Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models

Chen, Zeng-Ping and Li, Li-Mei and Yu, Ru-Qin and Littlejohn, David and Nordon, Alison and Morris, Julian and Dann, Alison S. and Jeffkins, Paul A. and Richardson, Mark D. and Stimpson, Sarah L. (2011) Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models. Analyst, 136 (1). pp. 98-106. ISSN 0003-2654 (https://doi.org/10.1039/c0an00171f)

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Abstract

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.

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