Research on monitoring method of dripping pills dripping process based on laser detection and multivariate data analysis technology / 药学学报

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.

Study on robust model construction method of multi-batch fruit online sorting by near-infrared spectroscopy.

In the online detection of fruit samples by near-infrared spectroscopy (NIRS), the natural change of sample states or the variations of instruments will often cause a large error in predicting different batches of samples. In this study, a total of 440 tomato samples were collected in four batches with each batch of 110 samples. The Spectral and soluble solids content (SSC) of single batch were collected every other day in batch order. The multivariate statistical process control (MSPC) method was adopted to establish a stability monitor model. The robustness regression (Rob-Reg) and partial least squares regression (PLSR) were used for mixed modeling of multiple batches of samples to eliminate the variability influence of sample and instrument states. The results show that MSPC can effectively monitor the consistency of the same batch samples measured at different times or different batches. The variation of sample attributes with spectral acquisition time has dramatically damaged the adaptation of PLSR models. The Rob-Reg method can predict the SSC of the different batches of samples at different collection times. Compared with the PLSR method, the correlation coefficient of prediction (Rp) was improved from 0.61 to 0.66, and the root mean square error of prediction (RMSEP) was decreased from 0.55 to 0.44 for Rob-Reg method. The RPD of 3.85 indicated that the model is excellent. The Robust modeling method can be well applied to fruit near-infrared online detection system.

A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties.

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters' prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.

[Critical quality attribute assessment of big brand traditional Chinese medicine: quality control of Tongren Niuhuang Qingxin Pills in texture and sensory attributes].

Texture sensory attributes are the key items in quality control of Chinese medicinal honeyed pills. The purpose of this study is to develop a quality control method for assessing the texture sensory attributes of Chinese medicinal honeyed pills based on real-world Tongren Niuhuang Qingxin pilular masses and finished products. First, parameters of texture profile analysis(TPA) were optimized through single factor and central composite design(CCD) experiments to establish a detection method for texture sensory attri-butes of Tongren Niuhuang Qingxin Pills. The results showed that the established detection method was stable and reliable, with the optimal parameters set up as follows: deformation percentage of 70%, detection speed at 30 mm·min~(-1), and interval time of 15 s. Furthermore, 540 data points yielded form six texture sensory attributes of pills from 30 batches were subjected to multivariate statistical process control(MSPC) with Hotelling T~2 and squared prediction error(SPE) control charts to establish the quality control method of Tongren Niuhuang Qingxin Pills. This study is expected to provide a reference for improving the quality control system of Chinese medicinal honeyed pills.

[Critical quality attribute assessment of big brand traditional Chinese medicine: quality control method of ginkgo leaves extract material based on powder physical properties].

The physical properties of ginkgo leaves extract(GLE) are the critical quality attributes for the control of the manufacturing process of ginkgo leaves preparations. In this study, 53 batches of GLE with different sources from the real world were used as the objects to carry out the research from 3 levels. First, based on micromeritics evaluation method, a total of 29 physical attribute quality parameters in five dimensions were comprehensively characterized, with a total of 1 537 data points. Further, with use of physical fingerprinting technology combined with similarity evaluation, the powder physical properties of 53 batches of GLE showed obvious differences from an overall perspective, and the similarity of the physical fingerprints was 0.876 to 1.000. Secondly, hierarchical clustering analysis(HCA) and principal component analysis(PCA) models were constructed to realize the reliable identification and differentiation of real-world materials produced by GLE from different sources. Multivariate statistical process control(MSPC) model was used to create GLE material Hotelling T~2 and squared prediction error(SPE) control charts. It was found that the SPE score of B_(21) powder exceeded the 99% confidence control limit by 22.495 9, and the SPE scores of A_1 and C_(10) powder exceeded the 95% confidence control limit by 16.099 2, realizing the determination of abnormal samples in the materials of GLE from the production in real world. Finally, the physical quality control method of GLE in the production process of ginkgo leaves preparations was established in this study, providing a reference for the quality control methods of ginkgo leaves preparations in their manufacturing process.

