Thermal infrared spectroscopy and partial least squares regression to determine mineral modes of granitoid rocks

[1] In this paper, we present an approach to extracting mineralogic information from thermal infrared (TIR) spectra that is not based on an input library of pure mineral spectra nor tries to extract spectral end-members from the data. Instead, existing modal mineralogy for a number of samples are used to build a partial least squares regression (PLSR) model that links the mineralogy of the samples to their respective TIR spectral signatures. The resulting PLSR models can be applied to a larger group of samples for which the mineralogic composition can be estimated from the TIR spectra alone. Thermal infrared reflectance spectra were recorded from 1330–625 cm−1 (7.5 to 16.0 μm). The method is tested on igneous rocks from a porphyry copper deposit in Yerington, Nevada. As a reference, modal mineralogic composition was determined with traditional polarization microscopy on thin sections. Partial least squares regression models were developed to link the thermal infrared spectra to the thin section determined mineral modes of alkali feldspar, plagioclase and quartz, as well as the average plagioclase composition information. Results indicate that rock samples can be classified successfully in a quartz-alkali feldspar-plagioclase diagram based on thermal infrared spectroscopy and partial least squares regression modeling. Estimated errors for the mineralogic composition model results were found to be smaller or equal to traditional methods with errors of ±5.1% (absolute) for alkali feldspar, ±8.5% (absolute) for plagioclase and ±6.9% (absolute) for quartz. The regression model for plagioclase composition predicted with estimated errors of ±7.8 mol% anorthite.