| AVS 65th International Symposium & Exhibition | |
| Biomaterial Interfaces Division | Friday Sessions |
| Session BI+AS+NS-FrM |
| Session: | Characterization of Biological and Biomaterial Surfaces |
| Presenter: | Paul Pigram, La Trobe University, Australia |
| Authors: | R.M.T. Madiona, La Trobe University, Australia N.G. Welch, CSIRO Manufacturing, Australia D.A. Winkler, La Trobe University, Australia J.A. Scoble, CSIRO, Austalia B.W. Muir, CSIRO, Australia P.J. Pigram, La Trobe University, Australia |
| Correspondent: | Click to Email |
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is continuously advancing. The data sets now being generated are growing dramatically in complexity and size. More sophisticated data analytical tools are required urgently for the efficient and effective analysis of these large, rich data sets. Standard approaches to multivariate analysis are being customised to decrease the human and computational resources required and provide a user-friendly identification of trends and features in large ToF-SIMS datasets.
We demonstrate the generation of very large ToF-SIMS data matrices using mass segmentation of spectral data in the range 0 – 500 m/z in intervals ranging from 0.01 m/z to 1 m/z. No peaks are selected and no peak overlaps are resolved. Sets of spectra are calibrated and normalized then segmented and assembled into data matrices. Manual processing is greatly reduced and the segmentation process is universal, avoiding the need to tailor or refine peak lists for difficult sample types or variants.
ToF-SIMS data for standard polymers (PET, PTFE, PMMA and LDPE) and for a group of polyamides are used to demonstrate the efficacy of this approach. The polymer types of differing composition are discriminated to a moderate extent using PCA. PCA fails for polymers of similar composition and for data sets incorporating significant random variance.
In contrast, artificial neural networks, in the form of self organising maps (SOMs) deliver an excellent outcome in classifying and clustering different and similar polymer types and for spectra from a single polymer type generated using different primary ions. This method offers great promise for the investigation of more complex bio-oriented systems.