[Critical quality attribute assessment of big brand traditional Chinese medicine: quality control method of Huangjing Zanyu Capsules based on chemical properties of characteristic components].

The chemical properties of characteristic components are significant to the manufacturing quality control of big brand traditional Chinese medicine. In this study, the Huangjing Zanyu Capsules were used as the research carrier to determine the content of five characteristic components including icraiin, emodin, schisandrin A, 2,3,5,4'-tetrahydroxystilbene-2-O-ß-D-glucoside, and osthole simultaneously by high-performance liquid chromatography(HPLC). The results showed that the chemical properties of five cha-racteristic components had a good linear relationship(r>0.999 9) within the quantitative range; the relative standard deviations(RSD) was 0.11%-2.0% and 0.25%-2.8% respectively for intra-day and inter-day precision; the RSD of repeatability was 1.8%-2.6%; the RSD of stability within 48 hours was 0.19%-2.8%, and the average recovery rate was 95.52%-100.1%, all meeting the requirements of pharmaceutical quantitative analysis. Additionally, the interval estimation method was used to directly reflect the distribution of samples with abnormal chemical properties of characteristic components, and the results showed ten samples were detected beyound the 95% control line of confidence level. Multivariate statistical process control(MSPC) method was used to monitor the abnormal samples of Huangjing Zanyu Capsules collectively, and the results showed that two samples were beyond the 95% control line of Hotelling's T~2 and three samples beyond the 95% control line of squared prediction error(SPE), indicating consistent quality control of Huangjing Zanyu Capsules. In conclusion, the proposed method is not only accurate and efficient but also a compensation for the traditional single-component quality control method, providing a scientific basis for the quality control in manufacturing process of Huangjing Zanyu Capsules. Furthermore, it could also serve as a reference method for the quality control in manufacturing big brand traditional Chinese medicine.

A FT-NIR Process Analytical Technology Approach for Milk Renneting Control.

The study proposes a process analytical technology (PAT) approach for the control of milk coagulation through near infrared spectroscopy (NIRS), computing multivariate statistical process control (MSPC) charts, based on principal component analysis (PCA). Reconstituted skimmed milk and commercial pasteurized skimmed milk were mixed at two different ratios (60:40 and 40:60). Each mix ratio was prepared in six replicates and used for coagulation trials, monitored by fundamental rheology, as a reference method, and NIRS by inserting a probe directly in the coagulation vat and collecting spectra at two different acquisition times, i.e., 60 s or 10 s. Furthermore, three failure coagulation trials were performed, deliberately changing temperature or rennet and CaCl2 concentration. The comparison with fundamental rheology results confirmed the effectiveness of NIRS to monitor milk renneting. The reduced spectral acquisition time (10 s) showed data highly correlated (r > 0.99) to those acquired with longer acquisition time. The developed decision trees, based on PC1 scores and T2 MSPC charts, confirmed the suitability of the proposed approach for the prediction of coagulation times and for the detection of possible failures. In conclusion, the work provides a robust but simple PAT approach to assist cheesemakers in monitoring the coagulation step in real-time.

Critical quality attribute assessment of big brand traditional Chinese medicine: quality control method of Huangjing Zanyu Capsules based on chemical properties of characteristic components / 中国中药杂志

