摘要
为探究水蒸气蒸馏法和超声有机溶剂法提取草果精油的最佳提取工艺,本文在单因素试验的基础上,通过响应面法(response surface methodology, RSM)与反向传播人工神经网络法(back-propagation artificial neural network, BP-ANN)优化了水蒸气蒸馏法与超声有机溶剂法提取草果(Amomum tsao-ko)中精油类成分的工艺,并利用气相色谱-质谱联用(gas chromatography-mass spectrometry, GC-MS)分析两种提取法获得的精油成分,并进行模型对比。结果表明,水蒸气蒸馏法的最佳工艺参数为浸泡时间53.00 min、蒸馏时间72.00 min、液固比11.00 mL/g,在此条件下草果精油的提取率为1.59%,共鉴定出34种成分;超声有机溶剂法的最佳工艺条件为超声功率150.00 W、超声时间27.00 min、液固比6.30 mL/g,在此条件下草果精油的提取率为3.70%,共鉴定出43种成分。尽管使用超声有机溶剂法获得草果精油的提取率高于水蒸气蒸馏法,但得到的精油品质差、颜色深、杂质多,不利于后续的应用。模型对比结果表明,BP-ANN模型比RSM模型在预测提取率方面误差指数更低,拟合度更高,具有更高的精度和预测优势。本研究将为草果精油的高效提取提供可靠的工艺参数,也进一步促进药食同源植物草果的开发利用。
In order to study the optimal extraction technology of essential oil from Amomum tsao-ko by steam distillation and ultrasonic organic solvent extraction.In this paper,based on single factor experiment,the extraction process of A.tsao-ko essential oil by steam distillation and ultrasonic organic solvent extraction was optimized by response surface methodology(RSM)and back-propagation artificial neural network(BP-ANN).Gas chromatography-mass spectrometry(GC-MS)was applied to analyze the essential oil components obtained from the two extraction methods,and the models were compared.The results showed that the optimum technological parameters of A.tsao-ko essential oil extraction by steam distillation were soaking time of 53.00 min,distillation time of 72.00 min,liquid to solid ratio of 11.00 mL/g,and extraction yield of 1.59%.A total of thirty-four components were identified in A.tsao-ko essential oil obtained by steam distillation.Under the optimum conditions of ultrasonic organic solvent extraction with ultrasonic power of 150.00 W,ultrasonic time of 27.00 min and liquid to solid ratio of 6.30 mL/g,the extraction yield was 3.40%and a total of 43 components were identified.Although the extraction yield of A.tsao-ko essential oil by ultrasonic organic solvent extraction was higher than that by steam distillation,the essential oil obtained by ultrasonic organic solvent extraction was poor in quality,dark in color and heavy in impurities,which was not conducive to the subsequent application.Compared with the RSM model,the BP-ANN model had a lower error-index and a higher fitting degree in predicting extraction yield,and had higher accuracy and prediction advantages.This study will provide reliable technological parameters for the efficient extraction of A.tsao-ko essential oil and further promote the development and utilization of the medicinal and edible plant A.tsao-ko.
作者
刘巨钊
鲜梦雪
孔伟华
田晓黎
袁强
崔琦
LIU Ju-zhao;XIAN Meng-xue;KONG Wei-hua;TIAN Xiao-li;YUAN Qiang;CUI Qi(Zhejiang Chinese Medical University,Hangzhou 310053,China)
出处
《天然产物研究与开发》
CAS
CSCD
2023年第5期766-780,共15页
Natural Product Research and Development
基金
国家自然科学基金(82204552)
中国博士后科学基金(2021M702927)
浙江省自然科学基金(LQ22H280007)
浙江省中医药科技计划(2023ZR079)。
关键词
草果
精油
GC-MS
提取工艺
响应面
人工神经网络
Amomum tsao-ko
essential oil
GC-MS
extraction process
response surface methodology
artificial neural network
