The quality evaluation of traditional Chinese medicine is one of the key issues related to the modernization of traditional Chinese medicine. The quality evaluation technology system of traditional Chinese medicine mainly includes traditional evaluation (traits, microscopic and physicochemical identification), chemical evaluation and biological evaluation. Due to the complex composition of traditional Chinese medicine, the single detection method in the above evaluation technology system usually cannot obtain sufficient quality information. The multi-source information fusion strategy can organically integrate data from multiple analysis and detection technologies to obtain more comprehensive information of samples and improve the quality evaluation effect. At present, multi-source information fusion strategy has been widely used in the fields of military, industrial and food, and it is still in its infancy in the field of quality evaluation of traditional Chinese medicine. This research introduces the definition, structure, method (algorithm) and fusion level of multi-source information fusion, summarizes its research progress in the origin traceability, variety identification and pharmaceutical analysis of traditional Chinese medicine, and sorts out the specific methods of data fusion in each literature. Finally, we summarized, prospected and discussed the application, development and existing problems of information fusion technology and its application in the quality evaluation of traditional Chinese medicine, in order to provide reference for broadening the application of this technology in the field of traditional Chinese medicine.

There is still a serious challenge of the measurement of critical quality attributes (CQAs) related to clinical efficacy for Chinese materia medica manufacturing. To overcome this challenge, an integrated strategy of biosensor and ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) was proposed using Tongren niuhuang qingxin pills as a trial. Firstly, an original biosensor was created using a semiconductor chip material high electron mobility transistor (HEMT) as the transducer and the macrophage migration inhibitory factor (MIF) as the identification element. By this MIF-HEMT biosensor, the efficacy on stoke of different components from Tongren niuhuang qingxin pills was measured. It was clear that all three components of Tongren niuhuang qingxin pills had strong therapeutic effects on stroke, especially the section A, the K_D of which reached to 8.722×10^-10 g·mL^-1. Furthermore, MIF-HEMT biosensor integrated UPLC-MS/MS was introduced to identify the efficacy CQAs of different components of Tongren niuhuang qingxin pills. As a result, 19 potential CQAs, such as albiforin, paeoniflorin, and prim-O-glucosylcimifugin, were measured as the efficacy CQAs of Tongren niuhuang qingxin pills on stroke treatment by MIF. These results provided vital measurement techniques and methodological guidance for the CQAs study of Tongren niuhuang qingxin pills intervention in MIF-induced stroke treatment. This also provided an essential guideline for the efficient utilization and quality control measurement of high-quality classical recipes.

An UPLC-Q-TOF/MS^E technology coupled with UNIFI database was used to develop a rapid, high coverage, accurate and efficient chemical composition qualitative method for Xuezhikang Capsule. A UNIFI database was established utilizing compound name, formula, structure, following automatic matching with high-resolution mass numbers, isotope distributions, mass deviations, fragment ion matching, and chromatographic retention features in UNIFI database to achieve the qualitative results of natural products in Xuezhikang Capsules. Combined with manual confirmation, 82 chemical components were identified in Xuezhikang Capsules, and the MS² fragmentation pathway of typical organic acids, flavonoids, monacrines, and monascus were analyzed to ensure accuracy of the LC-MS workflow. This study clarified the chemical substance basis of Xuezhikang Capsules by LC-MS technology, providing experimental data support for the identification of key quality attributes, quality control and consistency evaluation in the manufacturing process of Xuezhikang Capsules.

This study primarily concentrated on scientific problems of poor taste caused by unclear critical quality attributes of oral preparations manufactured by Chinese materia medica, successfully established an identification method for taste critical quality attribute and a taste improvement method combining electronic tongue with human senses, and determined the optimal taste formula, to improve patients' oral medication compliance. The study received ethical approval from the Review Committee of the Beijing University of Chinese Medicine. The results showed that the proportion of bitterness of Xiaoer Qingrening Granule was 61.8%, and its bitterness grade was 3.70, it was determined that bitterness is the critical quality attribute that caused the poor taste of Xiaoer Qingrening Granule. Additionally, the optimal taste formula per milliliter of Xiaoer Qingrening sugar-free intermediate was determined with allowable daily intake, solubility, and sweetness as the limiting conditions, which was 40 mg hydroxypropyl β-cyclodextrin, 180 mg trehalose, and 1.5 mg acesulfame potassium. Compared with the Xiaoer Qingrening Granule, the sensory evaluation score of the optimal taste formula was increased by 37.5 points. In conclusion, this study achieved the taste improvement of Xiaoer Qingrening Granule and formed a set of taste improvement strategies including the identification of taste critical quality attribute, the selection of the type and dosage of corrigent, and the optimization of taste formula, which provided a thought reference for the taste improvement of other oral preparations and a new perspective for quality control of intelligent manufacturing of traditional Chinese medicines.

To identify the bitter compounds of real-world Xiaoer Ganmao Oral Liquid sugar-free intermediates, an integrated strategy has been developed by using ultra-high performance liquid chromatography with linear ion trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MSⁿ) method and BitterX database prediction. The chromatographic operating conditions were as follows, chromatographic column: Acquity UPLC BEH C₁₈ (100 mm × 2.1 mm, 1.7 μm), mobile phase: 0.1% formic acid-water solution (A)-acetonitrile (B) with gradient elution. The data were collected in positive and negative ion modes, respectively. The accurate molecular mass and structural information of the target compounds were obtained based on quasi-molecular ions and fragmentation ions provided by high-resolution mass spectrometry. The compounds were identified by combining retention time, reference substances, reports, and other relevant data, and a total of 57 constituents including flavonoids, alkaloids, and phenylpropanoids were finally identified. Further, the BitterX database was used to predict binding probability of compounds to bitter receptors and identify potential bitter critical quality attributes, finally 33 potential bitter compounds, including kukoamine A and linarin, were predicted. This study comprehensively characterized the material basis of Xiaoer Ganmao Oral Liquid sugar-free intermediates, it provides an effective method for bitter compound screening and a reference for further improving the undesirable taste of Xiaoer Ganmao Oral Liquid.

