In this study, a software package for imaging and analysis of hyperspectral datasets named Vespucci was developed and successfully implemented by the author. Vespucci is capable of several analysis techniques including univariate imaging, principal components analysis, partial-least-squares discriminant analysis, and vertex components analysis with endmember extraction and k-means clustering. Additionally, Vespucci can perform a number of useful data-preprocessing operations, including filtering, normalization, baseline correction, and background subtraction. Vespucci can theoretically handle any dataset that consists of evenly-spaced spatial data with a corresponding digital signal, including spectroscopic images, mass spectrometric images, and X-ray diffraction data. The utility of this software has been successfully demonstrated on micro-Raman maps consisting of spectral and spatial data of ~1 cm-1 and 1 µm resolution, respectively. This was used to assess the degree of mineralization of Orconectes virilis exoskeleton in an environmental toxicology study by examining the bandwidths of signals arising from various calcium carbonate polymorphs. The software facilitated the rapid and versatile analysis of the spectral data.

Vespucci: A novel software tool for hyperspectral data analysis and imaging

249th National Meeting (2015)

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