As a bulk medicinal material and food spice, the quality of Angelicae Dahuricae Radix depends strongly on its producing area. Traditional origin identification relies on subjective judgment, while modern analytical techniques are costly and complex. Therefore, we aimed to develop a rapid, accurate method to identify the origin of Angelicae Dahuricae Radix and to predict the contents of its index components using multi-source information fusion. We integrated multi-dimensional bionic sensory (BS) technologies - electronic eye (EE), electronic nose (EN), electronic tongue (ET), and near-infrared spectroscopy (NIR) - to capture visual, olfactory, gustatory, and spectral information from 81 batches of Angelica dahurica samples collected from four major producing areas in China (Sichuan, Anhui, Henan, Hebei). We fused these multi-level data using multi-source information fusion (MIF). For qualitative origin discrimination, we built models on single-source and fused data using partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and convolutional neural network (CNN). For quantitative prediction of index components (Bergapten, Oxypeucedanin, Imperatorin, Phellopterin, Isoimperatorin), we constructed back-propagation neural network (BPNN) models. The results demonstrated that, within the MIF framework, the qualitative discriminant model achieved a 100% positive classification rate, markedly outperforming models based on single information sources. Among the quantitative prediction models, the ET-based Phellopterin model exhibited the highest predictive accuracy (Rp(Nan et al., 20262) = 0.9026). Incorporating MIF further enhanced the predictive performance of the Bergapten, Oxypeucedanin, and Imperatorin models, increasing Rp(Nan et al., 20262) values by 28.15%, 10.20%, and 28.63%, respectively. In conclusion, this study established a novel approach for origin traceability and quality evaluation of Angelica dahurica by integrating multi-dimensional BS data with NIR. The proposed method offers rapid and high-precision analysis, providing a robust technical framework and valuable reference for developing quality control and evaluation systems for Chinese medicinal materials and their decoction pieces.

Objective This study aimed to determine pre-processing parameters for Armeniacae Semen Amarum (ASA), specifically the optimal particle size and appropriate packaging conditions, in order to support intelligent dispensing and ensure product quality. Method The physical properties of 10 batches of raw, blanched, and fried ASA, including moisture content, hardness, relative density, and extract yield, were evaluated. A validated HPLC method was used to determine the contents of amygdalin and prunasin in water decoctions prepared from whole seeds and from five crushed particle size fractions (< 0.85, 0.85–2, 2–4, 4–5, and 5–6 mm). Accelerated stability testing was conducted according to Chinese Pharmacopoeia guidelines at 40 °C ± 2 °C and 75% ± 5% relative humidity for 30 days. Paper bags, polyethylene bags, and aluminum foil bags were evaluated under both vacuum and non-vacuum conditions. Key stability indicators—moisture content, fatty oil content, acid value, and amygdalin content—were monitored throughout the storage period. Result Processing significantly altered the physical properties of ASA ( P ≤ 0.001). The contents of amygdalin and prunasin in water decoctions prepared from crushed samples of all three processed ASA products were significantly higher than those from whole seeds ( P < 0.001). The recommended optimal particle size ranges were 4–5 mm for raw ASA, 2–4 mm for blanched ASA, and 0.85–2 mm for fried ASA. In the stability study, samples packaged in paper bags showed marked deterioration: after 30 days, moisture content increased to 9.03%, the acid value rose to 2.84, and amygdalin content decreased by 15.41%. In contrast, polyethylene and aluminum foil maintained all stability indicators within acceptable ranges, with no significant benefit from vacuum treatment. Conclusion Processing altered the physical properties of ASA, and crushing enhanced the dissolution of its active components. Optimal particle size ranges and suitable packaging materials for processed ASA were identified.

