Self-Weighted Correlation Coefficients and Their Application to Measure Spectral Similarity

Abstract

A technique for spectral searching with noisy data is described that improves the performance over contemporary approaches. Instead of simply calculating the correlation coefficient between the spectrum of an unknown and a series of reference spectra, greater weight is given to the more intense features in the reference spectra. The weight array, w, is given by |r|/{1 + d}, where the vector r represents the reference spectrum and the difference vector, d, contains the difference between the sample and reference data points, equal to |s-kr|, where k is a scaling factor that eliminates the effect of signal strength. By this approach, a large weight is only given to those points that have relatively high absorbance and are close to their counterparts in the reference spectrum. This technique was shown to give significantly improved performance when applied to noisy spectra of trace atmospheric components obtained by target factor analysis.