Aiming at the hysteresis and destructiveness of off-line static detection of critical quality attribute of the moisture content of the raw material unit of the traditional Chinese medicine manufacturing process, honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis were used as the research carriers, and the drying method was used to measure the moisture content as a reference value. The moving stage was used to simulate the movement process of samples on the conveyor belt in the actual on-site production process, and near-infrared (NIR) spectra were collected, combined with machine learning, to establish NIR on-site dynamic detection model of moisture content in multi-variety honey-processed Chinese herbal slice. The results show that the second derivative method is used to preprocess the spectrum. The number of decision trees (ntree), the number of random features (max feature), and the minimum number of samples for generating leaf nodes (node size) are selected: 46, 76, and 8, respectively. The quantitative analysis model of moisture content has the best effect. The prediction coefficient of determination (the prediction coefficient of determination, R_pre²) and the root mean square error of prediction (root mean square error of prediction, RMSEP) of the model were 0.903 2 and 0.330 2, respectively. The NIR quantitative model for the moisture content of multi-variety honey-processed Chinese herbal slice established in this study has good predictive performance, and can achieve rapid, accurate and non-destructive quantitative analysis of the moisture content of honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis at the same time, and provides a method for determining the moisture content of honey-processed Chinese herbal slice of the raw material unit of the traditional Chinese medicine manufacturing process.

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

A simulating method for dripping process of Ginkgo biloba leaf dripping pills based on computational fluid dynamics was constructed. Ginkgo biloba leaf dripping pills was explored as the experimental subject to simulate the dripping process based on FLOW-3D software. The dripping process was simulated through the derivation of the governing equations, the selection of the models, and simulation parameters. Firstly, the droplet morphologies and drop speeds under different liquid viscosity were simulated. It was found that with the increase of the liquid viscosity, the drop speed decreased and the difficulty of droplet preparation gradually increased. The simulation results were consistent with the experiment results. Secondly, the droplet morphologies at different drop speeds were investigated and verified by experiments. It was found that the simulation results had a good correlation with the experiment results. The results shown that the viscosity of the liquid was the critical material attribute, and the drop speed was the critical process parameter, according to the droplet morphology. The establishment of the simulation method can deepen the understanding of the dripping process and provide a reference for the selection of raw materials and process parameters.

At present, the digitalization and intelligence level of dripping pills production process is low, and there is a lack of process monitoring methods, which makes it difficult to effectively control the quality of dripping pills. Therefore, this paper proposed an online monitoring method for the dripping process of dripping pills based on laser detection technology and multivariate data analysis (MVDA) technology. Firstly, the width data of the falling droplets during the dripping process of the dripping pills were collected by the laser detector at a high frequency. Secondly, based on the width data, the nodes were selected for each droplet and the features were extracted. Then, the principal component analysis (PCA) model was established based on the feature dataset under normal process conditions, and Hotelling's T² or DModX statistic was selected to determine whether the droplets in the dripping process were abnormal, and the abnormalities were classified and diagnosed by the principal component score map combined with K-nearest neighbor (KNN) algorithm. In this study, the feasibility of this method was investigated by taking the dripping process of Ginkgo biloba leaf dripping pills as an example. The results showed that the obtained model has good detection and diagnosis ability for abnormal valve opening, abnormal liquid temperature, and abnormal liquid volume. This method can provide some reference for the industrial production of dripping pills.

This study focuses on the microbial quality control of the Chinese herbal decoction pieces. In view of the shortcomings of traditional culture methods such as slow detection speed and inability to detect unculturable microorganisms, a new method based on ATP bioluminescence technology combined with statistical analysis methods was established to rapidly predict and quantitatively detect the total aerobic microbial count (TAMC) and total yeast and mold count (TYMC) contaminated Bupleurum chinense DC. decoction pieces. Based on the optimized ATP bioluminesence detection system, accurate detection of pure bacterial solution of Escherichia coli, Bacillus subtilis and Staphylococcus aureus can be achieved, with detection limits of 47.86, 89.13 and 1 862.09 CFU·mL^-1, respectively. The detection time was 6.5 h, and the detection cost was as low as 2 yuan/time. The upper and lower warning limits of TAMC were determined by the misjudgment rates of 10% and 20%, respectively. And the warning limit of TYMC was determined by the misjudgment rate of 20%. The proposed crossing method could quickly predict the amount of microbial contamination in Bupleurum chinense DC. decoction pieces. The constructed partial least squares regression (PLSR) model could accurately quantify the quantity of microbial contamination in Bupleurum chinense DC. decoction pieces. The optimal PLSR prediction model for TAMC had a correction coefficient (R²) of 0.826, a root mean square error of correction set (RMSE_E) of 0.468 and a root mean square error of cross-validation set (RMSE_CV) of 0.465. The R², RMSE_E and RMSE_CV in the prediction model of TYMC were 0.778, 0.543 and 0.541, respectively. The aim of this study is to establish a kind of rapid detection method and prediction models for the microbial limit of traditional Chinese medicine and Chinese herbal decoction pieces, and to provide a more convenient and sensitive detection technology for the microbial quality process control of traditional Chinese medicine products.