摘要
多元校正模型在近红外光谱数据分析中具有非常重要的作用。然而,应用过程中仪器或环境条件的改变均可能造成样品光谱的变化,进而使得模型在新的系统条件下不适用。因此,多元校正模型需要进行维护或传递以保证其有效性。对光谱进行标准化处理及采用数据预处理方法可以校正或减少光谱信号的变化,使得模型能够有效地在不同系统间传递,以避免重新建模的繁琐。本文对近红外校正模型的各种传递方法及其有效性和适用性进行了综述。
Multivariate calibration models are of critical importance to many analytical measurements, particularly for near-infrared spectroscopic data. A problem arises, though, when the samples to be predicted are measured on a different instrument or under differing environment 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 near-infrared calibration model updating or transfer and a critical assessment of their validity and applicability.
出处
《药物分析杂志》
CAS
CSCD
北大核心
2009年第8期1390-1399,共10页
Chinese Journal of Pharmaceutical Analysis
基金
国家科技支撑计划课题-卫生安全重要技术标准研制(2006BAK04A11)
关键词
近红外光谱
模型维护
模型传递
PDS算法
标准化样品
near - infrared spectroscopy
model updating
calibration transfer
PDS algorithm
standardization samples