Maxing Qinlong Mixture (MXQLM) is a widely used hospital formulation for treating chronic bronchitis (CB). Current quality standards rely only on thin-layer chromatography (TLC) for qualitative identification, which cannot rapidly or accurately monitor the preparation's core active ingredients. To address this gap, we developed a fast, accurate quality-control workflow for MXQLM. We performed chemical separation and component analysis of 127 batches of MXQLM using liquid chromatography-mass spectrometry (LC-MS). Network pharmacology identified five potential active compounds, including baicalein. We quantified two key active ingredients and five representative components by high-performance liquid chromatography (HPLC) and used the CRITIC objective weighting method to assign quality grades to MXQLM batches. Next, we applied bionic sensory technologies - Electronic Tongue (ET), Electronic Nose (EN), and Electronic Eye (EE) - to capture sensor response profiles reflecting the preparation's chemical composition. After multivariate feature extraction, we built qualitative and quantitative prediction models for MXQLM quality using mathematical separation techniques. The qualitative model for MXQLM quality grading (ET + EN; PCA-DA) achieved 89.76% accuracy, and the sample-type classification model (ET + EN; BPNN(Training Set: Independent Test Set =7:3)) reached 100% accuracy. For quantitative prediction, the support vector regression (SVR) model for baicalein content yielded R² = 0.8772 with RMSE = 0.0018 on the validation set (ET & EN & EE), while the SVR model for wogonin content gave R² = 0.8650 with RMSE = 0.0011 on the validation set (ET & EE). Compared with traditional TLC, the method developed here markedly reduces detection time, improves MXQLM quality control, and provides a reliable technical reference for rapid evaluation of this preparation.

Amomi fructus (AF) has been used for both medicinal and food purposes for centuries. However, issues such as source mixing, substandard quality, and product adulteration often affect its efficacy. This study used E-nose (EN) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to determine and analyze the volatile organic compounds (VOCs) in AF and its counterfeit products. A total of 111 VOCs were detected by HS-GC-IMS, with 101 tentatively identified. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified 47 VOCs as differential markers for distinguishing authentic AF from counterfeits (VIP value >1 and P < 0.05). Based on the E-nose sensor response value and the peak volumes of the 111 VOCs, the unguided Principal Component Analysis (PCA), guided Principal Component Analysis-Discriminant Analysis (PCA-DA), and Partial Least Squares-Discriminant Analysis (PLS-DA) models were established to differentiate AF by authenticity, origin, and provenance. The authenticity identification model achieved 100.00% accuracy after PCA analysis, while the origin identification model and the provenance identification model were 95.65% (HS-GC-IMS: PLS-DA) and 98.18% (HS-GC-IMS: PCA-DA/PLS-DA), respectively. Further data-level fusion of E-nose and HS-GC-IMS significantly improved the accuracy of the origin identification model to 97.96% (PLS-DA), outperforming single-source data modeling. In conclusion, the intelligent data fusion algorithm based on E-nose and HS-GC-IMS data effectively identifies the authenticity, origin, and provenance of AF, providing a rapid and accurate method for quality evaluation.

This study investigates the consistency of quality between the traditional decoction (TD) of Pheretima aspergillum and its dispensing granule decoction (DGD) by examining their chemical composition and antithrombotic efficacy. We first determined the nucleoside components, protein content, and volatile compounds in 10 batches of TD and 9 batches of DGD sourced from three manufacturers using high-performance liquid chromatography (HPLC), UV-vis spectrophotometry, and gas chromatography ion mobility spectrometry (GC-IMS). Next, we assessed antithrombotic efficacy by measuring the relative length of tail thrombosis and calculating the thrombus inhibition rates at 24 and 48 h. Our comprehensive analysis revealed that the concentrations of hypoxanthine, uridine, inosine, adenosine, and proteins in TD were significantly higher than those in DGD. Moreover, the types and concentrations of volatile compounds in TD and DGD exhibited substantial differences, with 19 volatile compounds identified as differentially present between the two decoctions. Despite these compositional differences, both decoction types demonstrated equivalent antithrombotic efficacy. Overall, the quality of DGD from manufacturers A and B aligned closely with that of TD. However, TD exhibited a higher overall quality compared to DGD, while both decoctions maintained consistent antithrombotic efficacy.

Salvia miltiorrhiza: is a widely used Chinese medicinal herb whose quality is significantly influenced by geographical origin. Establishing reliable methods for origin identification is therefore crucial for quality assurance. In this study, 67 batches of Salvia miltiorrhiza samples from Shandong, Shanxi, Henan, and Sichuan provinces were analyzed using near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric techniques. Six preprocessing methods were applied to optimize spectral data, and PLS-DA models were constructed based on the optimized results. To further improve model performance, uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random forest (RF) were employed for variable selection. Discriminant models were then established using NIR, MIR, and fused (NIR + MIR) data, with performance evaluated by accuracy. Results showed that in NIR, the 2nd-RF-PLS-DA model achieved the best performance with 96.72% accuracy, while in MIR, the SG-UVE-PLS-DA model reached 98.33% accuracy. After integrating NIR and MIR data, the 2nd-UVE-PLS-DA model achieved 100% accuracy, demonstrating the strongest discriminative capability. These findings demonstrate that combining NIR and MIR spectroscopy with appropriate preprocessing and variable selection strategies fully exploits complementary spectral information, enabling the construction of rapid, reliable, and efficient discriminant models. This approach provides an effective tool for origin tracing of Salvia miltiorrhiza and serves as a methodological reference for advancing quality evaluation of other Chinese herbal medicines.

