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The effect of interferences and calbiration design on accuracy: Implications for sensor and sample selection
Author(s) -
Lorber Avraham,
Kowalski Bruce R.
Publication year - 1988
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.1180020108
Subject(s) - overdetermined system , analyte , calibration , chemometrics , selection (genetic algorithm) , set (abstract data type) , computer science , biological system , sample (material) , chromatography , mathematics , chemistry , statistics , artificial intelligence , machine learning , mathematical analysis , biology , programming language
Methods of multivariate calibration use models that relate spectral data or sensor array responses to the concentrations of analytes. The goal is to insure that the calibration model can accurately estimate analyte concentrations in unknown samples not contained in the calibration set. The sensors or spectral channels (e.g. wavelengths) selected for incorporation in the model, as well as the samples selected for the calibration step, are known to have an effect on the accuracy of analysis for unknown samples. This work provides a fundamental treatment of this effect and derives criteria for optimal selection. Additionally, a proof is given for the advantage of having more sensors and calibration samples than analytes—the overdetermined case.