ECOC-SVM Classification of Coffee Roast Levels based on MNDT s-Parameters

Azlan Salim, I. Yassin, M. K. A. Mahmood, Z. I. Khan, M. Ali, Khairul Khaizi Mohd Shariff, F. N. Osman, Adizul Ahmad, F. Eskandari
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引用次数: 1

Abstract

This research describes an intelligent method for differentiating coffee roasting levels based on Microwave Non- Destructive Testing (MNDT) data. The MNDT method collects s-parameter readings from several types of coffee (dark, medium, and light roast) by passing microwaves through them. Error-Correcting Output Coding Support Vector Machine (ECOC-SVM) was fed a multi-layer perceptron neural network to assess the degree of different coffee roasts. With a small number of hidden units, the ECOC-SVM could identify between the various roasts (with 6,400 data points per sample).
基于MNDT s-参数的咖啡烘焙程度的ECOC-SVM分类
研究了一种基于微波无损检测(MNDT)数据的咖啡烘焙程度智能判别方法。MNDT方法通过微波从几种咖啡(深、中、浅烘焙)中收集s参数读数。将纠错输出编码支持向量机(ECOC-SVM)馈送到多层感知器神经网络中,以评估不同咖啡烘焙的程度。使用少量隐藏单元,ECOC-SVM可以识别不同的烘焙(每个样本有6,400个数据点)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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