Frequently, a spectral-based multivariate calibration model formed on a particular instrument (primary) needs to predict samples measured on other (secondary) instruments of the same spectral type. This situation is often referred to as calibration maintenance or transfer. A new calibration maintenance approach is developed in this paper using spectral differences between instruments. In conjunction with a sample weighting scheme, spectral differences are piecewise (wavelength window) or full spectrum fitted with modeling terms (correction terms) such as polynomials and derivatives. Results demonstrating the potential usefulness of the new method using a near infrared (NIR) benchmark dataset are presented in this paper. The process does not need a standardization sample set measured in the primary condition. Thus, the new approach is a “hybrid” between the popular methods of extended inverted multiplicative signal correction (EISC) and direct standardization (DS) or piecewise DS (PDS). It is found that prediction errors reduce for samples measured in the secondary condition compared to those based on no calibration transfer. Prediction errors are also comparable to those from a full calibration in the secondary condition. In addition to instrument correction, an extension of the new approach is discussed (but not tested) for predicting new samples changing over time due to new chemical, physical, and environmental measurement conditions including individually or combinations of temperature, sample particle size, and new spectrally responding species.
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