Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC‐QTOF‐MS analysis

• Published in: Phytochemical analysis Vol. 36; no. 1; pp. 92 - 100
• Main Authors: Wang, Xian rui, Zhang, Jia ting, Guo, Xiao han, Li, Ming hua, Jing, Wen guang, Cheng, Xian long, Wei, Feng
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.01.2025
• Subjects: Algorithms; Artificial neural networks; Aucklandiae radix; Chemical composition; chemometrics; Chromatography, High Pressure Liquid - methods; Composition effects; digital identification; Drugs, Chinese Herbal - analysis; Drugs, Chinese Herbal - chemistry; Herbal medicine; Herbs; human resources; Identification; Information processing; Inulae radix; Least-Squares Analysis; Liquid chromatography; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Multivariate Analysis; Neural networks; Neural Networks, Computer; phytochemicals; Quadrupoles; Traditional Chinese medicine; UHPLC‐QTOF‐MS; ultra-performance liquid chromatography; Vladimiriae radix; Vladimiriae radix; chemometrics; digital identification; Aucklandiae radix; UHPLC‐QTOF‐MS; Inulae radix
• ISSN: 0958-0344; 1099-1565; 1099-1565
• DOI: 10.1002/pca.3421

Introduction The 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. Objectives This 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 methods UHPLC‐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. Results The 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. Conclusion ANN 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. To realize digital identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR), UHPLC‐QTOF‐MS combined with multivariate algorithm was used to explore digital identification. The results showed that ANN model is reliability and practicability with accuracy = 0.983 and precision = 0.984. ANN model combined with MS data is of great significance for digital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines.