Presentation
4 March 2019 Visualization of three-way and higher order data sets (Conference Presentation)
Author Affiliations +
Abstract
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Barry M. Wise "Visualization of three-way and higher order data sets (Conference Presentation)", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108830F (4 March 2019); https://doi.org/10.1117/12.2516224
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KEYWORDS
Visualization

3D modeling

Chemometrics

Principal component analysis

Spectroscopy

Biomedical optics

Imaging spectroscopy

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