The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra® based on chromatographic impurity profiles

The Analyst ◽  
2016 ◽  
Vol 141 (3) ◽  
pp. 1060-1070 ◽  
Author(s):  
B. Krakowska ◽  
D. Custers ◽  
E. Deconinck ◽  
M. Daszykowski
Keyword(s):  
Monte Carlo ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Selection Methods ◽  
Validation Framework ◽  
Partial Least Squares Model ◽  
Discriminant Models ◽  
Impurity Profiles

A general Monte Carlo validation framework of discriminant models is proposed that is used in the context of authenticity studies based on chromatographic impurity profiles.

Variable Selection for Partial Least-Squares Calibration of Near-Infrared Data from Orthogonally Designed Experiments

2002 ◽  
Vol 56 (3) ◽  
pp. 337-345 ◽  
Author(s):  
S. Kamaledin Setarehdan ◽  
John J. Soraghan ◽  
David Littlejohn ◽  
Daran A. Sadler
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Designed Experiments ◽  
Selection For ◽  
Infrared Data
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