2011/01/01 by Chad Paton, John Hellström, Bence Paul +2 · 16 citations
Computer Science · Environmental Science · #Geochemistry and Geologic Mapping #Time Series Analysis and Forecasting #Isotope Analysis in Ecology
paper · doi:10.1039/c1ja10172b
openalex publication_date 2011/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Iolite is a non-commercial software package developed to aid in the processing of inorganic mass spectrometric data, with a strong emphasis on visualisation versus time of acquisition. The goal of the software is to provide a powerful framework for data processing and interpretation, while giving users the ability to implement their own data reduction protocols. It is intended to be highly interactive, providing the user with a complete overview of the data at all stages of processing, and allowing the freedom to change parameters and reprocess data at any point. The program presents a variety of windows for the selection and viewing of data versus time, as well as features for the generation of X-Y plots, summary reports and export of data. In addition, it is capable of generating X-Y images from laser ablation rasters, and combining information from up to four separate elemental concentrations (intensities of red, green and blue, and the z-axis) in a false-colour three-dimensional image. By virtue of its underlying computing environment—Igor Pro—Iolite is capable of processing very large datasets (i.e., millions of timeslices) rapidly, and is thus ideal for the interrogation of multi-hour sessions of laser ablation data that can not be easily manipulated in conventional spreadsheet applications, for example. It is also well suited to multi-day sessions of solution-mode inductively-coupled plasma mass spectrometer (ICPMS) or thermal ionisation mass spectrometer (TIMS) data. A strong emphasis is placed on the interpolation of parameters that vary with time by a variety of user selectable methods including smoothed cubic splines. Data are processed on a timeslice-by-timeslice basis, allowing outlier rejection and calculation of statistics to be employed directly on calculated results. This approach can reduce the risk of processing biases associated with the manipulation of integrated datasets, while also allowing the implementation of more complex data reduction methods.