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

Abstract

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.