Raman emulsion image

Figure 2.4 shows a comparison of the results obtained from the PCA and real chemical models for a Raman emulsion image [30]. The latter image is formed by four constituents related to the drop phase, the interphase, an additive and the off-drop phase. The two models (real and PCA) resemble each other when considering general trends for example, the score maps are reminiscent of the real distribution maps, although the information seems to be more mixed, and the most salient spectral features in the real spectra can also be found in the different... 

Figure 2.4 PCA model and real chemical Beer-Lambert model for a Raman emulsion image.

Figure 2.8 SIMPLISMA analysis on a Raman emulsion image. Representation of the spectra of the purest pixels (right plot) and the distribution map related to the purest spectral channels (bottom plot). Letters and numbers in both plots mark the location of purest pixels and purest spectral channels, respectively.

Figure 2.9 FSIW-EFA modus operand (monolayer Raman emulsion image), (a) Construction of pixel windows ...

Figure 2.9 FSIW-EFA modus operand (monolayer Raman emulsion image), (a) Construction of pixel windows (b) singular value plots of local PCA analyses (c) complete local rank map and (d) partial local rank map.

Figu re 2.2 (a) Raw spectra of a Raman emulsion layer image (b) Spectra after de-noising by principal component analysis (PCA) (c) Spectra after de-noising and baseline correction by asymmetric least squares. 

Figu re 2.3 Classical visualizations of a Raman emulsion layer hyperspectral image, (a) Global intensity plot (b) Distribution map derived from a single wavenumber. 

Figure 2.5 (a) Left RGB plot of the score images related to PCI, PC2 and PC3. Right Scatter score plot of PC3 versus PC2 (b) Pixel masks in scatter score plot and location of mask pixels in the Raman monolayer emulsion image. 

Figure 2.3 Classical visualizations of a Raman emulsion layer hyperspectral image.

Figure 3 Point-scan Raman images of an emulsion system.

JJ Andrew, MA Browne, IE Clark, TM Hancewicz, AJ Millichope. Raman imaging of emulsion systems. Appl Spectrosc 52 790-796, 1998. 

In the multiset analysis, spectral consistency and, therefore, compound identity are ensured because of the architecture of the multiset (with a single matrix), and distribution maps are better defined. The deficient quality of the results obtained by individual single image analysis in this example is due to two different factors (a) not all sample constituents are sufficiently represented in all emulsion layers and (b) the quality of the Raman signal (signal-to-noise ratio) decreases as depth increases. These problems are solved with multiset analysis because of the complementary composition information from the different images and the better signal-to-noise ratio of top emulsion layers that influence the quality of the overall multiset structure results. 

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