Paper No. 7
Presentation Time: 9:00 AM-6:00 PM
MATLAB CODE FOR QUALITATIVE AND QUANTITATIVE XRD ANALYSES OF COMMON ROCK-FORMING MINERALS
LUETKEMEYER, P. Benjamin1, BOOMGARTEN, Erich
2, KELLY, Brian
2 and KIRSCHNER, David
3, (1)Earth and Atmospheric Sciences, Saint Louis University, 205 O'Neil Hall, 3642 Lindell Blvd, St. Louis, MO 63108, (2)School of Medicine, Saint Louis University, 221 N. Grand Blvd, St. Louis, MO 63103, (3)Department of Earth and Atmospheric Sciences, Saint Louis University, 205 O'Neil Hall, 3642 Lindell Blvd, St. Louis, MO 63108, luetkepb@slu.edu
The ability to identify and quantify the abundance of minerals in soils and rocks is fundamental to solving many problems in the geological and environmental sciences. One widely used method for identification and quantification of minerals is the X-ray diffraction analysis of powder samples. Developments in the preparation techniques of randomly oriented powder samples allows for the identification and quantification of most common rock- and soil-forming minerals to within one to two weight percents of true abundance. Numerous proprietary software (
e.g., MDI's Jade software and Bruker's Diffrac
Plus) and shareware packages (
e.g., WinFit, RockJock, FullPat, GSAS, FullProf 2000) are available to users that range significantly in cost and sophistication. The XRD toolbox developed by the authors incorporates a broad set of MATLAB functions that can be modified or tailored according to the user's needs. Because the analyses are implemented in MATLAB the XRD toolbox can be used as a teaching tool as well.
We introduce a MATLAB script that can identify and quantify the mineralogy of most common rocks. We take two approaches in our analysis of XRD data - one that depends on peak positions and relative intensities of known minerals, the other that relies on an in-house set of XRD spectra from known mineral standards that have been prepared and analyzed according to a rigorously defined set of procedures. The reproducibility and accuracy of the former approach depends in large part on how the spectra are handled and the algorithm used to quantify the mineral (e.g., peak height versus full-pattern fitting). The latter approach depends primarily on the reproducibility of results for both known mineral standards and unknown powders and is limited by the number of spectra obtained for the mineral standards database. The latter approach is emulates the award-winning approaches of Dennis Eberl and colleagues and Jan Srodon and colleagues from recent Reynolds Cup competitions. Samples prepared with known amounts of mineral and standard were used in this study to illustrate the different methods within the toolbox. These results are then compared to results obtained using MDI's Jade software and RockJock.
