Screening of potential α-glucosidase inhibitors from astragalus membranaceus by affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS method.

Astragalus membranaceus is a traditional Chinese medicine with multiple pharmacological activities. Modern pharmacological research has found that Astragalus membranaceus extract has an inhibitory effect on α-glucosidase, however, which component can inhibit the activity of α-glucosidase and its degree of inhibition are unknown. To address this issue, this study used affinity ultrafiltration screening combined with UPLC-ESI-Orbitrap-MS technology to screen α-glucosidase inhibitors in Astragalus membranaceus. Using affinity ultrafiltration technology, we obtained the active components, and using UPLC-ESI-Orbitrap-MS technology, we quickly analyzed and identified them. As a result, a total of 8 ingredients were selected as α-glucosidase inhibitors.

Screening of GLP-1r agonists from natural products using affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology: a case study of Panax ginseng.

In our study, a method based on affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology was established to select Glucagon-like peptide-1 receptor (GLP-1R) agonists from natural products, and as an example, the GLP-1R agonists from Panax ginseng was selected using our established method. As a result, total five GLP-1R agonists were selected from Panax ginseng for the first time. Our results indicated that activating GLP-1R to promote insulin secretion probably was another important hypoglycemia mechanism for ginsenosides in Panax ginseng, which had great influence on the study of the anti-diabetes effect of ginsenosides.

[Research on mechanism of hypolipidemic effect of Massa Medicata Fermentata based on metabolomics].

This paper aims to study the therapeutic effect of Massa Medicata Fermentata on hyperlipidemia model rats and investigate its mechanism of hypolipidemic effect with the help of non-targeted metabolomics. The mixed hyperlipidemia model rats were constructed by giving high-fat chow. After successful modeling, the rats were divided into the model group, pravastatin sodium group(4.4 mg·kg~(-1)), lipotropic group(0.1 g·kg~(-1)), high-dose group(2.4 g·kg~(-1)), medium-dose group(1.2 g·kg~(-1)), and low-dose group(0.6 g·kg~(-1)) of Massa Medicata Fermentata, and they were administered for four weeks once daily. An equal volume of ultrapure water was given to the blank group and model group. Serum lipid level and liver hematoxylin-eosin(HE) staining were used as indicators to estimate the intervention effect of Massa Medicata Fermentata on mixed hyperlipidemia, and the changes in metabolites in plasma of mixed hyperlipidemia model rats were analyzed by non-targeted metabolomics. The mechanism of the hypolipidemic effect of Massa Medicata Fermentata was analyzed through metabolite pathway enrichment. The results showed that compared with the model group, the Massa Medicata Fermentata administration group, especially the high-dose group, could significantly reduce the content of total cholesterol(TC), triglyceride(TG), and low-density lipoprotein cholesterol(LDL-c)(P<0.05 or P<0.01), and liver HE staining revealed that the number of adipocytes in the high-dose group was reduced to some extent. The potential biomarkers obtained by non-targeted metabolomics screening included glycerol 3-phosphate, sphingomyelin, sphingosine 1-phosphate, and deoxyuridine, which were mainly involved in the sphingolipid metabolism process, glycerophospholipid metabolism process, glycerol ester metabolism pathway, and pyrimidine metabolism pathway, totaling four possible metabolic pathways related to lipid metabolism. This study provides a reference for an in-depth investigation of the hypolipidemic mechanism of Massa Medicata Fermentata, which is of great significance for further promoting the clinical application of Massa Medicata Fermentata and increasing the indications.

Screening of Potential α-Glucosidase Inhibitors from the Roots and Rhizomes of Panax Ginseng by Affinity Ultrafiltration Screening Coupled with UPLC-ESI-Orbitrap-MS Method.

