校准
预处理器
计算机科学
多元统计
光学(聚焦)
标准化
数据挖掘
人工智能
机器学习
统计
数学
操作系统
光学
物理
作者
Robert N. Feudale,Nathaniel A. Woody,Huwei Tan,Anthony J. Myles,Steven D. Brown,Joan Ferré
标识
DOI:10.1016/s0169-7439(02)00085-0
摘要
Multivariate calibration models are of critical importance to many analytical measurements, particularly for spectroscopic data. Generally, considerable effort is placed into constructing a robust model since it is meant to be used for extended periods of time. A problem arises, though, when the samples to be predicted are measured on a different instrument or under differing environmental factors from those used to build the model. The changes in spectral variations between the two conditions may make the model invalid for prediction in the new system. Various standardization and preprocessing methods have been developed to enable a calibration model to be effectively transferred between two systems, thus eliminating the need for a full recalibration. This paper presents an overview of the different methods used for calibration transfer and a critical assessment of their validity and applicability. The focus is on methods for transfer of near-infrared (NIR) spectra.
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