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Importance of Prediction Outlier Diagnostics in Determining a Successful Inter-vendor Multivariate Calibration Model Transfer

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This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.


Document Type: Research Article

DOI: http://dx.doi.org/10.1366/000370207781393280

Affiliations: 1: Process Analytical Technology, Merck Manufacturing Division, Merck & Company, Inc, West Point, Pennsylvania 19486 2: MITRE Corporation, McLean, Virginia 22102-0758 3: Analytical Sciences Laboratory, The Dow Chemical Company, Midland, Michigan 48667 4: Sandia National Laboratories, Albuquerque, New Mexico 87185-0895

Publication date: July 1, 2007

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