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首页> 外文会议>International Workshop on Intelligent Systems and Applications >Application of an integration of uninformative variable elimination and least-squares support vector machine for discriminating soy milk powder based on visual near-infrared spectral calibration - Subtitle: Discrimination of Milk Powder
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Application of an integration of uninformative variable elimination and least-squares support vector machine for discriminating soy milk powder based on visual near-infrared spectral calibration - Subtitle: Discrimination of Milk Powder

机译：基于可视近红外光谱校准的无信息可变消除和最小二乘和最小二乘和最小二乘互相乳粉的应用的应用 - 乳粉辨别

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摘要

A novel method which is combination of uninformative variable elimination by partial least squares (UVE) and least-square support vector machine (LS-SVM) was proposed to discriminate soy milk powder. A total of 240 (60 for each variety) samples were characterized on the basis of visual and infrared spectroscopy (VIS-NIR), 160 (40 for each variety) samples were selected randomly for the calibration set, whereas, the remaining 80 samples (20 for each variety) for prediction set. UVE was executed to obtain the stability of each input variables. 327 wavelengths were selected by UVE, and inputted into LS-SVM to build recognition model. The classification rate reached 100%, and the performance was better than common LV-SVM model which was established using the whole spectral wavelengths. The overall results indicated that the proposed method of UVE-LS-SVM based VIS-NIR technology was a powerful way for discrimination of different varieties of soy milk powder.
机译：提出了一种新的方法，它采用部分最小二乘（UVE）和最小二乘支持向量机（LS-SVM）的未表现变量消除组合以辨别大豆奶粉。在视觉和红外光谱（Vis-Nir）的基础上表征了总共240（每种品种）样品，随机选择160（每种各种品种）样品进行校准集，而剩余的80个样本（ 20种多种）用于预测集。执行uve以获得每个输入变量的稳定性。通过UVE选择327个波长，并输入到LS-SVM以构建识别模型。分类率达到100％，并且性能优于使用整个光谱波长建立的常见LV-SVM模型。总体结果表明，基于UVE-LS-SVM的VIR-NIR技术的提出方法是鉴别不同品种的大豆奶粉的有力方法。

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