The Dynamic Quality Control Method Based on Relation Analysis

2013 ◽  
Vol 307 ◽  
pp. 433-436 ◽  
Author(s):  
Guang Zhou Diao ◽  
Li Ping Zhao ◽  
Yi Yong Yao
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Control Method ◽  
Principal Component ◽  
Support Vector ◽  
Multiple Control ◽  
Quality Control Method ◽  
Relation Analysis ◽  
Dynamic Quality ◽  
Control Chart Pattern

To improve product quality in manufacturing process, a dynamic quality control method based on relation analysis is proposed. With the method, the dynamic regulated principal component analysis is constructed by introducing discount factor to eliminate the autocorrelation via the data, and the limit of multiple control charts is calculated by squared prediction error (SPE) statistics. Then, a dynamic adjusting policy by support vector machine (SVM) is proposed based on control chart pattern recognition. Finally, a case study for applicability is presented to verify the proposed method.

Chemical Fingerprint Analysis and Simultaneous Determination of Nucleosides and Amino Acids in Kang Fu Xin Liquid by High Performance Liquid Chromatography with Diode Array Detector

2020 ◽  
Vol 16 (7) ◽  
pp. 831-843
Author(s):  
Yuwen Wang ◽  
Shuping Li ◽  
Liuhong Zhang ◽  
Shenglan Qi ◽  
Huida Guan ◽  
...  
Keyword(s):  
Amino Acids ◽  
Quality Control ◽  
Uric Acid ◽  
Control Method ◽  
Principal Component ◽  
Gradient Elution ◽  
Hplc Method ◽  
Array Detector ◽  
Fingerprint Analysis ◽  
Wavelength Switching

Background and Objective: Kang Fu Xin liquid (KFX) is an official preparation made from the ethanol extract product from P. Americana. The present quality control method cannot control the quality of the preparation well. The aim of the present study is to establish a convenient HPLC method for multicomponents determination combined with fingerprint analysis for quality control of KFX. Methods: An HPLC-DAD method with gradient elution and detective wavelength switching program was developed to establish HPLC fingerprints of KFX, and 38 batches of KFX were compared and evaluated by similarity analysis (SA), hierarchical clustering analysis (HCA), and principal component analysis (PCA). Meanwhile, six nucleosides and three amino acids, including uracil, hypoxanthine, uric acid, adenosine, xanthine, inosine, tyrosine, phenylalanine and tryptophan in KFX were determined based on the HPLC fingerprints. Results: An HPLC method assisted with gradient elution and wavelength switching program was established and validated for multicomponents determination combined with fingerprint analysis of KFX. The results demonstrated that the similarity values of the KFX samples were more than 0.845. PCA indicated that peaks 4 (hypoxanthine), 7 (xanthine), 9 (tyrosine), 11, 13 and 17 might be the characteristic contributed components. The nine constituents in KFX, uracil, hypoxanthine, uric acid, adenosine, xanthine, inosine, tyrosine, phenylalanine and tryptophan, showed good regression (R2 > 0.9997) within test ranges and the recoveries of the method for all analytes were in the range from 96.74 to 104.24%. The limits of detections and quantifications for nine constituents in DAD were less than 0.22 and 0.43 μg•mL-1, respectively. Conclusion: The qualitative analysis of chemical fingerprints and the quantitative analysis of multiple indicators provide a powerful and rational way to control the KFX quality for pharmaceutical companies.

scholarly journals A Reliable, Label Free Quality Control Method for the Production of DNA Microarrays with Clinical Applications

Polymers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 340
Author(s):  
Elisa Chiodi ◽  
Francesco Damin ◽  
Laura Sola ◽  
Lucia Ferraro ◽  
Dario Brambilla ◽  
...  
Keyword(s):  
Quality Control ◽  
Dna Microarrays ◽  
Control Method ◽  
False Negative ◽  
Label Free ◽  
Negative Results ◽  
System A ◽  
Quality Control Method ◽  
False Negative Results ◽  
Imaging Sensor

The manufacture of a very high-quality microarray support is essential for the adoption of this assay format in clinical routine. In fact, poorly surface-bound probes can affect the diagnostic sensitivity or, in worst cases, lead to false negative results. Here we report on a reliable and easy quality control method for the evaluation of spotted probe properties in a microarray test, based on the Interferometric Reflectance Imaging Sensor (IRIS) system, a high-resolution label free technique able to evaluate the variation of the mass bound to a surface. In particular, we demonstrated that the IRIS analysis of microarray chips immediately after probe immobilization can detect the absence of probes, which recognizably causes a lack of signal when performing a test, with clinical relevance, using fluorescence detection. Moreover, the use of the IRIS technique allowed also to determine the optimal concentration of the probe, that has to be immobilized on the surface, to maximize the target recognition, thus the signal, but to avoid crowding effects. Finally, through this preliminary quality inspection it is possible to highlight differences in the immobilization chemistries. In particular, we have compared NHS ester versus click chemistry reactions using two different surface coatings, demonstrating that, in the diagnostic case used as an example (colorectal cancer) a higher probe density does not reflect a higher binding signal, probably because of a crowding effect.

scholarly journals A Two-Stage Quality Control Method for 2-m Temperature Observations Using Biweight Means and a Progressive EOF Analysis

2013 ◽  
Vol 141 (2) ◽  
pp. 798-808 ◽  
Author(s):  
Zhifang Xu ◽  
Yi Wang ◽  
Guangzhou Fan
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Control Process ◽  
Lapse Rate ◽  
Systematic Bias ◽  
Eof Analysis ◽  
Two Stage ◽  
Quality Control Method ◽  
Quality Control Process ◽  
The Difference

Abstract The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.

Sign in / Sign up

Export Citation Format

Share Document