Papers by Author: Seong Min Kim

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Authors: Seong Min Kim, Michael J. McCarthy
Abstract: This study was performed to show the feasibility of nuclear magnetic resonance (NMR) techniques for quality evaluation of various agricultural and food products. A real-time in-line NMR quality evaluation sensor was designed, constructed and tested. The device consists of an NMR spectrometer coupled to a conveyor system and a data acquisition system. The conveyor was run at speeds ranging from 0 to 300 mm/s. An NMR signal can be detected when a sample is within ±50 mm of the NMR coil center. The response of NMR sensor was tested using several fruits. The results showed a feasibility of an NMR sensor for evaluating internal quality of various fruits.
Authors: Jeong Woo Lee, Dong Wha Shin, Seong Min Kim
Abstract: The purpose of this study is to develop a portable electronic nose system to measure volatile components of agricultural and food products. Also, operating software to control the electronic nose system through the Internet was developed. Various experiments to find optimum operating conditions of the system were performed. An array of commercial metal oxide gas sensors was used to detect various gas components. For kochujang experiment, flavour signal patterns were different to the type of kochujang. Transient and steady state signals were analyzed. Transient signal analysis was more useful in PCA. Cluster analysis showed the possibility of reduction of the number of sensors. It is feasible to optimise the kochujang making process.
Authors: Seong Min Kim, Chul Soo Kim, Chong Ho Lee, Myung Ho Kim, Seung Jae Park
Abstract: A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.
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