Angelica dahurica (AD) is both a medicinal and edible plant. Fresh AD materials are commonly sulfur-fumigated to prevent decay, insects, and mold. However, detecting sulfur-fumigation post-treatment remains challenging. This study innovatively developed a rapid detection method leveraging multi-dimensional bionic sensory information. By analyzing data from electronic eyes, electronic noses, electronic tongues, and HPLC of 84 samples (25 non-fumigated and 59 sulfur-fumigated), qualitative models including PLS-DA, LS-SVM, and CNN, as well as quantitative models BP-NN and RF, were established. The research achieved groundbreaking results: the leave-one-out cross-validation accuracy of qualitative models reached 100 %. In quantitative models, the coefficient of determination (R2) for sulfur dioxide residue prediction was 0.9967 (EE+ET:RF), and 0.8022 for the total content prediction of marker components (EE+ET:BP-NN). Based on these techniques, a quality identification system for sulfur-fumigated AD was constructed, effectively achieving the identification of sulfur-fumigated AD. This study also provides valuable references for the rapid quality assessment of sulfur-fumigated medicinal materials and foods.

Objective:To develop a classification model for the five flavors of Chinese medicine using advanced multi-source intelligent sensory information fusion technology. The primary aim is to investigate the feasibility of applying this model to classify and identify the flavors of various Chinese medicines effectively. Methods:We selected 122 representative Chinese medicines, each exhibiting a single distinct flavor (sour, pungent, salty, sweet, bitter), along with 14 common foods. Utilizing the nature and flavors of these decoction pieces specified in Chinese Pharmacopeia (ChP)2020 and the inherent attributes of food components, we obtained valuable data from various sensors, including the PEN3 electronic nose, ASTREE electronic tongue, and SA402B electronic tongue. We then collected single-source data matrices from these sample sensors and a multi-source data matrix that combined the data from all sensors. Using discriminant analysis (DA), principal component analysis-discriminant analysis (PCA-DA), and K-nearest neighbor algorithm (KNN) three kinds of chemometric methods were used to establish five flavors and five-category discrimination models. The results were comprehensively evaluated with the highest correct rate of the model of leave-one-out cross-validation as the index. Results:Upon leave-one-out cross-validation, the correct judgment rate of the five flavors, five-category two-source fusion DA discrimination model (83.8%; ASTREE + SA402B) was significantly higher than the correct judgment rate of the single-source optimal DA and KNN model (73.5%; ASTREE). Following full-sample modeling, the correct judgment rate of the five flavors, five-category three-source fusion DA discrimination model (94.9%; PEN3+ASTREE+SA402B) rose substantially. This was higher than the correct judgment rate of the single-source optimal DA model (77.9%; ASTREE) and slightly higher than the two-source optimal correct judgment rate (89.7%; PEN3 + ASTREE). Conclusions:Compared to single-source identification, multi-source intelligent senses information fusion (MISIF) significantly improved accuracy, providing a new outlook for identifying flavor in Chinese medicine.

