Multivariate control charts based on net analyte signal (NAS) and Raman spectroscopy for quality control of carbamazepine

2011 ◽  
Vol 705 (1-2) ◽  
pp. 35-40 ◽  
Author(s):  
Werickson Fortunato de Carvalho Rocha ◽  
Ronei Jesus Poppi
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Control Charts ◽  
Multivariate Control Charts ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Multivariate Control

A Method for Monitoring the Surface Conservation of Wooden Objects by Raman Spectroscopy and Multivariate Control Charts

2003 ◽  
Vol 75 (20) ◽  
pp. 5567-5574 ◽  
Author(s):  
Emilio Marengo ◽  
Elisa Robotti ◽  
Maria Cristina Liparota ◽  
Maria Carla Gennaro
Keyword(s):  
Raman Spectroscopy ◽  
Control Charts ◽  
Multivariate Control Charts ◽  
Multivariate Control

scholarly journals Comparing the Performance of Several Multivariate Control Charts Based on Residual of Multioutput Least Square SVR (MLS-SVR) Model in Monitoring Water Production Process

2021 ◽  
Vol 2123 (1) ◽  
pp. 012018
Author(s):  
M Ahsan ◽  
T R Aulia
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Control Charts ◽  
Continuous Process ◽  
Quality Characteristics ◽  
Least Square ◽  
Support Vector ◽  
Water Quality Control ◽  
Multivariate Control Charts ◽  
Multivariate Control

Abstract Water that is used as the basic human need, requires a processing process to get it. Water quality control in Tirtanadi Water Treatment Plant is still univariate, while theoretically the quality characteristics of water quality are correlated and there is also an autocorrelation due to the continuous process. In this study, quality control is performed on three main variables of water quality characteristics, namely acidity (pH), chlorine residual (ppm), and turbidity (NTU) using several multivariate control charts based on Multioutput Least Square Support Vector Regression (MLS-SVR) residuals. MLS-SVR modelling is used to overcome and get rid of autocorrelation. The input results of the MLS-SVR model are specified from the significant lag of the Partial Autocorrelation Function (PACF), which in this study, is the first lag. The results of the MLS-SVR input model and the optimal combination of hyper-parameters produce residual values that have no autocorrelation anymore. The residuals are used to develop the Hotelling’s T 2, Multivariate Exponentially Weighted Moving Average (MEWMA), and Multivariate Cumulative Sum (MCUSUM) control charts. In phase I, we found that the processes are statically controlled. Meanwhile, in phase II, the monitoring results show that there are several out-of-control observations.

Quality Control-An Approach Applying Multivariate Control Charts during the Operation of Systems Involving Human Processes

2013 ◽  
Vol 17 (2) ◽  
pp. 204-212
Author(s):  
Matthew J. Mihalcin ◽  
Thomas A. Mazzuchi ◽  
Shahram Sarkani ◽  
Jason R. Dever
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Multivariate Control Charts ◽  
Multivariate Control

Treatment plan quality control using multivariate control charts

2021 ◽  
Author(s):  
Arkajyoti Roy ◽  
Reisa Widjaja ◽  
Min Wang ◽  
Dan Cutright ◽  
Mahesh Gopalakrishnan ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Treatment Plan ◽  
Plan Quality ◽  
Multivariate Control Charts ◽  
Multivariate Control

Multivariate control charts based on NAS and mid-infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

2015 ◽  
Vol 29 (7) ◽  
pp. 411-419 ◽  
Author(s):  
Hery Mitsutake ◽  
Eloiza Guimarães ◽  
Helieder C. Freitas ◽  
Lucas C. Gontijo ◽  
Douglas Q. Santos ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Control Charts ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Multivariate Control Charts ◽  
Soybean Biodiesel ◽  
Multivariate Control
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