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Testing for redundancy in product quality control test criteria: An application to aviation turbine fuel
Author(s) -
Deane John M.,
MacFie Halliday J. H.
Publication year - 1989
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.1180030305
Subject(s) - spurious relationship , principal component analysis , context (archaeology) , curse of dimensionality , computer science , dimensionality reduction , redundancy (engineering) , mathematics , data mining , statistics , reliability engineering , engineering , artificial intelligence , paleontology , biology
Abstract Given a set of test criteria that determine a quality specification, the question often arises whether any of the tests are redundant because of intercorrelations Simple selection of tests on the basis of partial correlations with the other tests is rejected on the basis that the random error in the data may be causing spurious correlations. One method is to use cross‐validation to define the systematic principal components and examine the correlation structure in this reduced space. It is shown that the presence of principal components dominated by individual tests (‘variable specific’ PCs), which are indicated by cross‐validation as being non‐systematic, must be taken into account. Having defined the dimensionality, a variable reduction method based on Procrustes rotation selects subsets of tests that preserve the structure of the samples in multivariate space. This is an attractive proposition in the context of maintaining a quality control specification. It is also shown that the variable reduction technique can be used to aid the identification of the true dimensionality of the data space. This approach is applied to a number of routine tests carried out on aviation turbine fuel.