Highlights
- Upload your reference standards data
- Every day, technicians upload the most recent PXRF measurement spreadsheets to one or more Projects
- Analysts download corrected data and QAQC reports at regular intervals
- Option to accommodate an individual organization’s file formats or reporting needs
Features
- Manage projects and data as an individual user, or provide access to all users from your organization
- The upload system can be customized to your exact file format
- Configure the in-built correction algorithm, or request your own
- Reduced Major Axis regression, or standard linear regression
- Method for averaging standard measurements
- Acceptance criteria for correction factor – R2 threshold, number of standard points (per element)
- Use specific standards for one or more elements
- View and download QA/QC visualizations and reporting
- Outlier detection and automatic identification of possible data integrity issues
Correction algorithm
The data processing methods used by this app are the same as those outlined in Fisher et al. (2014), Gazley & Fisher (2014), and the references in these papers. Standards analysed in the sample stream are used to calculate a correction factor.
For all elements corrected, a correction factor m is derived from fitting a linear regression y = mx, where x is the raw value from the pXRF machine, y is the standard reference value, and m = the correction factor or regression gradient. The R^2 value of the correction factor is reported as some users may assess the validity of their correction factors, for instance, accepting slope values m that are 0.9–1.1 and R^2 values that are > 0.8.
References
Fisher, L., Gazley, M.F., Baensch, A., Barnes, S.J., Cleverley, J. & Duclaux, G., 2014. Resolution of geochemical and lithostratigraphic complexity: a workflow for application of portable X-ray fluorescence to mineral exploration. Geochemistry: Exploration, Environment, Analysis, 14(2), pp.149- 159. link
Gazley, M.F. & Fisher, L.A., 2014. A review of the reliability and validity of portable X-ray fluorescence spectrometry (pXRF) data. In: Mineral Resource and Ore Reserve Estimation – The AusIMM Guide to Good Practice. Second edition. The Australasian Institute of Mining and Metallurgy, Melbourne, 69–82.