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The direct qualitative and quantitative determination of mineral components in shale rocks is a problem that has not been satisfactorily resolved to date. Infrared spectroscopy (IR) is a non-destructive method frequently used in mineral identification, yet challenging due to the similarity of spectral features resulting from quartz, clay, and feldspar minerals. This study reports on a significant improvement of this methodology by combining infrared attenuated total reflection spectroscopy (IR-ATR) with partial least squares (PLS) regression techniques for classifying and quantifying various mineral components present in a number of different shale rocks. The developed multivariate classification model was calibrated using pure component mixtures of the most common shale minerals (i.e., kaolinite, illite, montmorillonite, calcite, and quartz). Using this model, the IR spectra of 11 real-world shale samples were analyzed and evaluated. Finally, the performance of the developed IR-ATR method was compared with results obtained via X-ray diffraction (XRD) analysis.

Infrared Attenuated Total Reflectance Spectroscopy: An Innovative Strategy for Analyzing Mineral Components in Energy Relevant Systems