Assessment of the status property(boiling time) is a challenge for the quality control of extraction process in pharmaceutical enterprises. In this study, the pilot extraction process of Phellodendron chinense was used as the research carrier to develop an online near-infrared(NIR) quality control method based on the status property(boiling time). First, the NIR spectra of P. chinense were collected during the two pilot-scale extraction processes, and the status property(boiling time) was assessed by observing the state of bubbles in the extraction tank using a transparent window during the extraction process, which was then used as a reference standard. Based on the moving block standard deviation(MBSD) algorithm, the assessment model using online NIR spectra for boiling time during extraction process was established. In addition, the model was optimized as follows: standard normal variable(SNV) for spectral pretreatment, modeling band of 800-2 200 nm, and window size of 4. The results showed that, with 0.002 0 as the MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process. Furthermore, the principal component analysis-moving block standard deviation(PCA-MBSD) model was developed by our group to reduce the influence of online NIR spectral noise and background signal on the model, and the number of principal components was optimized into 2 in the PCA-MBSD model. The results showed that, with 0.000 075 as the PCA-MBSD model threshold, the boiling time can be accurately assessed using online NIR spectra during extraction process, with improved reliability. This study can provide a assessment method for boiling time during extraction process using online NIR spectra, which can replace the empirical judgment in manual observation, and realize the digitalization of the extraction process for big brand traditional Chinese medicine.
Subject(s)Medicine, Chinese Traditional , Principal Component Analysis , Quality Control , Reproducibility of Results , Spectroscopy, Near-Infrared
The physical properties of powder and granules are the critical quality attributes for the process control of Suhuang Zhike Capsules, a big brand traditional Chinese medicine. This paper took the production of 25 batches of real-world Suhuang Zhike Capsules dry extract powder and granules intermediates as the research object. Firstly, a method for testing the physical properties of Suhuang Zhike Capsules powder and granules with 19 physical indicators was established. The results showed that the granules of dry extract powder after granulation had a smaller particle size, wider particle size distribution range and poor fluidity, which easily caused the problem of over-limit capsule loading. Secondly, correlation analysis, principal component analysis and cluster analysis were used for mathematical statistics. The correlation analysis showed that the density of dry extract powder could affect the chroma and fluidity. At the same time, the particle size in the granules had a stronger effect on the chroma and fluidity than the density. The study also found that the particle size and hygroscopicity of dry extract powder were potentially key physical properties that affected the physical properties of granules. Furthermore, the results of principal component analysis and cluster analysis showed that the consistency of the physical properties between the dry extract powder and intermediate granules was relatively poor. To this end, similarity analysis was carried out, and the quality control method of powder and granules based on physical fingerprint was established. The results showed that the physical fingerprint similarity of 25 batches of dry extract powder was 0.639-0.976, and the physical fingerprint similarity of the gra-nules was 0.716-0.983. With the similarity of 0.85 as the threshold, the batches with abnormal physical properties could be identified. In this study, the process quality control method of Suhuang Zhike Capsules based on the physical properties of powder and granules was established finally, which realized the identification of abnormal batches, and provided a reference for the process quality control of Suhuang Zhike Capsules.
Subject(s)Capsules , Drugs, Chinese Herbal , Medicine, Chinese Traditional , Powders , Quality Control
Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.
Subject(s)Calibration , Ginkgo biloba , Least-Squares Analysis , Medicine, Chinese Traditional , Plant Leaves , Quality Control , Spectroscopy, Near-Infrared , Tablets
The spatial distribution uniformity of valuable medicines is the critical quality attribute in the process control of Tongren Niuhuang Qingxin Pills. With the real world sample of the mixed end-point powder of Tongren Niuhuang Qingxin Pills as the research object, hyperspectral imaging technology was used to collect a total of 32 400 data points with a size of 180 pix×180 pix. Spectral angle matching(SAM), classical least squares and mixed tuned matched filtering(MTMF) were used to identify the spatial distribution of rare medicines. MTMF model showed higher identification accuracy, therefore the spatial distribution of the blended intermediates was identified based on the MTMF model. The histogram method was also used to evaluate the spatial distribution uniformity of rare medicines. The results showed that the standard deviation was 4.78, 6.5, 3.48, 1.96, and 3.00 respectively for artificial bezoar, artificial musk, Borneol, Antelope horn and Buffalo horn; the variance was 22.8, 42.3, 12.1, 3.82, and 9.00, and the skewness was 1.26, 1.71, 0.06,-0.86, and 1.04, respectively. The final results showed that the most even blending was achieved in concentrated powder of Borneol, Antelope horn and Buffalo horn, followed by artificial bezoar, and last artificial musk. A visualization method was established for quality attributes of distribution uniformity in blending process of Tongren Niuhuang Qingxin Pills. It could provide evidences of quality control methods in the mixing process of big brand traditional Chinese medicine.