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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LLS-tiger-iron.PNG MAP 2 WHITE LIGHTR.jpgLLS-tiger-iron-fc-image.PNGstage-Map-regions.PNG