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Caesalpinia sappan L. (C. sappan L.), a traditional Chinese medicine, has been widely used to treat bruises and dysmenorrhea, performing pharmacological activities such as anti-inflammatory and anti-tumor. C. sappan L. has been reported to contain compounds such as protosappanins, brazilins, and homoisoflavones. In the pre-experiments, we discovered that there were many unstable components in the characteristic chromatogram of C. sappan L. Here, silica-gel column chromatography, high-speed counter-current chromatography, and preparative high-performance liquid chromatography were combined and applied to isolate the unstable components from alcohol extract of C. sappan L. The results showed that four unstable compounds were collected with the purity higher than 95.0%, characterized as episapponal, brazilin, sapponal, and 4-O-methylsapponal by hydrogen-1 and carbon-13 nuclear magnetic resonance. Based on the above results, the characteristic chromatogram of C. sappan L. was established, and the characteristic peaks were identified. These results provided a theoretical basis for the quality assessment of C. sappan L.

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected. [ABSTRACT FROM AUTHOR]

Traditional Chinese Medicine (TCM) formulations often contain a complex mixture of compounds, making it challenging to evaluate their quality. Buzhong Yiqi Oral Liquid (BYOL), a well-known TCM formulation, has been used to treat gastrointestinal diseases and fatigue for a long time. However, the effective substances for its therapeutic effects remain unclear. This study aimed to establish a novel quality control approach for BYOL using a multidimensional feature network to identify quality markers (Q-markers). The multidimensional characteristic network was constructed by a "Spider-Web" model, including the compatibility of ingredients, traceability of compounds from raw materials to the final product, effectiveness, measurability, and stability. Multivariate statistical analysis was applied to quantify the individual dimensions of the feature network, and candidate compounds were identified based on regression regions within the network. Using the multidimensional characteristic network, 12 components were identified as key Q-markers for BYOL, consisting of calycosin-7-O-β-D-glucoside, formononetin, astragaloside IV, liquiritin, quercetin, hesperidin, glycyrrhizic acid, isorhamnetin, liquiritin apioside, hesperidin, luteolin, and nobiletin. The identified Q-markers offered valuable insights for future research on medicinal substances and quality control of BYOL. [ABSTRACT FROM AUTHOR]

There has been an increasing demand for rapid and sensitive techniques for the detection of heavy metal ions that are harmful to the human body in traditional Chinese medicine (TCM). However, the complex chemical composition of TCM makes the quantitative detection of heavy metal ions difficult. In this study, the magnetic Fe3O4@SiO2@AuNPs nanoparticles combined with a probe molecule DMcT were used for the specific enrichment and detection of Hg2+ in the complex system of licorice. The core of Fe3O4 was bonded with SiO2 to increase its stability. A layer of AuNPs was deposited to produce a "core–shell" Raman substrate with high surface-enhanced Raman spectroscopy (SERS) activity, which was surface modified by DMcT probe molecules with sulfhydryl groups. In the presence of Hg2+, Hg2+ binds to N on the amino group of DMcT to form N-Hg2+-N complexes, which induces Fe3O4@SiO2@AuNPs-DMcT clustering to enhance SERS signal. The Raman probe molecule DMcT showed an excellent linear relationship (R2 = 0.9709) between the SERS signal at 1416 cm−1 and the Hg2+ concentration (0.5~100 ng/mL). This method achieved a good recovery (89.10~111.00%) for the practical application of detection of Hg2+ in licorice extracts. The results demonstrated that the functional Fe3O4@SiO2@AuNPs-DMcT performed effective enrichment and showed high sensitivity and accurate detection of heavy metal ions from the analytes. [ABSTRACT FROM AUTHOR]