Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867020845512130561 |
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| author | Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei |
| author_facet | Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei |
| collection | Wiley Open Access |
| contents | Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei Phytochemical Analysis AbstractIntroductionThe identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time‐consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough.ObjectivesThis study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra‐high‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR.Materials and methodsUHPLC‐QTOF‐MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS‐DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs.ResultsThe results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model.ConclusionANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC‐QTOF‐MS and multivariate algorithms. 10.1002/pca.3421 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/pca.3421 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_pca_3421 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei Phytochemical Analysis Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis Xian rui Wang Jia ting Zhang Xiao han Guo Ming hua Li Wen guang Jing Xian long Cheng Feng Wei Phytochemical Analysis AbstractIntroductionThe identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time‐consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough.ObjectivesThis study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra‐high‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR.Materials and methodsUHPLC‐QTOF‐MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS‐DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs.ResultsThe results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model.ConclusionANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC‐QTOF‐MS and multivariate algorithms. 10.1002/pca.3421 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis |
| topic | Phytochemical Analysis |
| url | https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pca.3421 |