Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 321903
Title Net analyte signal calculation for multivariate calibration
Author(s) Ferre, J.; Faber, N.M.
Source Chemometrics and Intelligent Laboratory Systems 69 (2003)1-2. - ISSN 0169-7439 - p. 123 - 136.
DOI https://doi.org/10.1016/S0169-7439(03)00118-7
Department(s) Agrotechnological Research Institute
Publication type Refereed Article in a scientific journal
Publication year 2003
Keyword(s) partial least-squares - simultaneous spectrophotometric determination - spectroscopic analysis - wavelength selection - multicomponent analysis - emission-spectrometry - preprocessing methods - chemometric analysis - sensitivity - interrelationships
Abstract A unifying framework for calibration and prediction in multivariate calibration is shown based on the concept of the net analyte signal (NAS). From this perspective, the calibration step can be regarded as the calculation of a net sensitivity vector, whose length is the amount of net signal when the value of the property of interest (e.g. analyte concentration) is equal to unity. The prediction step can be interpreted as projecting a measured spectrum onto the direction of the net sensitivity vector. The length of the projected spectrum divided by the length of the net sensitivity vector is the predicted value of the property of interest. This framework, which is equivalent to the univariate calibration approach, is used for critically revising different definitions of NAS and their calculation methods. The framework is particularized for the classical least squares (CLS), principal component regression (PLS) and partial least-squares (PCR) regression models
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