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Number of Samples and Wavelengths Required for the Training Set in Near-Infrared Reflectance Spectroscopy

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Near-infrared reflectance spectroscopy uses a learning algorithm to derive a set of weighting coefficients from the reflectance spectra of a reference sample set. These coefficients, when applied to the reflectance values of an unknown sample at specific wavelengths, can be used to calculate constituent concentrations. Having enough samples in the training set and enough wavelengths in the calculation procedures is essential, but there are severe drawbacks to picking too large a number. This paper describes the principles and implementation of a working procedure for objectively calculating the minimum number of training samples required.

Keywords: Near-infrared; Reflectance spectroscopy

Document Type: Research Article


Affiliations: 1: Department of Chemistry, Indiana University, Bloomington, Indiana 47405; present address: Dept. of Chemistry BG-10, University of Washington, Seattle, WA 98195 2: Department of Chemistry, Indiana University, Bloomington, Indiana 47405 3: Lawrence Livermore National Laboratory, Livermore, California 94550

Publication date: November 1, 1984

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