martha Mantiniotou, Vassilis Athanasiadis, Κωνσταντίνος Λιάκος, Eleni Bozinou, Stavros I. Lalas
Abstract
Rosemary (Rosmarinus officinalis or Salvia rosmarinus) is an aromatic herb that possesses numerous health-promoting and antioxidant properties. Pressurized Liquid Extraction (PLE) is an efficient, environmentally friendly technique for obtaining valuable compounds from natural sources. The optimal PLE conditions were established as 25% v/v ethanol at 160 °C for 25 min, and a liquid-to-solid ratio of 10 mL/g. The optimal extract exhibited high polyphenol and antioxidant content through various assays. The recovered bioactive compounds possess potential applications in the food, pharmaceutical, and cosmetics sectors, in addition to serving as feed additives. This research compares two distinct optimization models: one statistical, derived from experimental data, and the other based on artificial intelligence (AI). The objective was to evaluate if AI could replicate experimental models and ultimately supplant the laborious experimental process, yielding the same results more rapidly and adaptably. To further enhance data interpretation and predictive capabilities, six machine learning models were implemented on the original dataset. Due to the limited sample size, synthetic data were generated using Random Forest (RF)-based resampling and Gaussian noise addition. The augmented dataset significantly improved the model performance. Among the models tested, the RF algorithm achieved the highest accuracy.