The versatility of diffuse spectral reflectance (DSR) was investigated as a complementary methodology to XRD and XRF when studying clay minerals in stratigraphic sequences. The Analytical Spectral Device (ASD) LabSpec® Pro FR UV/VIS/nIR spectrometer provides an innovative nondestructive methodology that is cost effective, portable, quick, and easy to use with samples in the lab or field. LabSpec® Pro FR spectrometer and similar equipment are remarkable research tools underutilized in the area of clay mixtures. This study develops a new methodology that demonstrates the versatility of the LabSpec® Pro FR and the use of DSR as a tool for generating a spectral library and then determining clay mineralogy of various core samples. Samples from two sources were evaluated: (1) sediment from core MNK3, from a slack water Pleistocene lake near St. Louis, in which stratigraphic changes in clay mineralogy occur down core, and (2) the Ordovician Millbrig K-bentonite (samples from AL, GA, KY, TN, and VA), an altered tephra in which the changes occur laterally in a single horizon. DSR spectral data is validated against XRD, ICP-MS, and XRF data. This spectral library was generated from four primary clays and clay mixtures, consisting over 231 two variable mixtures in 5% increments, by weighted percents and is augmented with spectra from the USGS spectral library. Clay mineral standards were obtained from the Clay Mineral Repository and Wards Natural Science. The aim is to close the gap that currently exists for an expanded spectral library of clay mixtures and explore the DSR variability of clay mixtures. PCA (Principal Component Analysis) was used to correlate the spectral data of the library with the two MNK3 and Millbrig sample sets. Stepwise Linear Regression (SLR) analysis was used with the composite library as an identification tool. By combining PCA analysis of unknowns with SLR against our clay mixture library, we identify our components in an objective, quantifiable way. The model predictors from the analysis gave highly significant R-squared values for the extracted PCA assemblages depending on component. One of the challenges was comparing the XRD clay percents against the predicted models. Frequently, the primary clay was predicted, but not the secondary clay. Basically, the result is an ordinal distribution of the amounts of minerals present in the mixture. Ordinal distributions, as non-parametric data, do not allow the computation of averages or proportions, but tell only relative amounts such as greater, greatest, and least. This may be because both cores represent a four component clay mixtures plus ancillary minerals, as opposed to the two component library. Predictability difficulties may also have been due to confounding factors such as the presence of iron-bearing minerals in the mixture causing what is termed by Balsam, 1999, as the ‘matrix effect’; Balsam also states that iron-bearing minerals such as hematite may be masked by illite and chlorite. The spectral clay mineral library is useful and the methodology pursued has proven successful. However, at this time there is no consistency in the predictability of the data. As a result, future research needs to eliminate intervening factors sequentially to determine various iron components and their impact on readings (Balsam et al, 1999).