A combined quality evaluation method that integrates chemical constituents, appearance traits and origins of raw Rehmanniae Radix pieces

2021 ◽  
Vol 19 (7) ◽  
pp. 551-560
Author(s):  
Min GU ◽  
Yi-Ping YUAN ◽  
Zi-Nan QIN ◽  
Yan XU ◽  
Nan-Nan SHI ◽  
...  
2020 ◽  
Author(s):  
Min Gu ◽  
Yiping Yuan ◽  
Zinan Qin ◽  
Nannan Shi ◽  
Yanping Wang ◽  
...  

Abstract Background The quality control of traditional Chinese medicine (TCM) is a challenge for internationalization of TCM. Modern quality evaluation methods ignore origins and appearance traits, and traditional quality evaluation methods lack quantitative analysis. Therefore, an integrated quality evaluation method is urgent in need. Raw Rehmanniae Radix (RRR) was a widely used TCM. Its quality has caught much attention, and the existing quality evaluation methods have certain limitations. The aim of this study was to establish a comprehensive and practical method for the quality evaluation and control of RRR pieces based on its chemical components, appearance traits and origins.Methods 33 batches of RRR pieces were collected from 6 provinces, high performance liquid chromatography (HPLC) was applied to determine 5 constituents including catalpol, rehmannioside A, rehmannioside D, leonuride and verbascoside in RRR pieces. The appearance traits were qualitatively observed. Furthermore, correlation analysis, principal components analysis (PCA), cluster analysis and t-test were performed to discriminate and evaluate different quality of RRR pieces. Results 33 batches of RRR pieces could be divided into three categories, the samples of Henan province were in a group, the samples from Shandong province and Shanxi province were classified in a group, and the samples from other provinces were divided in a group. Furthermore, the constituents and appearance traits of RRR pieces were significantly different in diverse origins. Conclusions The combined method of chemical components, appearance traits and origins could classify and distinguish different quality of RRR pieces, which could provide a basic reference to quality control of TCM.


2013 ◽  
Vol 32 (3) ◽  
pp. 710-714
Author(s):  
Jin-jin WEI ◽  
Su-mei LI ◽  
Wen-juan LIU ◽  
Yan-jun ZANG

2021 ◽  
pp. 1-13
Author(s):  
Yanjie Qi ◽  
Zehui Yang ◽  
Lin Kang

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.


Fast track article for IS&T International Symposium on Electronic Imaging 2021: Image Quality and System Performance XVIII proceedings.


2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>


Sign in / Sign up

Export Citation Format

Share Document