UPLC-Q-TOF-MS/MS combined with machine learning methods for screening quality indicators of Hypericum perforatum L.

Hypericum perforatum L. (HPL), also known as St. John's wort, is one of the extensively researched domestically and internationally as a medicinal plant. In this study, non-targeted metabolomics combined with machine learning methods were used to identify reasonable quality indicators for the holistic quality control of HPL. First, the high-resolution MS data from different samples of HPL were collected, and visualized the chemical compounds through the MS molecular network. A total of 122 compounds were identified. Then, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established for comparing the differences in metabolite expression between flower, leaf, and branches. A total of 46 differential metabolites were screened out. Subsequently, analyzing the pharmacological activities of these differential metabolites based on protein-protein interaction (PPI) network. A total of 25 compounds associated with 473 gene targets were retrieved. Among them, 13 highly active compounds were selected as potential quality markers, and five compounds were ultimately selected as quality control markers for HPL. Finally, three different classifiers (support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN)) were used to validate whether the selected quality control markers are qualified. When the feature count is set to 122 and 46, the RF model demonstrates optimal performance. As the number of variables decreases, the performance of the RF model degrades. The KNN model and the SVM model also exhibit a decrease in performance but still manage to satisfy the intended requirements. The strategy can be applied to the quality control of HPL and can provide a reference for the quality control of other herbal medicines.

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

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.

Change of season-specific telomere lengths in Ginkgo biloba L.

Telomeres have lately received considerable attention in the development of tree species. Normal somatic cells have limited replicative capacity and telomeres get shorten with each round of DNA replication. For broad-leaved tree species, to determine what changes happen to their somatic cells in its annual development cycle, an exhaustive research on different ages of gingko trees telomere length changes was carried out. Analysis of changes in leaf telomere lengths in the annual development cycle of Ginkgo biloba L. showed no significant changes (P > 0.05) from April to August, but a dramatic decrease in September and October (P < 0.05). Statistical analyses showed that TRF length of males and females are equal, the p values of the three age groups comparison were all bigger than 0.05. The results showed that specific apoptotic changes occur in the annual development cycle of Ginkgo biloba L.