Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end‐to‐end fingerprint conversion based on FT‐NIR spectroscopy

• Published in: Phytochemical analysis 2024-06, Vol.35 (4), p.754-770
• Main Authors: Cui, Tongcan, Ying, Zehua, Zhang, Jianyu, Guo, Shubo, Chen, Wei, Zhou, Guifang, Li, Wenlong
• Format: Article
• Language: English
• Subjects: Algorithms; Chemical composition; Chrysanthemi Flos; Cluster analysis; Clustering; Data collection; fingerprint conversion; Fingerprints; Fourier transforms; FT‐NIR spectroscopy; High-performance liquid chromatography; Infrared analysis; Infrared spectroscopy; Least squares method; Liquid chromatography; Near infrared radiation; Quality assessment; Quality control; quality evaluation; Regression analysis; Similarity; similarity evaluation; Spectroscopy; Spectrum analysis
• ISSN: 0958-0344; 1099-1565
• DOI: 10.1002/pca.3326

Introduction Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. Objective The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near‐infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)‐NIR to HPLC conversion of fingerprints. Materials and methods A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT‐NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. Results The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT‐NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT‐NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. Conclusion This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF. A comprehensive evaluation strategy for assessing Chrysanthemi Flos (CF) quality was developed using HPLC, NIR spectroscopy, and chemometrics. This study included rapid quantification analyses of chemical components and the conversion of FT‐NIR to HPLC fingerprints. Results show that the PLS regression models effectively predict chemical composition content, and both DS and PLSR algorithms successfully convert CF fingerprints from FT‐NIR to HPLC. The approach offers a novel and dependable method for rapid CF quality evaluation.