The chemical properties of characteristic components are significant to the manufacturing quality control of big brand traditional Chinese medicine. In this study, the Huangjing Zanyu Capsules were used as the research carrier to determine the content of five characteristic components including icraiin, emodin, schisandrin A, 2,3,5,4'-tetrahydroxystilbene-2-O-β-D-glucoside, and osthole simultaneously by high-performance liquid chromatography(HPLC). The results showed that the chemical properties of five cha-racteristic components had a good linear relationship(r>0.999 9) within the quantitative range; the relative standard deviations(RSD) was 0.11%-2.0% and 0.25%-2.8% respectively for intra-day and inter-day precision; the RSD of repeatability was 1.8%-2.6%; the RSD of stability within 48 hours was 0.19%-2.8%, and the average recovery rate was 95.52%-100.1%, all meeting the requirements of pharmaceutical quantitative analysis. Additionally, the interval estimation method was used to directly reflect the distribution of samples with abnormal chemical properties of characteristic components, and the results showed ten samples were detected beyound the 95% control line of confidence level. Multivariate statistical process control(MSPC) method was used to monitor the abnormal samples of Huangjing Zanyu Capsules collectively, and the results showed that two samples were beyond the 95% control line of Hotelling's T~2 and three samples beyond the 95% control line of squared prediction error(SPE), indicating consistent quality control of Huangjing Zanyu Capsules. In conclusion, the proposed method is not only accurate and efficient but also a compensation for the traditional single-component quality control method, providing a scientific basis for the quality control in manufacturing process of Huangjing Zanyu Capsules. Furthermore, it could also serve as a reference method for the quality control in manufacturing big brand traditional Chinese medicine.

Critical quality attribute assessment of big brand traditional Chinese medicine: quality control method of ginkgo leaves extract material based on powder physical properties / 中国中药杂志

The physical properties of ginkgo leaves extract(GLE) are the critical quality attributes for the control of the manufacturing process of ginkgo leaves preparations. In this study, 53 batches of GLE with different sources from the real world were used as the objects to carry out the research from 3 levels. First, based on micromeritics evaluation method, a total of 29 physical attribute quality parameters in five dimensions were comprehensively characterized, with a total of 1 537 data points. Further, with use of physical fingerprinting technology combined with similarity evaluation, the powder physical properties of 53 batches of GLE showed obvious differences from an overall perspective, and the similarity of the physical fingerprints was 0.876 to 1.000. Secondly, hierarchical clustering analysis(HCA) and principal component analysis(PCA) models were constructed to realize the reliable identification and differentiation of real-world materials produced by GLE from different sources. Multivariate statistical process control(MSPC) model was used to create GLE material Hotelling T~2 and squared prediction error(SPE) control charts. It was found that the SPE score of B_(21) powder exceeded the 99% confidence control limit by 22.495 9, and the SPE scores of A_1 and C_(10) powder exceeded the 95% confidence control limit by 16.099 2, realizing the determination of abnormal samples in the materials of GLE from the production in real world. Finally, the physical quality control method of GLE in the production process of ginkgo leaves preparations was established in this study, providing a reference for the quality control methods of ginkgo leaves preparations in their manufacturing process.

Critical quality attribute assessment of big brand traditional Chinese medicine: quality control of Tongren Niuhuang Qingxin Pills in texture and sensory attributes / 中国中药杂志

Texture sensory attributes are the key items in quality control of Chinese medicinal honeyed pills. The purpose of this study is to develop a quality control method for assessing the texture sensory attributes of Chinese medicinal honeyed pills based on real-world Tongren Niuhuang Qingxin pilular masses and finished products. First, parameters of texture profile analysis(TPA) were optimized through single factor and central composite design(CCD) experiments to establish a detection method for texture sensory attri-butes of Tongren Niuhuang Qingxin Pills. The results showed that the established detection method was stable and reliable, with the optimal parameters set up as follows: deformation percentage of 70%, detection speed at 30 mm·min~(-1), and interval time of 15 s. Furthermore, 540 data points yielded form six texture sensory attributes of pills from 30 batches were subjected to multivariate statistical process control(MSPC) with Hotelling T~2 and squared prediction error(SPE) control charts to establish the quality control method of Tongren Niuhuang Qingxin Pills. This study is expected to provide a reference for improving the quality control system of Chinese medicinal honeyed pills.

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control.

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.

[Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections.

Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy.

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

Multivariate statistical process control in product quality review assessment - A case study.

According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment.

Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry.

The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control / 中国中药杂志

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections.