Identification of key phenolic compounds for alleviating gouty inflammation in edible chrysanthemums based on spectrum-effect relationship analyses

Identification of chrysanthemum variety via hyperspectral imaging and wavelength selection based on multitask particle swarm optimization

Chrysanthemum, a widely favored flower tea, contains numerous phytochemicals for health benefits. Due to the different geographical origins and processing technics, its variety has a direct influence on the phytochemical content and pharmacological effect. Accordingly, an accurate identification for chrysanthemum varieties is significant for quality detection and market supervision. In this study, the hyperspectral imaging (HSI) combined with chemometrics methods was exploited to identify the chrysanthemum varieties. First, to alleviate the problem of easily trapping into local optimum in traditional spectral variable selection methods, the multi-tasking particle swarm optimization (MTPSO) was developed to select the key wavelengths by dividing hundreds of variables into low-dimensional subtasks. Second, to enrich the feature information, the spatial texture and color features contained in hyperspectral images were extracted and applied to chrysanthemum identification for the first time. Finally, an ensemble learning model, extreme gradient boosting (XGBoost), was constructed to conduct the chrysanthemum variety classification due to its strong generalization ability. Experimental results showed that the proposed MTPSO achieved the identification accuracy of 96.89%, and increased by 1.11-5.91% than classical spectral feature selection methods. Furthermore, after the involvement of spatial image information, the classification accuracy using spatial-spectral features was improved further, and reached 98.39%. Overall, this study highlights that the feature fusion of key wavelengths and spatial information is more effective for chrysanthemum variety identification, and can also provide technical reference for other HSI-related applications.

Spectrum-Effect Relationship in Chinese Herbal Medicine: Current Status and Future Perspectives

The quality of Chinese herbal medicine (CHM) directly impacts clinical efficacy and safety. Fingerprint technology is an internationally recognized method for evaluating the quality of CHM. However, the existing quality evaluation models based on fingerprint technology have blocked the ability to assess the internal quality of CHM and cannot comprehensively reflect the correlation between pharmacodynamic information and active constituents. Through mathematical methods, a connection between the "Spectrum" (fingerprint) and the "Effect" (pharmacodynamic data) was established to conduct a spectrum-effect relationship (SER) of CHM to unravel the active component information associated with the pharmacodynamic activity. Consequently, SER can efficiently address the limitations of the segmentation of chemical components and pharmacodynamic effect in CHM and further improve the quality evaluation of CHM. This review focuses on the recent research progress of SER in the field of CHM, including the establishment of fingerprint, the selection of data analysis methods, and their recent applications in the field of CHM. Various advanced fingerprint techniques are introduced, followed by the data analysis methods used in recent years are summarized. Finally, the applications of SER based on different research subjects are described in detail. In addition, the advantages of combining SER with other data are discussed through practical applications, and the research on SER is summarized and prospected. This review proves the validity and development potential of the SER and provides a reference for the development and application of quality evaluation methods for CHM.