- 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
- 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
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.
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.