Panax ginseng was a traditional Chinese medicine with various pharmacological activities and one of its important activities was hypoglycemic activity; therefore, panax ginseng has been used in China as an adjuvant in the treatment of diabetes mellitus. In vivo and in vitro tests have revealed that ginsenosides, which are derived from the roots and rhizomes of panax ginseng have anti-diabetic effects and produce different hypoglycemic mechanisms by acting on some specific molecular targets, such as SGLT1, GLP-1, GLUTs, AMPK, and FOXO1. α-Glucosidase is another important hypoglycemic molecular target, and its inhibitors can inhibit the activity of α-Glucosidase so as to delay the absorption of dietary carbohydrates and finally reduce postprandial blood sugar. However, whether ginsenosides have the hypoglycemic mechanism of inhibiting α-Glucosidase activity, and which ginsenosides exactly attribute to the inhibitory effect as well as the inhibition degree are not clear, which needs to be addressed and systematically studied. To solve this problem, affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology was used to systematically select α-Glucosidase inhibitors from panax ginseng. The ligands were selected through our established effective data process workflow based on systematically analyzing all compounds in the sample and control specimens. As a result, a total of 24 α-Glucosidase inhibitors were selected from panax ginseng, and it was the first time that ginsenosides were systematically studied for the inhibition of α-Glucosidase. Meanwhile, our study revealed that inhibiting α-Glucosidase activity probably was another important mechanism for ginsenosides treating diabetes mellitus. In addition, our established data process workflow can be used to select the active ligands from other natural products using affinity ultrafiltration screening.

Comprehensive Identification of Ginsenosides in the Roots and Rhizomes of Panax ginseng Based on Their Molecular Features-Oriented Precursor Ions Selection and Targeted MS/MS Analysis.

Panax ginseng is widely used in Asian countries and its active constituents-ginsenosides-need to be systematically studied. However, only a small part of ginsenosides have been characterized in the roots and rhizomes of panax ginseng (RRPG) up to date, mainly because of a lack of the fragmentation ions of many more ginsenosides. In order to comprehensively identify ginsenosides in RRPG, molecular features of ginsenosides orienting precursor ions selection and targeted tandem mass spectrometry (MS/MS) analysis strategy were proposed in our study, in which the precursor ions were selected according to the molecular features of ginsenosides irrespective of their peak abundances, and targeted MS/MS analysis was then performed to obtain their fragmentation ions for substance characterization. Using this strategy, a total of 620 ginsenosides were successfully characterized in RRPG, including 309 protopanaxadiol-type ginsenosides, 258 protopanaxatriol-type ginsenosides and 53 oleanane-type ginsenosides. It is worth noting that, except for the known aglycones mass-to-charge ratio (m/z) 459, 475 and 455, twelve other aglycones, including m/z 509, 507, 493, 491, 489, 487, 477, 473, 461, 457, 443 and 441, were first reported in our experiment and they were probably the derivatizations of the protopanaxatriol and protopanaxadiol. Our study will not only help people to improve the cognition of ginsenosides in RRPG, but will also play a guiding and reference role for the isolation and characterization of potentially new ginsenosides from RRPG.

[Research progress and maturity assessment of continuous manufacturing of traditional Chinese medicine].

The pharmaceutical manufacturing model is gradually changing from intermittent manufacturing to continuous manufacturing and intelligent manufacturing. This paper briefly reviewed the supervision and research progress in continuous pharmaceutical manufacturing in China and abroad and described the definition and advantages of continuous pharmaceutical manufacturing. The continuous manufacturing of traditional Chinese medicine(TCM) at the current stage was summarized in the following three terms: the enhancement of the continuity of intermittent manufacturing operations, the integration of continuous equipment to improve physical continuity between units, and the application of advanced process control strategies to improve process continuity. To achieve continuous manufacturing of TCM, the corresponding key technologies, such as material property characterization, process modeling and simulation, process analysis technology, and system integration, were analyzed from the process and equipment, respectively. It was proposed that the continuous manufacturing equipment system should have the characteristics of high speed, high response, and high reliability, "three high(H~3)" for short. Considering the characteristics and current situation of TCM manufacturing, based on the two dimensions of product quality control and production efficiency, a maturity assessment model for continuous manufacturing of TCM, consisting of operation continuity, equipment continuity, process continuity, and quality control continuity, was proposed to provide references for the application of continuous manufacturing technology for TCM. The implementation of continuous manufacturing or the application of key continuous manufacturing technologies in TCM can help to systematically integrate advanced pharmaceutical technology elements and promote the uniformity of TCM quality and the improvement of production efficiency.

[Effect of Danhong Injection on platelet metabolites based on metabonomics technology].

