Calibration transfer without standards for spectral analysis based on stability competitive adaptive reweighted sampling

Abstract

A novel calibration transfer method based on stability competitive adaptive reweighted sampling (SCARS) was proposed in the present paper. An informative criterion, i. e. the stability index, defined as the absolute value of regression coefficient divided by its standard deviation was used. And the root mean squared error of prediction (RMSEP) after transfer was also used. The wavelength variables which were important and insensitive to influence of measurement parameters were selected. And then the differences in responses of different instruments or measurement conditions for a specific sample were eliminated or reduced to improve the calibration transfer results. Moreover, in the proposed method, the spectral variables were compressed, making calibration transfer more stable. The application of the proposed method to calibration transfer of NIR analysis was evaluated by analyzing the corn with different NIR spectrometers. The results showed that this method can well correct the difference between instruments and improve the analytical accuracy. The transfer results obtained by the proposed method, orthogonal signal correction (OSC), Monte Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighted sampling (CARS), respectively, for corn with different NIR spectrometers indicated that the former gave the best analytical accuracy, and was effective for the spectroscopic data compression which can simplify and optimize the transfer process.