Due to morphological similarities, adulterants are frequently substituted for Qingfengteng (QFT, Sinomenium acutum) in regional markets. This study developed a TaqMan probe-based real-time quantitative polymerase chain reaction (qPCR) assay targeting a 57-base pair (bp) fragment of the internal transcribed spacer 2 (ITS2) region for the specific detection of QFT. The method was validated using a diverse set of samples, including: (1) 19 batches of QFT and 8 batches of Beidougen (BDG, Menispermum dauricum), comprising both medicinal materials and decoction pieces; (2) 5 batches each of decoction pieces from Qingsheteng (QST, Periploca calophylla), Jishiteng (JST, Paederia foetida), Kuanjinteng (KJT, Tinospora sinensis), and Huibeiqingfengteng (HBQFT, Sabia discolor); (3) 6 batches of commercial QFT-containing tablets (with different batch numbers) and 6 batches of laboratory-prepared QFT aqueous decoctions (with different decocting time). Distinct cycle threshold (Ct) values and amplification curves unambiguously differentiated QFT from all adulterants. An external standard-based quantification approach was established to detect adulteration with BDG, the morphologically and genetically most similar adulterant. Recovery rates ranged from 81.79 to 102.38% in herbal mixed powders spiked with 1%, 5%, 50%, and 100% BDG. The method reliably detected QFT in processed tablets and freeze-dried decoctions, demonstrating high tolerance to deoxyribonucleic acid (DNA) degradation. This qPCR assay enables specific and quantitative detection of QFT in dried and processed samples using short amplicons (57 bp), thereby supporting quality control throughout the herbal production chain.

During the X-ray Diffraction (XRD) identification of the traditional Chinese medicine Os Draconis, the identification model often suffers from limited classification accuracy due to the difficulty in determining optimal parameters. To address this issue, this paper proposes a Hydrodynamic Improved Particle Swarm Optimization (HIIPSO) algorithm for the deep optimization of model parameters. In practical identification scenarios, the high complexity of XRD data poses severe challenges to the convergence speed and global search capability of optimization algorithms. To enhance model performance, this study introduces the interaction mechanism from fluid dynamics into the particle swarm optimization process. Specifically, HIIPSO incorporates a Voronoi neighbor topology to enhance population diversity and spatial distribution rationality. Concurrently, a hydrodynamic interaction mechanism is constructed to simulate the cooperative behavior of particles in a fluid environment, thereby effectively preventing the algorithm from falling into local optima. A theoretical analysis of the computational complexity of the HIIPSO algorithm in the parameter search task for XRD identification models was conducted, confirming that it falls within an ideal range for engineering applications. Statistical analysis of the experimental results demonstrates that, in the parameter optimization task for the Os Draconis identification model, the HIIPSO algorithm significantly outperforms traditional and other baseline algorithms across key metrics, including the optimal value, mean, standard deviation, and median of the objective function. The experimental data indicates that the HIIPSO algorithm can substantially improve the robustness and identification accuracy of the XRD-based Os Draconis identification model, making it an optimal solution for parameter optimization problems in the digital identification of complex mineral-based traditional Chinese medicines.

A simple, rapid, and highly sensitive analytical method was established for identification of constituents in the spot of the thin layer chromatogram of Chuzhou Stemona sessilifolia (Miq.) Miq. (Chuzhou S. sessilifolia) with ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). The technology was applied to systematically analyze and detect the targeted spots. Compared with the fragmentation behaviors of more than thirty reference constituents, the possibly existing compounds of the target spots were identified or tentatively identified by their exact masses and diagnostic fragment ions. Finally, the four clear spots of the thin layer chromatograms of Chuzhou S. sessilifolia were screened and identified of possible molecular formula and structures.