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. 2024 Nov 8;8(1):91.
doi: 10.1038/s41538-024-00333-3.

Identification strategy of wild and cultivated Astragali Radix based on REIMS combined with two-dimensional LC-MS

Sijian Chen  1 , Xiaoshuang Li  1 , Danshu Shi  2 , Yisheng Xu  3 , Yingyuan Lu  4 , Pengfei Tu  5
Affiliations

Identification strategy of wild and cultivated Astragali Radix based on REIMS combined with two-dimensional LC-MS

Sijian Chen et al. NPJ Sci Food. .

Abstract

A rapid and real-time method was established based on the combination of rapid evaporative ionization mass spectrometry (REIMS) and two-dimensional liquid chromatography mass spectrometry (2DLC-MS) for identification of wild Astragali Radix (WAR) and cultivated AR (CAR). The samples were analyzed under ambient ionization without time-consuming sample preparation. The phenotypic data of WAR and CAR were used to develop a real-time recognition model. Subsequently, the compounds in these two species were comprehensively characterized based on 2DLC-MS, and 45 different compounds were screened out by multivariate statistical analysis. A semi-quantitative method for 45 different compounds was established based on ultrahigh-performance liquid chromatography/quadrupole-linear ion trap mass spectrometry (UHPLC-QTRAP-MS). The results showed that the relative content of most compounds in WAR was higher than in CAR. In summary, the method has demonstrated remarkable performance in distinguishing between WAR and CAR, providing a reference in the field of traditional Chinese medicine (TCM) analysis and identification.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Experimental workflow of the study.
a REIMS ion source and transfer setup. b 2DLC-MS characterization. c QTRAP-MS semi-quantitative analysis.
Fig. 2
Fig. 2. Representative TICs and mass spectra of WAR and CAR.
a Representative TIC of WAR. b Representative TIC of CAR. c The mass spectra of WAR in the range of m/z 100–1000. d The mass spectra of CAR in the range of m/z 100–1000.
Fig. 3
Fig. 3. REIMS was used to model and identify the CAR and WAR.
a Tridimensional visualization of PCA. b Tridimensional visualization of PCA-LDA. c Bidimensional score lots along the first two main linear discriminants. d Loading plot relative to the PC1. e Loading plot relative to the PC2 (explaining 32.48% and 8.38% of the total variance of the model). f Real-time recognition by LiveID of WAR in the mutivariate model. g Real-time recognition by LiveID of CAR in the mutivariate model.
Fig. 4
Fig. 4. Waterfall diagram of representative TICs.
a The TICs of WAR obtained by 2DLC-MS. b The TICs of CAR obtained by 2DLC-MS.
Fig. 5
Fig. 5. Multivariate statistical analysis of WAR and CAR to unveil differential ions.
a Score plots of PCA. b Score plots of OPLS-DA. c Results of 100 permutation tests of OPLS-DA. d Volcano map.
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