Raman Spectroscopy and Mapping
Raman
Spectra
Converting Raman Data Exported in SPC Binary Format
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Converting Raman Data from ANSI ASCII txt files
A directory of ASCII files each
containing a different Raman spectrum are converted to VAMAS format using the
.irf conversion filter. These Raman spectra are stored using an irregular
step-size in the wave number and hence an IRREGULAR VAMAS format is used to
save the same data as VAMAS format.
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Modelling Raman Background Signal using a Cubic Spline Approximation
Curved backgrounds within Raman
Spectra can be modelled using the Spine Linear background type. A
quantification region used to specify the background to a spectrum forms the
basis for constructing a cubic spline function. Once a background is defined
the Test Data property page provides the means of processing the background
subtracted spectrum. The video illustrates how to define a Spline Linear
background and the means by which the mouse is used to make adjustments to the
shape of the cubic spline background. These mouse interactions with the spline
background are enabled by selecting the Components property page which allow
both synthetic peaks and the spline to be adjusted simultaneously using the
mouse.
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Raman
Mapping
Converting Renishaw Raman Image Data Sets in ASCII Format (CasaXPS
2.3.19)
Convert an ASCII file exported from
Renishaw data as x, y, wave and intensity ASCII values.
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Spectra and Images from Raman Data
A Renishaw Raman data set
consisting of spectra assigned stage locations is used to illustrate how
differentiation of Raman spectra with a rising backgrounds can be used to
reduce the influence of background signal within the imaging data set thus
highlight chemically significant variation in imaging Raman data. These
chemically interesting spatial variations are used to construct spectra with
differing peak structures characteristic of the sample material.
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Reducing Raman Spectra to Component Spectral Forms
Using data from a mapping
experiment, the relationship between spectra and images is explored using
Principal Component Analysis and Vector Manipulation Techniques. Images are
prepared from spatially resolved Raman spectra by integrating signal from
spectral peak structures using quantification regions. These images are in turn
used to extract spectra based on image intensity to classify pixels into 12
distinct locations with similar pixel intensities resulting in 12 spectra with
differing background and peak structure. The reduced set of spectra are used to
construct 6 component spectral forms which are shown to include the essential differences
for all spectra measured during the Raman map of a tiger iron sample. These
data were measured using a Renishaw Raman instrument at University of Western
Ontario, Canada.
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