Simultaneous quantification of twelve active components in Yiqing granule by ultra-performance liquid chromatography: application to quality control study

Zwitterionic polymers grafting of metal organic framework encapsulated boronic acid carbon dots as antibiofouling fluorescent probe for baicalin monitoring

The sensitivity and accuracy of fluorescence probes for biological samples are affected by not only interfering molecule compounds but also the nonspecific adsorption of proteins and other macromolecules. Herein, fluorescence probe based on zwitterionic sulfobetaine methacrylate polymer (PSBMA) as an antibiofouling layer and amino boric acid carbon dots encapsulated in the metal-organic framework UiO-66-NH2 (UiO-66-NH2/BN-CDs) as a target recognition site was designed for the detection of baicalin (BAI). Owing to the introduction of BN-CDs into UiO-66-NH2 with high specific surface area, the prepared UiO-66-NH2/BN-CDs@PSBMA probe exhibited a high adsorption capacity of 78.9 mg g-1, while presented fluorescence enhancing and superior fluorescence selectivity to BAI at excitation and emission wavelengths of 400 and 425 nm, respectively. Connecting PSBMA with good hydrophilicity to UiO-66-NH2, resulted in an anti-protein capacity of over 96.3 %, effectively inhibiting protein interference with the fluorescence signal. By virtue of its good antibiofouling and recognizing capacities, the fluorescence probe exhibited a satisfactory detection range of 10-80 nmol L-1, with a fairly low detection limit of 0.0064 μmol L-1. Using the method to detect BAI in Goji berry, Sophora and Yinhuang oral solution, demonstrating its potential for the accurate and quantitative detection of BAI in complex biological samples.

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) (P < 0.05). By setting the cluster size K = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm-1 for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

Chemical Profile Determination and Quantitative Analysis of Components in Oryeong-san Using UHPLC-Q-Orbitrap-MS and UPLC-TQ-MS/MS

In this study, a method to both qualitatively and quantitively analyze the components of Oryeong-san (ORS), which is composed of five herbal medicines (Alisma orientale Juzepzuk, Polyporus umbellatus Fries, Atractylodes japonica Koidzumi, Poria cocos Wolf, and Cinnamomum cassia Presl) and is prescribed in traditional Oriental medicine practices, was established for the first time. First, ORS components were profiled using ultra-high-performance liquid chromatography/quadrupole Orbitrap mass spectrometry, and 19 compounds were clearly identified via comparison against reference standard compounds. Subsequently, a quantitative method based on ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry was established to simultaneously measure the identified compounds. Nineteen compounds were accurately quantified using the multiple-reaction-monitoring mode and used to analyze the sample; we confirmed that coumarin was the most abundant compound. The method was validated, achieving good linearity (R2 ≤ 0.9991), recovery (RSD, 0.11-3.15%), and precision (RSD, 0.35-9.44%). The results suggest that this method offers a strategy for accurately and effectively determining the components of ORS, and it can be used for quality assessment and management.

Qualitative Profiling and Quantitative Analysis of Major Constituents in Jinmu-tang by UHPLC-Q-Orbitrap-MS and UPLC-TQ-MS/MS

Jinmu-tang (JMT) is a traditional herbal medicine consisting of five herbal medicines: Poria cocos Wolf, Paeonia lactiflora Pallas, Zingiber officinale Roscoe, Atractylodes japonica Koidzumi, and Aconitum carmichaeli Debeaux. In this study, the JMT components were profiled using UHPLC-Q-Orbitrap-MS, and 23 compounds were identified and characterized. In addition, UPLC-TQ-MS/MS analysis was performed in the positive and negative ion modes of an electrospray ionization source for the simultaneous quantification of the identified compounds. The multiple reaction monitoring (MRM) method was established to increase the sensitivity of the quantitative analysis, and the method was verified through linearity, recovery, and precision. All analytes showed good linearity (R2 ≤ 0.9990). Moreover, the recovery and the relative standard deviation of precision were 86.19-114.62% and 0.20-8.00%, respectively. Using the established MRM analysis method, paeoniflorin was found to be the most abundant compound in JMT. In conclusion, these results provide information on the constituents of JMT and can be applied to quality control and evaluation.

