Abstract
Calibration transfer between near-infrared (NIR) spectrometers is a subtle issue in chemometrics and process industry. In fact, as even very similar instruments may generate strongly different spectral responses, regression models developed on a first NIR system can rarely be used with spectra collected by a second equipment. Several ways of addressing this issue have been described in the scientific literature, such as global modeling, Piecewise Direct Standardization (PDS) and Maximum Likelihood Principal Component Analysis (MLPCA).
In this work, two novel methods to perform calibration transfer between NIR spectrometers are proposed. Both of them permit to exploit the specific relationships between instruments for imputing new unmeasured spectra, which will be then resorted to for building an improved predictive model, suitable for the analysis of future incoming data. Specifically, the two approaches are based on Trimmed Scores Regression (TSR) and Joint-Y Partial Least Squares regression (JY-PLS), respectively.
The performance of these novel strategies is assessed and compared to that of classical well-established techniques in a real-world case study.