The effect of Danhong Injection on the endogenous metabolites of rabbit platelets was analyzed by the liquid chromatography-mass spectrometry( LC-MS) based metabonomic approach. Anti-platelet aggregation was detected after Danhong Injection treatment and the changes of platelet metabolites were analyzed by metabonomics. Principal component analysis( PCA) and partial least squares discriminant analysis( PLS-DA) were performed to investigate the effect of Danhong Injection on endogenous metabolites of platelets,characterize the biomarkers,and explore the relevant pathways and the underlying mechanism. As demonstrated by the pharmacodynamic results,Danhong Injection of different doses and concentrations antagonized platelet aggregation in a dose-and concentration-dependent manner. In contrast to the control group,25 differential metabolites such as nicotinic acid,nicotinic acid riboside,and hypoxanthine were screened out after platelets were treated by Danhong Injection. These metabolites,serving as important biomarkers,were mainly enriched in the nicotinic acid-niacinamide metabolic pathway and purine metabolic pathway. This study explored the therapeutic mechanism of Danhong Injection from a microscopic perspective by metabonomics,which is expected to provide a new idea for the investigation of platelet-related mechanisms.

[Key technologies and applications of industrial big data in manufacturing of Chinese medicine].

Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

[A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology].

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.

[Online endpoint detection algorithm for blending process of Chinese materia medica].

Blending process, which is an essential part of the pharmaceutical preparation, has a direct influence on the homogeneity and stability of solid dosage forms. With the official release of Guidance for Industry PAT, online process analysis techniques have been more and more reported in the applications in blending process, but the research on endpoint detection algorithm is still in the initial stage. By progressively increasing the window size of moving block standard deviation (MBSD), a novel endpoint detection algorithm was proposed to extend the plain MBSD from off-line scenario to online scenario and used to determine the endpoint in the blending process of Chinese medicine dispensing granules. By online learning of window size tuning, the status changes of the materials in blending process were reflected in the calculation of standard deviation in a real-time manner. The proposed method was separately tested in the blending processes of dextrin and three other extracts of traditional Chinese medicine. All of the results have shown that as compared with traditional MBSD method, the window size changes according to the proposed MBSD method (progressively increasing the window size) could more clearly reflect the status changes of the materials in blending process, so it is suitable for online application.

[Feature Selection and Interpretation in Infrared Quantitative Models of Liquiritin and Glycyrrhizin in Radix Glycyrrhizae].

Feature selection can improve the interpretation of the modeling variables to a certain extent by selecting variables from the complex spectra backgrounds. However, the improvement of models interpretation does not mean that the modeling variables have the exact physical or chemical significance. In this paper, We explore the relation between the chemical characteristics of target components and the spectrum variables selected with 3 kinds of variables selection methods which are moving window partial least squares regression (mwPLS), synergy interval partial least squares regression (siPLS) and competitive adaptive re-weighted sampling (CARS), and compare the interpretation difference of the variables selected with the above variables selection methods. The results show that the variables selected with mwPLS accord with ν(φ)C=C of liquiritin and δCH3 or δCH2 of glycyrrhizin, which are the obvious spectra differences between the flavonoids and saponins in Radix Glycyrrhizae, and the variables selected with siPLS are the characteristic intervals combinations of the flavonoids or saponins in Radix Glycyrrhizae, which is the combination of ν(ø)C=C, ν(ø)C-O, ν(ø)C-H of flavonoids or the combination of νC-O vC-H, νO-H of saponins while the variables selected with CARS can better accord with most of the characteristic peaks from 1000 to 4000 cm(-1) of liquiritin or glycyrrhizin in Radix Glycyrrhizae, and the predict performance of the infrared quantitative model established on the spectroscopic variables selected with CARS can be improved. Therefore, most of the variables selected with CARS can be interpreted by the characteristic peaks in the infrared characteristic region of the target components, which is beneficial to improve the interpretation of the quantitative model.

[Effect of algorithms for calibration set selection on quantitatively determining asiaticoside content in Centella total glucosides by near infrared spectroscopy].

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.

Comprehensive identifying flavonoids in Citri Reticulatae Pericarpium using a novel strategy based on precursor ions locked and targeted MS/MS analysis.

