Local vs global methods applied to large near infrared databases covering high variability

O. Minet, V. Baeten, B. Lecler, P. Dardenne, J. Pierna
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引用次数: 3

Abstract

Author Summary: The purpose of this study was to evaluate two different locally based regression methods (LOCAL and Local Calibration by Customized Radii Selection) and compare their performance to the classical global PLS for large NIR data. The data used in this study came from two inter-laboratory studies for wheat grain analysis organized in 2016 in the framework of the REQUASUD network. The results showed that improved predictions in terms of prediction errors can be obtained using local approaches compared to the classical global PLS. Moreover, the study highlighted clear differences between inter-laboratory studies and participating laboratories, which were even more evident when working with local procedures.
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局部与全局方法应用于覆盖高变异性的大型近红外数据库
作者摘要:本研究的目的是评估两种不同的基于局部的回归方法(LOCAL和LOCAL Calibration by Customized Radii Selection),并将它们与经典的全局PLS在大近红外数据中的表现进行比较。本研究中使用的数据来自2016年在REQUASUD网络框架下组织的两项小麦籽粒分析实验室间研究。结果表明,与经典的全局PLS相比,使用局部方法可以获得预测误差方面的改进预测。此外,该研究强调了实验室间研究和参与实验室之间的明显差异,这在使用局部程序时更为明显。
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