不同干燥方法对花椒精油和黄樟油得率的影响

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Helder Kiyoshi Miyagawa, Alberdan Silva Santos
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引用次数: 0

摘要

在实验室和田间条件下对花椒叶片和细茎进行干燥。在实验室中,使用带和不带强制对流的遮阳干燥机和30°C和40°C的烘箱干燥机进行干燥。采用透明、黑色塑料、棕榈秸秆三种不同覆盖物的太阳能干燥机进行田间试验。采用水蒸气蒸馏法提取挥发油,气相色谱法分析黄樟油的含量。采用Page、对数、Henderson and Pabis、分数、扩散5种数学模型对实验数据进行拟合,并根据决定系数(R2)、均方根误差(RMSE)和χ2进行比较。结果表明,最佳模型为对数模型(R2 >0.99, RMSE <0.000 5, χ2 <0.005)。充分干燥后,黄樟油含量可达提取油的95%;但随着干燥时间的延长,提取油的出油率和黄樟酚含量均有所下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The effect of drying of Piper hispidinervium by different methods and its influence on the yield of essential oil and safrole

The Piper hispidinervium leaves and thin stems were dried under laboratory and field conditions. Laboratory drying was performed using a shade dryer operating with and without forced convection and an oven dryer operating at 30 and 40 °C. Field experiments were conducted using solar dryers with three different covers, i.e., transparent, black plastic, and palm straw covers. The essential oil extraction was performed by steam distillation, and the safrole content was analyzed by gas chromatography. Five mathematical models (Page, logarithmic, Henderson and Pabis, fractional, and diffusion) were fitted with the experimental data and compared based on the coefficient of determination (R2), root mean square error (RMSE) and χ2. Results suggest that the best model was the logarithmic model (R2 > 0.99, RMSE < 0.000 5, and χ2 < 0.005). With sufficient drying, the safrole content increased up to 95% of the extracted oil; however, when the drying time was prolonged, both the oil yield and safrole content of the extracted oil decreased.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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