Iolite at the forefront of data reduction

The iolite team has just produced a new publication detailing a novel approach to multi-phase trace element mapping (also known as trace element imaging). The new module is called MinMapping, because it was originally developed for multi-mineralic samples, but it can also be applied to any sample where you need to assign a separate reference material, internal standard (IS) element, or IS content for different regions in your sample. To take a geological example, if you were mapping a basalt, you could use NIST612 for plagioclase, and BCR2G for the olivine and clinopyroxene. Even better, you can use Si as the IS for the plagioclase, and Ti or Mg for the olivine and clinopyroxene grains.

The process is based on assigning regions of your mapped area to “phases”. You can use maps created from your baseline subtracted channels to define each phase. We’ve also put in some snazzy extra features, like Principal Component Analysis (to reduce data dimensionality and to highlight differences between phases) and fuzzy c-means clustering to automatically define phases.

The whole approach has been documented in a peer-reviewed publication for GGR, and is a complementary process to the CellSpace spatially registered mapping procedure. And it’s included as standard in iolite 3.

If you have any comments or questions about CellSpace or MinMapping, feel free to start a new topic on the forum.

We’re currently working hard on 3D imaging in iolite, and have some great new features coming soon (subpixel pyramidal registration included by default!) 🙂 And we’re also hoping to get empirical cumulative distribution function colour mapping working in iolite, just as Rittner & Müller (2012) described in their paper about their R package LAICPMS. It’s a great feature that iolite has been needing for some time, and is really important for LA-ICP-MS images

If you have any requests for new features, feel free to head over to the forum and let us know!

The iolite team