An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration
Wavelength selection is a critical step for producing better prediction performance when applied to spectral
data. Considering the fact that the vibrational and rotational spectra have continuous features of
spectral bands, we propose a novel method of wavelength interval selection based on random frog, called
interval random frog (iRF). To obtain all the possible continuous intervals, spectra are first divided into
intervals by moving window of a fix width over the whole spectra. These overlapping intervals are ranked
applying random frog coupled with PLS and the optimal ones are chosen. This method has been applied to
two near-infrared spectral datasets displaying higher efficiency in wavelength interval selection than
others.
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