Sulfur-doped carbon dots@polydopamine-functionalized magnetic silver nanocubes for dual-modality detection of norovirus

Synergistic dual-mode optical platforms are up-and-coming detection tools in the diagnosis and management of infectious diseases. Here, novel dual-modality fluorescence (FL) and surface-enhanced Raman scattering (SERS) techniques have been integrated into a single probe for the rapid and ultrasensitive detection of norovirus (NoV). The developed FL-SER-based biosensor relies on the dual-signal enhancements of newly synthesized sulfur-doped agar-derived carbon dots (S-agCDs). The antigen-antibody immunoreaction results in forming a core-satellite immunocomplex between anti-NoV antibody-conjugated S-agCDs and polydopamine-functionalized magnetic silver nanocubes [poly (dop)-MNPs-Ag NCs]. By deploying an immunomagnetic enrichment protocol and performing the SERS modality on a single-layer graphene substrate, norovirus-like particles (NoV-LPs) were detected across a wide range of 1 fg mL^-1 - 10 ng mL^-1 with an excellent limit of detection of 0.1 fg mL^-1. The combined advantage of the dual-signaling properties of the biosensor was demonstrated using FL confocal imaging for "hotspots" tracking prior to SERS detection of clinical NoV in fecal specimen down to ⁓10 RNA copies mL^-1. The proposed dual-modality biosensor's performance increases the prospect of a rapid and low-cost sensitive NoV detection and surveillance option for public health.

Rapid quantification of active pharmaceutical ingredient for sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on machine learning techniques

Rapid quantification methods for sugar-free Yangwei granules were developed based on near-infrared (NIR) spectroscopy combined with machine learning approaches as a quality control strategy for Chinese medicine granules (CMGs). Different machine learning approaches-i.e., interval partial least squares optimized by the genetic algorithm (GA-iPLS), the backpropagation artificial neural network (BP-ANN), and the particle swarm optimization-support vector machine (PSO-SVM)-were used to develop prediction models for three active pharmaceutical ingredients (APIs), namely, albiflorin, paeoniflorin, and benzoylpaeoniflorin. The partial least squares (PLS) algorithm was used for linear model calibration and comparison of the prediction performance of these developed models. The performance of the final models was assessed by the correlation coefficient (R), root mean square error of calibration set (RMSEC), and root mean square error of prediction set (RMSEP). All models performed well in model fitting and provided satisfactory prediction accuracy. The results indicate that the machine learning approaches are more stable, predictable, and suitable for CMGs when a high-accuracy analysis is required. In summary, NIR spectroscopy coupled with machine learning techniques is a suitable tool for the straightforward quantification of CMGs.

Qualitative and quantitative analysis of the major constituents in Acorus tatarinowii Schott by HPLC/ESI-QTOF-MS/MS

Acorus tatarinowii Schott (ATS) is a well-known traditional Chinese medicine (TCM) for the treatment of epilepsy, amnesia and insomnia. In this study, a methodology utilizing high-performance liquid chromatography (HPLC) coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-QTOF-MS/MS) was established for the separation and structural identification of the major chemical constituents in ATS for the first time. Overall, 46 major constituents including flavonoid glycosides, phenylpropane derivatives, amides and lignans were identified or tentatively characterized. Seven major constituents, including four phenylpropane derivatives and three lignans, were further quantified as marker substances, which showed good linearity within the test ranges. These results indicated that the developed quantitative method was linear, sensitive, and precise for quality control of ATS.

Quality control of traditional Chinese medicines: a review

Traditional Chinese medicines (TCMs) are in great demand all over the world, especially in the developing world, for primary health care due to their superior merits such as low cost, minimal side effects, better cultural acceptability, and compatibility with humans. However, Chinese medicines consist of several herbs which may contain tens, hundreds, or even thousands of constituents. How these constituents interact with each other, and what the special active ones are, may be the biggest bottleneck for the modernization and globalization of TCMs. Valid methods to evaluate the quality of TCMs are therefore essential and should be promoted and be developed further through advanced separation and chromatography techniques. This paper reviews the strategies used to control the quality of TCMs in a progressive perspective, from selecting single or several ingredients as the evaluation marker, to using different kinds of chromatography fingerprint methods. In summary, the analysis and quality control of TCMs are developing in a more effective and comprehensive manner to better address the inherent holistic nature of TCMs.