{"title":"Determination of foreign-material content in uncleaned peanuts by microwave measurements and machine learning techniques","authors":"S. Julrat, S. Trabelsi","doi":"10.1080/08327823.2021.1993047","DOIUrl":null,"url":null,"abstract":"Abstract Foreign-material content determination in uncleaned peanuts based on dielectric properties and bulk density measurements by microwave techniques is presented in this paper. A microwave free-space transmission technique was used at 10 GHz. Two measurement systems for measuring the dielectric properties of cleaned unshelled peanuts (nine-peanut pods) and uncleaned unshelled peanuts placed in polycarbonate sample holder (12.1 cm × 21 cm × 20.5 cm) were developed and integrated in one single measuring unit. The nine-peanut-pods system provided the cleaned unshelled peanuts moisture content which was used in the algorithms for foreign material content determination. The dielectric properties and bulk density measurements of the uncleaned unshelled peanut sample were related to the foreign-material content. These parameters, namely bulk density and dielectric properties of uncleaned peanuts and cleaned unshelled moisture content were supplied to machine learning algorithms, linear regression technique and artificial neural network algorithms. Results obtained with the artificial neural network algorithm showed the best estimate of foreign material content with a standard error of performance of 1.36% compared to that obtained with the linear regression algorithm with a standard of performance of 2.39%.","PeriodicalId":16556,"journal":{"name":"Journal of Microwave Power and Electromagnetic Energy","volume":"143 1","pages":"3 - 17"},"PeriodicalIF":0.9000,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Microwave Power and Electromagnetic Energy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/08327823.2021.1993047","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Foreign-material content determination in uncleaned peanuts based on dielectric properties and bulk density measurements by microwave techniques is presented in this paper. A microwave free-space transmission technique was used at 10 GHz. Two measurement systems for measuring the dielectric properties of cleaned unshelled peanuts (nine-peanut pods) and uncleaned unshelled peanuts placed in polycarbonate sample holder (12.1 cm × 21 cm × 20.5 cm) were developed and integrated in one single measuring unit. The nine-peanut-pods system provided the cleaned unshelled peanuts moisture content which was used in the algorithms for foreign material content determination. The dielectric properties and bulk density measurements of the uncleaned unshelled peanut sample were related to the foreign-material content. These parameters, namely bulk density and dielectric properties of uncleaned peanuts and cleaned unshelled moisture content were supplied to machine learning algorithms, linear regression technique and artificial neural network algorithms. Results obtained with the artificial neural network algorithm showed the best estimate of foreign material content with a standard error of performance of 1.36% compared to that obtained with the linear regression algorithm with a standard of performance of 2.39%.
摘要本文介绍了利用微波介电特性和容重测量技术测定花生中杂质含量的方法。在10ghz频段采用了微波自由空间传输技术。开发了两种测量系统,分别用于测量清洗后的脱壳花生(9粒花生荚)和置于聚碳酸酯样品架(12.1 cm × 21 cm × 20.5 cm)中的未清洗脱壳花生的介电特性,并将其集成在一个测量单元中。九个花生豆荚系统提供了清洗后的去壳花生水分含量,用于异物含量测定算法。未清洗花生脱壳样品的介电性能和容重测量与杂质含量有关。这些参数,即未清洗花生的体积密度和介电性能以及清洗后的去壳水分含量,提供给机器学习算法、线性回归技术和人工神经网络算法。人工神经网络算法对外来物质含量的最佳估计性能标准误差为1.36%,而线性回归算法的性能标准误差为2.39%。
期刊介绍:
The Journal of the Microwave Power Energy (JMPEE) is a quarterly publication of the International Microwave Power Institute (IMPI), aimed to be one of the primary sources of the most reliable information in the arts and sciences of microwave and RF technology. JMPEE provides space to engineers and researchers for presenting papers about non-communication applications of microwave and RF, mostly industrial, scientific, medical and instrumentation. Topics include, but are not limited to: applications in materials science and nanotechnology, characterization of biological tissues, food industry applications, green chemistry, health and therapeutic applications, microwave chemistry, microwave processing of materials, soil remediation, and waste processing.