ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

An approach for extracting essential oils from Cinnamomum camphora leaves: genetic algorithms and artificial neural network models for improving yield and extraction mechanism

Hongxiang Yang, Xiangzhou Li, and Liqun Shen

College of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha, China

E-mail: rlxz@163.com

Received: 9 March 2024  Accepted: 1 June 2024

Abstract:

This research proposed a green and efficient enzyme-assisted steam distillation (EASD) method for extracting essential oils from Cinnamomum camphora leaves, and a novel neural network model was constructed to precisely optimize the extraction process parameters. The results demonstrated that the artificial neural network and genetic algorithm (GA-ANN) model exhibited higher accuracy and reliability than traditional models. The optimized conditions were as follows: distillation time 4.0 h, liquid–solid ratio 13.469 mL/g, incubation time 1.511 h, and cellulase dosage 0.555 g. The actual yield of the essential oils reached 1.365%, which was in accordance with the predicted yield of 1.373%. The yield was significantly higher than that obtained by steam distillation and predicted by other optimization methods. Moreover, the extraction kinetics analysis showed that first-order kinetics was a more suitable description of essential oils extraction. The extraction mechanism was clearly explained through the analysis of the extraction kinetics and microstructure. The enzyme exhibited a strong destructive effect on the plant cell wall, reducing mass transfer resistance and facilitating the dissolution of essential oils. Compared to steam distillation (SD) method, the EASD method notably diminished distillation time and energy consumption, while maintaining antioxidant activity.

Graphical Abstract

Keywords: Cinnamomum camphora leaves; Essential oils; Artificial neural network; Extraction kinetics

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-024-03547-7

Chemical Papers 78 (11) 6457–6469 (2024)