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

本文介绍了基于介电特性和微波技术测量体积密度的未清洗花生中异物含量的测定。在 10 GHz 下使用了微波自由空间传输技术。开发了两种测量系统，用于测量放置在聚碳酸酯样品架（12.1 cm × 21 cm × 20.5 cm）中的清洁去壳花生（九个花生荚）和未清洁去壳花生的介电特性，并将其集成在一个测量单元中。九个花生荚系统提供了清洗后的带壳花生水分含量，该水分含量用于异物含量测定的算法中。未清洗去壳花生样品的介电性能和体积密度测量与异物含量有关。这些参数，即未清洗花生的体积密度和介电特性以及清洗后的未剥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。与使用线性回归算法获得的性能标准误差为 2.39% 的结果相比，使用人工神经网络算法获得的结果显示了对异物含量的最佳估计，性能标准误差为 1.36%。