IntroductionLing-Gui-Zhu-Gan Decoction (LGZGD), one of the first batches of classical Chinese prescriptions formally recognized by the Chinese government, has a long-standing history of clinical application and significant potential for modern development. However, the chemical composition and content of different types of pharmaceutical preparations are not clear.MethodsThis study aimed to develop an analytical approach integrating HPLC and UHPLC-Q-Orbitrap/MS to comprehensively characterize the chemical constituents of LGZGD across different preparation stages and to investigate its pharmacodynamic basis in the treatment of unstable angina pectoris (UA) using network pharmacology. The content and transfer rate of six index components were quantified using HPLC.ResultsA total of 75 compounds were identified via UHPLC-Q-Orbitrap/MS, comprising 24 flavonoids, 25 organic acids, nine phenylpropanoids, eight terpenoids, five saponins, and four other compounds, based on precursor ion peaks and fragment ion spectra. Notably, five compounds—(-)-pterocarpin glucoside, γ-aminobutyric acid, calycosin, trimethyl citrate, and proline-phenylalanine—were absent following the drying of the concentrate. Using the LC-MS data as a foundation, network pharmacology and molecular docking analyses were conducted to elucidate the pharmacodynamic components responsible for LGZGD’s therapeutic effects on UA. This integrative analysis identified three key active compounds—naringenin, glycyrrhizin, and calycosin—and three core targets: TNF, EGFR, and PTGS2.DiscussionThe analytical method established in this study effectively delineates the chemical profile and index component transfer dynamics of LGZGD preparation intermediates, providing essential data for the development of both liquid and solid dosage forms. The constructed “medicine-component-target-pathway-disease” network preliminarily reveals the multi-component, multi-target, and multi-pathway mechanisms by which LGZGD may exert therapeutic effects on UA. This work provides a scientific foundation for its clinical application, supporting rational drug use and formulation development.

This study investigates the consistency between the formula granule decoction of stir-fried Bombyx Batryticatus(JC-DGD) and its traditional decoction (JC-TD) counterpart, using roasted Bombyx Batryticatus as a representative example.First, used high-performance liquid chromatography (HPLC) to establish the fingerprints of JC-TD and JC-DGD from four manufacturers (A, B, C, and D) and assess their similarity. There was no significant difference between the two (P > 0.05).Quantified five key index components, including uracil.Compared the protein and crude polysaccharide contents using the Coomassie Brilliant Blue and phenol-sulfuric acid methods. The mean total content of index components, proteins, and crude polysaccharides in 10 batches of JC-TD was set as 1. The contents of index components in different categories - JC-DGD-provincial standard(JC-DGD-PS), JC-DGD-enterprise standard(JC-DGD-ES), TD, DGD, and DGCM from manufacturers A, B, C, and D-were 1.23, 0.38, 1.00, 0.72, 1.28, 0.58, 0.45, and 0.27, respectively. The protein contents were 1.51, 0.46, 1.00, 0.88, 1.43, 0.93, 0.35, and 0.66, respectively. The crude polysaccharide contents was 9.01, 0.74, 1.00, 4.05, 10.57, 1.96, 0.38, and 1.77, respectively. We identified 32 differential volatile organic compounds, including fenugreek lactone, using GC-IMS. Then evaluated the anticonvulsant effects of JC-TD and JC-DGD using an acute convulsion mouse model induced by pentylenetetrazole. The results showed that JC-DGD-A > JC-TD ≈ JC-DGD-B (P > 0.05)) were superior to those of JC-DGD-C and JC-DGD-D (P < 0.01). Finally, calculate the recommended equivalent ratio of Chinese medicine formula granules (DGCM). The manufacturers A, B, C, and D were adjusted from 1:3, 1:10, 1:10, and 1:11-1:4.54 ± 0.51, 1:5.24 ± 2.22 (JC-B3 without adjustment: 1:3.6), 1:5.17 ± 0.49, and 1:3.44 ± 0.59, respectively. The overall, and the consistency between the two was analyzed through correlation analysis, cluster analysis, and multi-index dimensionality reduction discriminant analysis.Comprehensive results indicated that the quality of JC-DGD-A was significantly higher than that of JC-TD, while JC-DGD-B exhibited similar quality to JC-TD. In contrast, the quality of JC-DGD-C and JC-DGD-D was significantly lower than that of JC-TD. To enhance product quality and ensure the scientific rationality of clinical applications, manufacturers must adhere to national and provincial standards for formula granule drugs.

