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Multivariate selection of variables in industrial quality control: Optimizing aviation fuel final control
Author(s) -
Andrade J. M.,
Prada D.,
Muniategui S.,
Gomez B.,
Pan M.
Publication year - 1993
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180070507
Subject(s) - selection (genetic algorithm) , multivariate statistics , quality (philosophy) , computer science , control (management) , kerosene , product (mathematics) , engineering , mathematics , machine learning , chemistry , artificial intelligence , geometry , epistemology , organic chemistry , philosophy
This paper deals with a typical question encountered in all industrial analytical laboratories: are all the tests performed in the laboratory to characterize the final product really necessary? In this work the cross‐validation technique, Procrustes rotation techniques and statistical variable selection have been used to reduce the 26 initial British Petroleum and ASTM kerosene specification test to ten ‘essential’ ones. Statistical as well as chemical considerations were used to select the optimum subset of original variables to be retained from all the possible ones.