Data sets of order three or more are increasingly common in areas ranging from biomedical imaging to threat detection, and are output from a number of spectroscopy (e.g. NIR, Raman, Excitation Emission Fluorescence) and spectrometry (e.g. SIMS) methods. Various chemometrics methods can be used to reduce the dimensionality of these data sets, and the resulting compressed data can then be visualized. These methods include Principal Components Analysis (PCA), Multivariate Curve Resolution (MCR), and Maximal Autocorrelation Factors (MAF) as well as numerous data clustering methods (e.g. HCA, DBSCAN, KNN) and classification techniques (e.g. PLS-DA, SIMCA). These methods can also be combined with traditional image analysis techniques such as particle analysis. This talk gives examples of how up front chemometric modeling can be used to extract relevant information which can then be visualized in two and three dimensions, and in time.

Date Published: 4 March 2019
PDF
Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830F (4 March 2019); doi: 10.1117/12.2516224

Published in SPIE Proceedings Vol. 10883:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI
Thomas G. Brown; Tony Wilson, Editor(s)