This study aims to evaluate differences in chemical composition and intelligent sensory information between Angelica sinensis-Ligusticum chuanxiong dispensing granule decoction (DGD) and traditional decoction (TD), providing a reference for quality control of formula granules. Twelve batches of Angelica sinensis and Ligusticum chuanxiong dispensing granules from four manufacturers (A, B, C, and D) were analyzed, alongside 12 batches of decoction pieces randomly combined into drug pairs. HPLC established the characteristic chromatograms, and differences in chemical components were evaluated by comparing chromatographic similarity, composition types, index component content, and common peak areas. The electronic nose and tongue were used to assess differences in sensory information, focusing on taste and smell. No significant differences were observed in chromatographic fingerprint similarity between DGD and TD (P > 0.05). Seven index components were identified, and, except for adenosine, the overall content of these components was higher in TD than DGD (P < 0.01). After equivalent correction using the Criteria Importance through Intercriteria Correlation (CRITIC) method, no significant differences in index component content were found (P > 0.05). Regarding sensory information, a significant difference was observed in B-bitterness 2 between TD and DGD (P ＜0.05). Differences between DGD and TD primarily due to component content, with TD generally exhibiting higher quality. After equivalent correction, DGD approaches the quality of TD. Correlations between electronic tongue data and chemical composition suggest that electronic tongue technology can rapidly distinguish these dosage forms.

Purpose:This study aims to identify the transdermal penetration components of Shufeitie ointment (SFTOT) and investigate the potential active components and mechanisms through which SFTOT exerts its effects on Chronic Obstructive Pulmonary Disease (COPD). Methods:An in vitro permeation test (IVPT) of SFTOT was conducted using a modified Franz diffusion cell method. Ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap/MS) was employed to analyze data from the transdermal receiving solution, enabling comprehensive identification of the components that permeate through the skin. To predict the potential mechanisms by which SFTOT may treat COPD, network pharmacology was used to construct a component-target-collaterals network. Additionally, molecular docking was applied to verify the interactions between the potential transdermal active components of SFTOT and the core targets. Results:Using UHPLC-Q-Orbitrap/MS, we identified 129 transdermal permeation components in SFTOT. Network pharmacology analysis revealed 222 common targets between SFTOT and COPD. The primary active components were predicted to be luteolin, kaempferol, quercetin, 7-O-methylluteolin, apigenin, ferulic acid, palmitic acid, inapinic acid, 6-shogaol, and myristic acid. These components were primarily enriched in the AGE-RAGE, TNF, PI3K-Akt, and MAPK signaling pathways. Protein-protein interaction (PPI) analysis identified TNF, ALB, AKT1, EGFR, and CASP3 as core targets. Molecular docking results showed that 72% of component-target interactions had a binding energy of < -5.0 kcal/mol, indicating strong binding activity. Among these, apigenin exhibited the lowest binding energy with EGFR and consistently lower binding energies with other core targets compared to the other components. This suggests that apigenin may play a key role in treatment. Conclusion:High-resolution liquid chromatography-mass spectrometry effectively identified the transdermal penetration components of SFTOT, providing a foundation for further screening of key active compounds. Our findings suggest that SFTOT may alleviate COPD by downregulating TNF, ALB, AKT1, EGFR, and CASP3 while inhibiting inflammatory mediator release through the AGE-RAGE, TNF, PI3K-Akt, and MAPK signaling pathways. These effects may help reduce COPD-related symptom clusters. Notably, apigenin appears to be a crucial bioactive component in the prevention and treatment of COPD.

Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.

Coptidis Rhizoma (CR) holds significant clinical importance. In this study, we conducted a comparative analysis of CR's dispensing granule decoction (DGD) and traditional decoction (TD) to establish a comprehensive evaluation method for the quality of DGD. We selected nine batches of DGD (three from each of manufacturers A, B and C) and 10 batches of decoction pieces for analysis. We determined the content of representative components using high-performance liquid chromatography and assessed the content of blood components in vivo post-administration using ultra-performance liquid chromatography-mass spectrometry. The antibacterial activity was measured using the drug-sensitive tablet method. To evaluate the overall consistency of DGD and TD, we employed the CRITIC method and Grey relational analysis method. Our CRITIC results indicated no significant difference between the CRITIC scores of DGD-B and TD, with DGD-B exhibiting the highest consistency and overall quality. However, DGD-A and DGD-C showed variations in CRITIC scores compared with TD. After equivalent correction, the quality of DGD-A and DGD-C approached that of TD. Furthermore, our Grey relational analysis results supported the findings of the CRITIC method. This study offers a novel approach to evaluate the consistency between DGD and TD, providing insights into improving the quality of DGD.

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.

