Use of machine learning methods in predicting the main components of essential oils: Laurus nobilis L.

Abstract

Laurus nobilis L. is an export plant that contributes to the industry with its medicinal and aromatic properties, as well as the use of its leaves or fruits in the food, pharmaceutical, and cosmetic industries. This study aims to predict the first three main components of bay laurel essential oil with different machine learning methods as a result of GC-MS analysis, taking into account a wide range of parameters used before and after the analysis of the essential oil in pharmacognostic studies. In this study, the K-Means algorithm was used to detect unknown relationships (chemical or physical properties) between the data obtained as a result of the analysis, and Artificial Neural Network (ANN) and Decision Tree machine learning methods were used to perform prediction operations. Based on this, considering the R2 values obtained from ANN analyses, R2 values of 0.9992 for testing, 1 for training, 1 for validation value, and 0.99992 for all were obtained. The ANN model produced results close to the real values with an accuracy rate of 98.97% in predicting the three main components. The Decision Tree algorithm achieved classification with a 96.23% accuracy rate in predicting the three main components. K-Means clustering was performed with an accuracy rate of 97.83% and significant relationships between the features of the Laurus nobilis dataset were detected. This comprehensive study serves as a guide for machine learning research on other essential oils.