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Variance‐decomposition of pure‐component spectra as a measure of selectivity
Author(s) -
Kalivas John 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.1180030208
Subject(s) - orthogonality , calibration , spectral line , component (thermodynamics) , variance (accounting) , measure (data warehouse) , mathematics , degree (music) , spectral shape analysis , statistics , principal component analysis , computer science , physics , data mining , thermodynamics , geometry , accounting , astronomy , acoustics , business
Care is required for multicomponent analysis if misleading results are to be avoided. The problem of ill‐conditioned calibration matrices is of primary concern. This type of numerical instability is represented as spectral overlap of calibration spectra. Depending on the degree of spectral overlap, the sample concentration estimates can be severely affected. A practical statistical procedure is discussed which tests for the presence of spectral overlap among the pure‐component spectra and simultaneously assesses the degree that concentration estimates may be degraded. Guidelines are developed to ascertain how much departure from spectral orthogonality is acceptable.