@article {10.3844/ojbsci.2026.1.13,
article_type = {journal},
title = {Extraction Performance of Essential Oils by Hydrodistillation Using Mathematical Models: A Systematic Review },
author = {Huatangari, Lenin Quiñones and Rosillo, Frank Fernandez and Cabrejos Barrios, Eliana Milagros and Quinde Flores, Erika Guisella and Cueva Ríos, María Alina and Aguirre Vargas, Elza Berta},
volume = {26},
number = {1},
year = {2026},
month = {Mar},
pages = {1-13},
doi = {10.3844/ojbsci.2026.1.13},
url = {https://thescipub.com/abstract/ojbsci.2026.1.13},
abstract = {Essential oils are complex mixtures of volatile organic compounds valued for their antimicrobial and antioxidant properties, with widespread applications in the food, pharmaceutical, and cosmetic industries. Among the various extraction techniques employed, hydrodistillation remains one of the most widely used methods. The extraction yield of essential oils by hydrodistillation is influenced by multiple factors, and quantifying these through mathematical modelling is critical for assessing the efficiency, efficacy, and economic viability of the process. This systematic review aimed to identify, evaluate, and synthesize scientific studies that applied mathematical models to calculate the hydrodistillation extraction yield of essential oils. A systematic search was conducted across four databases - Scopus, ScienceDirect, SciELO, and EBSCO - covering literature published up to April 2023. Studies were screened and selected based on pre-defined eligibility criteria in accordance with PRISMA guidelines. Seventeen studies met the inclusion criteria, reporting the hydrodistillation of essential oils from nineteen plant species using various plant parts including leaves, flowers, fruits, and roots. Of these, ten studies applied response surface methodology, four used kinetic modelling, and three employed data mining algorithms. Variables influencing extraction yield were classified into agro-climatic, conditioning, and exploitation factors. Response surface models with varied design types and kinetic models were the most frequently applied approaches; however, an emerging trend toward data mining algorithms was observed. These findings provide a structured overview of current modelling approaches for essential oil extraction and highlight the growing potential of machine learning-based methods as promising tools for optimizing hydrodistillation processes.},
journal = {OnLine Journal of Biological Sciences},
publisher = {Science Publications}
}