OBJECTIVE:This study aims to investigate the quality consistency between traditional decoction (TD) of Amomum villosum and its dispensing granule decoction (DGD). Fifteen batches of TD and nine batches of dispensing granules (manufactured by A, B, and C) were prepared and evaluated for their consistency. METHODS:Firstly, The chemical similarity of TD and DGD was examined using GC and HPLC, coupled with hierarchical cluster analysis (HCA), criteria importance though intercrieria correlation(CRITIC) weighting method, and principal component analysis (PCA). Secondly, the gastrointestinal motility experiments in mice, along with the CRITIC weighting method, were employed to assess the bioequivalence of TD and DGD of Amomum villosum. Finally, the entropy weight technique-gray relative analysis(GRA) method was used to compare the quality of Amomum villosum decoctions. RESULTS:①The CRITIC weighting method indicated significantly higher scores for TD than DGD (p < 0.01). HCA and PCA results demonstrated a clear distinction between TD and DGD. ②Gastrointestinal motility test results revealed no significant difference between TD and DGD in other indicators (p > 0.05).③Gray relative analysis results showed that the relative correlation of TD was more significant than that of DGD. CONCLUSION:The chemical composition of DGD and TD differed. The biological activity of DGD-A/B was consistent with that of TD, while the difference between DGD-C and TD was significant. A comprehensive evaluation showed that TD exhibited better quality than DGD. DGD manufacturers should optimize the preparation process to enhance product quality.

ObjectiveThe aim of the present study was to carry out a systematic research on bitterness quantification to provide a reference for scholars and pharmaceutical developers to carry out drug taste masking research. Significance: The bitterness of medications poses a significant concern for clinicians and patients. Scientifically measuring the intensity of drug bitterness is pivotal for enhancing drug palatability and broadening their clinical utility.MethodsThe current study was carried out by conducting a systematic literature review that identified relevant papers from indexed databases. Numerous studies and research are cited and quoted in this article to summarize the features, strengths, and applicability of quantitative bitterness assessment methods.ResultsIn our research, we systematically outlined the classification and key advancements in quantitative research methods for assessing drug bitterness, including in vivo quantification techniques such as traditional human taste panel methods, as well as in vitro quantification methods such as electronic tongue analysis. It focused on the quantitative methods and difficulties of bitterness of natural drugs with complex system characteristics and their difficulties in quantification, and proposes possible future research directions.ConclusionThe quantitative methods of bitterness were summarized, which laid an important foundation for the construction of a comprehensive bitterness quantification standard system and the formulation of accurate, efficient and rich taste masking strategies.

OBJECTIVE:In our previous taste-masking study, we found that Acesulfame K (AK) had a better taste-masking effect than other high-efficiency sweeteners for several representative bitter natural drugs in aqueous decoction. Furthermore, we performed a preliminary taste-masking study of AK for representative bitter API Berberine Hydrochloride (BH) and found that it had a good taste-masking effect. We also found that flocculent precipitation was generated in the BH solution, but it was not clear whether it was related to the good taste-masking effect. This study was conducted to explore the taste-masking effect and mechanism of AK on BH.METHODS:The taste-masking effect of AK on BH was evaluated based on the Traditional Human Taste Panel Method and the electronic tongue evaluation method. DSC, XRD, and molecular simulation techniques were used to explore the mechanism of AK on BH, from the macro level and molecular level, respectively.RESULTS:When evaluating the taste-masking effect, we found that 0.1% AK had the best taste-masking effect on BH, while higher concentrations had a worse taste-masking effect. DSC and XRD revealed that the flocculent precipitation was a complex AK-BH. Finally, by simulating the binding of AK, BH, and TAS2R46 receptors, we found the unique taste-masking mechanism of AK.CONCLUSION:The sweet taste stimulus of AK can mask the bitter taste stimulus of BH, and AK can generate AK-BH with BH to reduce the contact between BH and bitter taste receptors. Additionally, it could block the expression of the TAS2R46 receptors.

目前,中药饮片临床不合理使用现象较普遍,其主要因素是缺乏规范中药饮片临床应用的指导性文件,制定一套科学、能体现中医药特点的中药饮片临床应用规范势在必行.由北京中医药大学东方医院、河南中医药大学第一附属医院牵头,联合全国 28 家中医医疗机构共同起草的《中药饮片临床应用规范》,已于 2021 年 6 月由中华中医药学会作为团体标准(T/CACM1362-2021)发布.该文对该团体标准进行详细解读,以期规范中药饮片处方开具,促进中药饮片临床应用的安全性与有效性,提高中药饮片临床应用水平.