ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Capsules , Chromatography, High Pressure Liquid , Drugs, Chinese Herbal , Medicine, Chinese Traditional , Quality ControlABSTRACT
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.
Subject(s)
Drugs, Chinese Herbal , Ginkgo biloba , Medicine, Chinese Traditional , Plant Extracts , Plant Leaves , Powders , Quality ControlABSTRACT
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.
Subject(s)
Drugs, Chinese Herbal , Medicine, Chinese Traditional , Quality ControlABSTRACT
Objective To develop a multivariate statistical process control (MSPC) model for in-line monitoring of the extraction process of Lonicerae Japonicae Flos (LJF). Methods The spectral data collected by near infrared spectrascopy coupled with MSPC technique were applied to establish the statistical model. Three kinds of multivariate control charts (PC scores, Hottelling T2 and DModX) were used to monitor the abnormal variations caused by the change of starting material quality attributes and abnormal operation conditions. Moreover, the extraction process trajectory was developed by applying principal component analysis on the process spectra, reflecting the changing trend with extraction time. Results The MSPC model with good repeatability and robustness could show the quality variation of extraction process of LJF and accurately predict the normal batches. The joint use of three control charts could effectively identify the abnormal conditions. Compared with the traditional monitoring methods, this approach was fast and nondestructive, and can implemente the in-line and real-time monitoring. Conclusion The near infrared spectroscopy combined with MSPC can be successfully applied to the extraction processes of LJF, which is of great significance for the improvement of the quality control of Chinese material medica (CMM).
ABSTRACT
This study was aimed to develop in-line monitoring of extraction process of Lonicerajaponica in the extraction process trajectory of Re-Du-Ning (RDN) injection.Several batches of near-infrared (NIR) spectrum dates were collected and multivariate statistical process control (MSPC) method was in conjunction with.The results showed that using score,Hotelling T2 and DModX control chart,various normal and abnormal behaviors of the test batches were detected in time by comparison with the extraction process trajectory.It was concluded that the process trajectory for in-line quality control based on NIR spectrum dates and MSPC could indicate the changes of process.It was a feasible technology tool of the total process quality control during traditional Chinese medicine (TCM) manufacturing process.
ABSTRACT
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.