Application of Kalman filtering to multivariate calibration and drift correction

被引:16
作者
Andrew, KN
Rutan, SC
Worsfold, PJ
机构
[1] Univ Plymouth, Plymouth Environm Res Ctr, Dept Environm Sci, Plymouth PL4 8AA, Devon, England
[2] Virginia Commonwealth Univ, Dept Chem, Richmond, VA 23284 USA
关键词
Kalman filter; multivariate calibration; drift correction; diode array spectrophotometry; transition metals;
D O I
10.1016/S0003-2670(99)00084-7
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper discusses a recursive digital filtering technique, the Kalman filter, which has potential applications for on-line process and environmental monitoring. Two different models are initially used with the Kalman filter algorithm for multivariate calibration of multicomponent spectral data sets obtained by diode array spectrophotometric measurements of synthetic transition metal mixture solutions. The predictive accuracies are compared with those obtained in previous work using direct multicomponent analysis (DIMA) and partial least squares regression (PLS1). A model based on K-matrix regression in conjunction with the Kalman filter is generally found to produce improved predictive performances over DMA and a DMA-type Kalman filter model, but cannot match the performance of PLS1 when significant physical or chemical interference effects an present. A further modification of the model is applied to the determination and correction of linear and random baseline drift components in single- and multicomponent spectral data. Relative calibration and prediction errors obtained using this third model are found to be significantly lower than those achieved using Kalman filter models with no drift collection capability (all <1% when using a value of zero for q, the system noise variance). (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:315 / 325
页数:11
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