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Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments. [ABSTRACT FROM AUTHOR]

Abstract Measurement environmental changes and spectral signal differences among multi-spectrometers may lead to big error in the process of transferring the model of near infrared (NIR) spectra to secondary instruments. Basing on the common sense, NIR calibration models could be shared directily among multi-instruments, if they were built on the wavelengths at which the spectral signals of secondary instruments are stable and well consistent with the primary's. Present work advanced a method named as screening wavelengths with consistent and stable signals (SWCSS) to transfer NIR calibration models. It eliminates the wavelengths at which the standard deviation of difference spectra between the primary and secondary instruments (SDDSI) is much higher than the standard deviation of precision detection spectra (SDPDS) of a sample tested on the primary and the wavelengths with higher SDPDS values. So that the spectral signals of different instruments at these selected wavelengths are consistent well and stable. The NIR calibration model is built by partial least square regression (PLS) based on the screened wavelengths. Two datasets of corn and radix scutellariae samples measured with different NIR instruments are used to test the performance of the method. The results show that the overall prediction performance of the SWCSS-PLS models for samples measured on secondaries is much better than that of the full-wavelength PLS models. The root mean square of error prediction (RMSEP) of the SWCSS-PLS models for samples tested on secondaries is equivalent or superior to that of the piecewise direct standardization (PDS) correction. The SWCSS-PLS model is of fewer parameters, robust and can give good prediction results for samples of secondary instruments. Because standard samples are no longer needed during transferring the calibration models to secondary instruments, SWCSS-PLS models could be directly shared by multi-spectroscopy instruments. Graphical Abstract Unlabelled Image Highlights • A method named as SWCSS selecting wavelengths to build robust PLS models was advanced. • SWCSS selects consistent and stable spectral variables by analyzing spectra deviation. • The PLS models built by SWCSS are accurate for samples of host and slave instruments. • The SWCSS-PLS models have good transfer performance on multi-instruments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Scale invariant feature transform (SIFT) method is applied to select characteristic NIR variables. • SIFTur-stable and independent feature variables are screened from the variables selected by SIFT. • PLS models built on SIFTur at many nLVs work well for both master and slave samples. In order to realize calibration model transfer of near infrared (NIR) spectra without standards, scale invariant feature transform (SIFT) algorithm was applied to extract characteristic spectral points of NIR spectra in this study. Three sets of spectral points were selected by SIFT from the spectra of precision detection (SPD) of a radix scutellariae sample by continuously testing the sample three times. Aiming at obtaining high consistency of the three sets, the orthogonal table L9 (34) was used to optimize the parameters of SIFT. Basing on the NIR spectra of several representative radix scutellariae samples, a series of spectral point sets were screened by SIFT with the optimized parameters. Three methods of further treating the spectral points sets to optimize the combination of the spectral points and provided three spectral point sets, which were recorded as U i , U u and U ur , respectively. The partial least square (PLS) calibration models for predicting baicalin content of radix scutellariae were built on whole wavelengths, U i , U u and U ur at different number of latent variables (nLVs), respectively. Compared with other PLS models, the models of SIFTur-PLS built on U ur , which was obtained by taking union of the firstly selected spectral point sets, then eliminating the points with high deviance of SPD and those with high correlativity from the union, are most robust and always give lower or lowest prediction errors for both master and slave samples at many nLVs. It is a good way to filter stable, highly independent and characteristic spectral points to build robust PLS calibration models by combining SIFT algorithm with standard deviance analysis of SPD and correlative analysis. The models can be directly shared by the slave instrument, without needing transfer sets, and without requiring to correct the spectra of slave instruments or spectral calibration models. [ABSTRACT FROM AUTHOR]

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength 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 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 built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength 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 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. Image 1 • Robust PLS models were built by selecting wavelengths from those with consistent and stable signals. • Suitable wavelength selection methods provide good wavelength combinations. • The robust PLS model has good transfer performance and can be directly shared on multi-instruments. [ABSTRACT FROM AUTHOR]