{"title":"The effect of drying of Piper hispidinervium by different methods and its influence on the yield of essential oil and safrole","authors":"Helder Kiyoshi Miyagawa, Alberdan Silva Santos","doi":"10.1016/j.inpa.2021.10.003","DOIUrl":null,"url":null,"abstract":"<div><p>The <em>Piper hispidinervium</em> 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 (R<sup>2</sup>), root mean square error (RMSE) and χ<sup>2</sup>. Results suggest that the best model was the logarithmic model (R<sup>2</sup> > 0.99, RMSE < 0.000 5, and χ<sup>2</sup> < 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.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"10 1","pages":"Pages 28-39"},"PeriodicalIF":7.7000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317321000810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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