Mineral abundance determination: Quantitative deconvolution of thermal emission spectra

A linear retrieval (spectral deconvolution) algorithm is developed and applied to high‐resolution laboratory infrared spectra of particulate mixtures and their end‐members. The purpose is to place constraints on, and test the viability of, linear spectral deconvolution of high‐resolution emission spectra. The effects of addition of noise, data reproducibility, particle size variation, an increasing number of minerals in the mixtures, and blind end‐member input are also examined. Thermal emission spectra of 70 mineral mixtures ranging from 2 to 15 end‐members and having particle diameters of 250–500 μm were obtained. Deconvolution results show that the assumption of linear mixing is valid and enables mineral percentage prediction to within 5% on average with residual errors of less than 0.1% total emissivity. One suite (21 distinct mixtures), varying from