Robust calibration model transfer

Abstract

Calibration model transfer is an important issue in infrared spectra analysis. For identical sample, spectra collected with master and slave spectrometers share same components. In the sense of mathematics, they share same basis. If the basis and corresponding coefficient matrices can be obtained, the model transfer can be efficiently realized. On the other hand, the performance of calibration model transfer method will degrade if there are outliers and noise in samples. In this paper, a robust calibration transfer model is proposed. Cauchy estimator are employed to learn same basis shared by master and slave spectra robustly. Transformation matrix can be calculated with the two corresponding coefficient matrices. Slave testing spectra are represented with the common basis and corresponding coefficients are then transferred using the transformation matrix. The slave testing spectra can be transferred using common basis and the corrected coefficients. The convergence property and bound of proposed model are also discussed. Extensive experiments are conducted, experimental results demonstrate that our robust calibration transfer model can generally outperform the existing methods.