Citri Reticulatae Pericarpium is a traditional Chinese medicine with extremely high health benefits as well as clinical value. In vivo and in vitro tests have proved that its main active secondary metabolites are flavonoids. However, they have not been comprehensively analyzed up to now mainly due to lack of suitable analysis method. To solve this problem, a novel strategy based on precursor ions locked and targeted MS/MS analysis was proposed. Firstly, the database of the flavonoids previously isolated from Citri Reticulatae Pericarpium was established to obtain the characteristics of their precursor ions. Secondly, after performing the full MS scan of the extract, all compounds in the total ion chromatogram were extracted by Compound Discoverer software. Thirdly, the precursor ions of the flavonoids were locked from the extracted compounds according to their characteristics, forming a precursor ions list. Finally, the precursor ions in the constructed list were performed targeted MS/MS analysis for structures characterization. As a result, total 187 flavonoids were successfully identified, and except for flavones, flavonols as well as dihydroflavones, some chalcones were also characterized from Citri Reticulatae Pericarpium for the first time.

[Rapid identification of wolfberry fruit of different geographic regions with sample surface near infrared spectra combined with multi-class SVM].

Portable near infrared spectrometer combined with multi-class support vector machines was used to discriminate wolfberry fruit of different geographic regions. Data pre-processing methods were explored before modeling with the identification rate as indicator. To eliminate the influence of sample subset partitioning on model performance, multiple modeling and predicting were conducted and the statistical result of identification rate was utilized to assess model performance of different acquisition sites. The results showed that SVM model with raw spectra after pretreatment of second derivative and Savitzky-Golay filter smoothing showed the best predicative ability. And the model of every acquisition site except for site 5 exhibited good stability and prediction ability and its median and average of identification rate of external validation were all greater than 97%. It was suggested that surface NIR spectra of wolfberry fruit was applicable to accurate identification of geographic region, and portable near infrared spectrometer could act as an effective means of monitoring the quality of Chinese herbal medicine in circulation.

[Establishment of design space for production process of traditional Chinese medicine preparation].

The philosophy of quality by design (QbD) is now leading the changes in the drug manufacturing mode from the conventional test-based approach to the science and risk based approach focusing on the detailed research and understanding of the production process. Along with the constant deepening of the understanding of the manufacturing process, the design space will be determined, and the emphasis of quality control will be shifted from the quality standards to the design space. Therefore, the establishment of the design space is core step in the implementation of QbD, and it is of great importance to study the methods for building the design space. This essay proposes the concept of design space for the production process of traditional Chinese medicine (TCM) preparations, gives a systematic introduction of the concept of the design space, analyzes the feasibility and significance to build the design space in the production process of traditional Chinese medicine preparations, and proposes study approaches on the basis of examples that comply with the characteristics of traditional Chinese medicine preparations, as well as future study orientations.

A quality-comprehensive-evaluation-index-based model for evaluating traditional Chinese medicine quality.

BACKGROUND: Evaluating traditional Chinese medicine (TCM) quality is a powerful method to ensure TCM safety. TCM quality evaluation methods primarily include characterization evaluations and separate physical, chemical, and biological evaluations; however, these approaches have limitations. Nevertheless, researchers have recently integrated evaluation methods, advancing the emergence of frontier research tools, such as TCM quality markers (Q-markers). These studies are largely based on biological activity, with weak correlations between the quality indices and quality. However, these TCM quality indices focus on the individual efficacies of single bioactive components and, therefore, do not accurately represent the TCM quality. Conventionally, provenance, place of origin, preparation, and processing are the key attributes influencing TCM quality. In this study, we identified TCM-attribute-based quality indices and developed a comprehensive multiweighted multi-index-based TCM quality composite evaluation index (QCEI) for grading TCM quality. METHODS: The area of origin, number of growth years, and harvest season are considered key TCM quality attributes. In this study, licorice was the model TCM to investigate the quality indicators associated with key factors that are considered to influence TCM quality using multivariate statistical analysis, identify biological-evaluation-based pharmacological activity indicators by network pharmacology, establish real quality indicators, and develop a QCEI-based model for grading TCM quality using a machine learning model. Finally, to determine whether different licorice quality grades differently reduced the inflammatory response, TNF-α and IL-1ß levels were measured in RAW 264.7 cells using ELISA analysis. RESULTS: The 21 quality indices are suitable candidates for establishing a method for grading licorice quality. A computer model was established using SVM analysis to predict the TCM quality composite evaluation index (TCM QCEI). The tenfold cross validation accuracy was 90.26%. Licorice diameter; total flavonoid content; similarities of HPLC chromatogram fingerprints recorded at 250 and 330 nm; contents of liquiritin apioside, liquiritin, glycyrrhizic acid, and liquiritigenin; and pharmacological activity quality index were identified as the key indices for constructing the model for evaluating licorice quality and determining which model contribution rates were proportionally weighted in the model. The ELISA analysis results preliminarily suggest that the inflammatory responses were likely better reduced by premium-grade than by first-class licorice. CONCLUSIONS: In the present study, traditional sensory characterization and modern standardized processes based on production process and pharmacological efficacy evaluation were integrated for use in the assessment of TCM quality. Multidimensional quality evaluation indices were integrated with a machine learning model to identify key quality indices and their corresponding weight coefficients, to establish a multiweighted multi-index and comprehensive quality index, and to construct a QCEI-based model for grading TCM quality. Our results could facilitate and guide the development of TCM quality control research.

