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首页> 外文期刊>International journal of food science & technology >Unsound kernel identification using linear colour charge-coupled device
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Unsound kernel identification using linear colour charge-coupled device

机译：使用线性彩色电荷耦合器件进行不可靠的内核识别

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摘要

Wheat is one of the most consumed grains in the world. The identification of wheat based on surface characteristics is important for the market. This study is aimed at identifying unsound kernels (Triticum durum Desf), including 710 black germ kernels, 627 broken kernels and 1169 sound kernels from several seed distributors in China. The system is mainly composed of a liner charge-coupled device for image capture and a software package for extracting various morphological, colour and texture features. The models built by partial least squares discriminate analysis, support vector machine discrimination analysis (SVMDA) and principal component analysis-artificial neural networks for identifying the unsound kernels have been explored. After comparisons of these three methods, it has been found that SVMDA got the best accuracy: 95.1%, 96.0% and 98.3% (black germ kernels, broken kernels and sound kernels). Obviously, the experimental results have shown that SVMDA is the most feasible and effective choice for the identification.
机译：小麦是世界上最消耗的谷物之一。根据表面特征鉴定小麦对市场很重要。本研究旨在鉴定来自中国多家种子分销商的不合格粒（Triticum durum Desf），包括710个黑胚粒，627个破碎粒和1169个好粒。该系统主要由用于图像捕获的线性电荷耦合设备和用于提取各种形态，颜色和纹理特征的软件包组成。探索了由偏最小二乘判别分析，支持向量机判别分析（SVMDA）和主成分分析-人工神经网络建立的模型，用于识别不健全的内核。通过对这三种方法的比较，发现SVMDA的准确度最高：95.1％，96.0％和98.3％（黑种仁，破碎的果仁和健全的果仁）。显然，实验结果表明，SVMDA是最可行，最有效的识别方法。

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