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Graphical abstract Highlights • Danshen and Zidanshen are successfully classified by near infrared spectroscopy. • Their lipophilic component fingerprints are built by UHPLC and identified by LC–MS. • Lipophilic component fingerprints of Danshen are obviously different from Zidanshen. • Zidanshen has three lipophilic compounds that are not found in Danshen. • The contents of main common compounds are much lower in Zidanshen than in Danshen. Abstract Near infrared spectroscopy (NIR) was applied to discriminate the roots of salvia miltiorhiza Bunge (Danshen for short) and Salvia yunnanensis C. H. Wright (Zidanshen for short) by means of principal component analysis (PCA), improved and simplified K nearest neighbors (IS-KNN). Furthermore, an ultra-high performance liquid chromatographic (UHPLC) coupled with photodiode-array detector was developed for building fingerprints of lipophilic components of Danshen and Zidanshen, respectively. Basing on NIR information, both PCA and IS-KNN method classified the two kinds of Chinese medical herbs with 100% accuracy. The chromatographic fingerprints of the lipophilic components of Danshen and Zidanshen have 10 and 12 common peaks, respectively. Liquid chromatography coupled with mass spectroscopy (LC–MS-MS) was applied to identify these peaks. Among these, three small peaks in the fingerprints of Zidanshen are not found in Danshen, one of which was identified as α-lapachone, and the other two compounds were not yet identified; a small peak after tanshinone IIA in the fingerprints of Danshen was not found in Zidanshen, which was identified as miltirone. The two herbs have 10 common lipophilic components. The similarity between the two reference chromatograms of Zidanshen and Danshen is 0.902, but the mean similaritie between Zidanshen (or Danshen) fingerprints and its own reference chromatogram is 0.973 (or 0.976). The contents of main lipophilic components are significantly lower in Zidanshen than in Danshen (P < 0.01 or P < 0.05). The results indicate that the two Chinese medical materials are not only different in NIR spectra, but also different in species and quantities of lipophilic components. NIR spectra analysis can identify Danshen and Zidanshen rapidly and accurately. UHPLC coupled with MS analysis demonstrates the detail differences between the two herbs both in species and contents of their lipophilic components. [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] • A robust NIR calibration model of TPA was built by a three-step wavelength selection method of SISCW. • The SISCW-PLS model of predicting TPA in tobacco leaves can be re-used by 6 NIR secondaries. • The SISCW-PLS model can serve on 4 NIR devices for 7 years from 2014 to 2020. • The transferability and service life of the SISCW-PLS are superior to other developed models. • SISCW realizes TPA model transfer without standards and improves the model's service life greatly. The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths）to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011–2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as U isc. The results indicated that the partial least square (PLS) calibration model of TPA based on U isc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011–2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model. [ABSTRACT FROM AUTHOR]