Application of orthogonal space regression to calibration transfer without standards



To transfer a calibration model in cases where the standardization samples are rare or unstable, a method based on orthogonal space regression (OSR) is proposed. It uses virtual standardization spectra to account for response changes between instruments or batches. A comparative study of the proposed OSR, piecewise direct standardization, finite impulse response, orthogonal signal correction, and model updating (MU) was conducted on both pharmaceutical tablet data and chlorogenic acid data. The results of these studies suggest that both the OSR and the MU are superior to the other transfer techniques in terms of root-mean-squared error of prediction and ratio of performance to interquartile distance. Moreover, OSR requires no identical standard samples, and it avoids re-optimizing the transfer models. In conclusion, both the differences among spectra measured on different spectrometers and the differences between different batches can be corrected successfully using the OSR method. Copyright © 2013 John Wiley & Sons, Ltd.