The method of calibration model transfer by optimizing wavelengths combinations based on consistent and stable spectral signals

Abstract Based on the wavelengths with consistent and stable spectral signals between spectrometers, wavelengths combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with the dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as UscrUVE, UscrVIP and UscrSR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as UscrUVE-PLS, UscrVIP-PLS, UscrSR-PLS, respectively. Both UscrUVE-PLS and UscrVIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model based on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelengths combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models' efficiency, but also ensure the models’ accuracy when they are transferred to the slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.