VARIANCE-DECOMPOSITION OF PURE-COMPONENT SPECTRA AS A MEASURE OF SELECTIVITY

Care is required for multicomponent analysis if misleading results are to be avoided. The problem ofill-conditioned calibration matrices is of primary concern. This type of numerical instability isrepresented as spectral overlap of calibration spectra. Depending on the degree of spectral overlap, thesample concentration estimates can be severely affected. A practical statistical procedure is discussedwhich tests for the presence of spectral overlap among the pure-component spectra and simultaneouslyassesses the degree that concentration estimates may be degraded. Guidelines are developed to ascertainhow much departure from spectral orthogonality is acceptable.

VARIANCE-DECOMPOSITION OF PURE-COMPONENT SPECTRA AS A MEASURE OF SELECTIVITY

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.