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data.

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr). Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared. Results and Discussion: Gas chromatography-ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Highly selective monitoring of in-source fragmentation sapogenin product ions in positive mode enabling group-target ginsenosides profiling and simultaneous identification of seven Panax herbal medicines.

In-source fragmentation of ginsenosides in the positive ESI mode (pISF-G) frequently occurs, which results in little fragment information useful for the structural elucidation. We are aimed to unveil the genesic mechanism and explore its potential significance in quality control of Ginseng and the related compound formulae. By applying six high-resolution mass spectrometers from Agilent, Waters, and Thermo Fisher, we could primarily demonstrate the susceptibility of pISF-G. The ion clusters in the positive full-scan MS1 spectra were generated from the protonated sapogenins by successive elimination of H2O, and showed specificity for ginsenoside classification. Selective ion monitoring (SIM) of the sapogenin product ions could delineate group-target ginsenoside profiles from Ginseng. A high-selectivity characteristic chromatogram (CC) was elaborated for Ginseng, on the Vion™ IMS-QTOF mass spectrometer by IM (ion mobility) separation and quadrupole filtering of four sapogenin fragments (m/z 407.37/CCS 206.24 Å2; m/z 423.36/CCS 211.26 Å2; m/z 439.36/CCS 209.60 Å2; m/z 457.37/CCS 217.81 Å2). Chemometric analysis, based on the CC data of seven Ginseng drugs (P. ginseng, P. quinquefolius, P. notoginseng, Red ginseng, leaf of P. ginseng, P. japonicus, and P. japonicus var. major), disclosed 35 marker compounds. We could readily discriminate among P. ginseng, P. quinquefolius, and P. notoginseng, in 15 different compound formulae by identifying these marker compounds on both the Vion IMS-QTOF and QTrap 4500 mass spectrometers. Conclusively, SIM of the pISF-G sapogenin product ions renders a new concept of CC enabling the group-target profiling of ginsenosides and authentication of Ginseng and the related compound formulae.

Rapid screening and identification of target constituents using full scan-parent ions list-dynamic exclusion acquisition coupled to diagnostic product ions analysis on a hybrid LTQ-Orbitrap mass spectrometer.

A highly sensitive and effective strategy for rapid screening and identification of target constituents has been developed using full scan-parent ions list-dynamic exclusion (FS-PIL-DE) acquisition coupled to diagnostic product ions (DPIs) analysis on a hybrid LTQ-Orbitrap mass spectrometer. The FS-PIL-DE was adopted as a survey scan to trigger the MS/MS acquisition of all the predictable constituents contained in traditional Chinese medicines. Additionally, DPIs analysis can provide a criterion to judge the target constituents detected into certain chemical families. Results from analyzing polymethoxylated flavonoids (PMFs) in the leaves of Citrus reticulata Blanco demonstrated that FS-PIL-DE was capable of targeting a greater number of constituents than FS, FS-PIL and FS-DE, thereby increasing the coverage of constituent screening. As a result, 135 PMFs including 81 polymethoxyflavones, 54 polymethoxyflavanones or polymethoxychalcones were identified preliminarily. And this was the first time to systematically report the presence of PMFs in the leaves of Citrus reticulata Blanco, especially for polymethoxylated flavanones and chalcones, most of which were new compounds. The results indicated that the developed FS-PIL-DE coupled to DPIs analysis methodology could be employed as a rapid, effective technique to screen and identify target constituents from TCMs extracts and other organic matter mixtures whose compounds contained can also be classified into families based on the common carbon skeletons.