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Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

Du, Wen and Chen, Zeng-Ping and Zhong, Li-Jing and Wang, Shu-Xia and Yu, Ru-Qin and Nordon, Alison and Littlejohn, David and Holden, Megan (2011) Maintaining the predictive abilities of multivariate calibration models by spectral space transformation. Analytica Chimica Acta, 690 (1). pp. 64-70. ISSN 0003-2670

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Abstract

In quantitative on-line/in-line monitoring of chemical and bio-chemical processes using spectroscopic instruments, multivariate calibration models are indispensable for the extraction of chemical information from complex spectroscopic measurements. The development of reliable multivariate calibration models is generally time-consuming and costly. Therefore, once a reliable multivariate calibration model is established, it is expected to be used for an extended period. However, any change in the instrumental response or variations in the measurement conditions can renders multivariate calibration model invalid. In this contribution, a new method, spectral space transformation (SST), has been developed to maintain the predictive abilities of multivariate calibration models when the spectrometer or measurement conditions are altered. SST tries to eliminate the spectral differences induced by the changes in instruments or measurement conditions through the transformation between two spectral spaces spanned by the corresponding spectra of a subset of standardization samples measured on two instruments or under two sets of experimental conditions. The performance of the method has been tested on two data sets comprising NIR and MIR spectra. The experimental results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used. Compared with the existing popular methods designed for the same purpose. i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). SST has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy.