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

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成果类型:
期刊论文
作者:
Yang, Hongxiang;Li, Xiangzhou;Shen, Liqun
通讯作者:
Li, XZ
作者机构:
[Yang, Hongxiang; Li, Xiangzhou; Li, XZ] Cent South Univ Forestry & Technol, Coll Mat Sci & Engn, Changsha 410004, Hunan, Peoples R China.
[Li, Xiangzhou; Li, XZ] Cent South Univ Forestry & Technol, Inst Nat Prod Res & Dev, Changsha 410004, Hunan, Peoples R China.
[Shen, Liqun] Guangxi Minzu Univ, Guangxi Collaborat Innovat Ctr Chem & Engn Forest, Key Lab Chem & Engn Forest Prod, State Ethn Affairs Commiss,Guangxi Key Lab Chem &, Nanning 530006, Guangxi, Peoples R China.
通讯机构:
[Li, XZ ] C
Cent South Univ Forestry & Technol, Coll Mat Sci & Engn, Changsha 410004, Hunan, Peoples R China.
Cent South Univ Forestry & Technol, Inst Nat Prod Res & Dev, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Cinnamomum camphora leaves;Essential oils;Artificial neural network;Extraction kinetics
期刊:
Chemical Papers
ISSN:
2585-7290
年:
2024
卷:
78
期:
11
页码:
6457-6469
基金类别:
the National Key Research and Development Program of China [2022YFD2200605]; National Key Research and Development Program of China [GXFK2302]; China Guangxi Key Laboratory of Chemistry and Engineering of Forest Products
机构署名:
本校为第一且通讯机构
院系归属:
材料科学与工程学院
摘要:
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...

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