Rapid Chemical Analysis Using FT-IR and Raman Microscopy

Sophisticated instruments help in collecting large amounts of data conveniently as well as in a short time span. Thermo Fisher Scientific offer a range of instruments for rapid materials analysis such as a true imaging FT-IR microscope, the Nicolet iN10 MX, the Nicolet Continuum, the Nicolet iN10 and two dispersive Raman microscopes, the DXR Raman microscope and the Nicolet Almega XR.

These instruments can generate large quantities of high quality data rapidly. The challenge is now to extricate useful data from the large amount of data available. It is not always to easy to transport these large data files into external software packages. The OMNIC Atlµs software from Thermo Scientific offers comprehensive image analysis solutions that help obtaining actionable results in a short span of time.

Experimental

The OMNIC Atlµs software is highly powerful and has a number of sophisticated processing features for map sets and large chemical images. A range of processing techniques are supported by OMNIC ranging from simple such as correlation to advanced such as principal component analysis (PCA). These methods are shown in Table 1. The advantage of using these supplied methods is that they function within the OMNIC software and there is no need for any external program. The native OMNIC file that uses the same intuitive structure as OMNIC is used.

Table 1. Common analysis methods used in OMNIC Atlμs software.

Type Mathematics Required
Correlation None – display only
Peak Area/Intensity Basic – may also ratio
Principal Component Analysis (PCA) Advanced – finds variance ad reduces dimensionality of data set
Multivariate Curve Resolution (MCR) Advanced – reduces dimensionality and determines spectral components

Discussion

The peak area or correlation calculation handles simple issues like identification and general sizing. This is as simple as determining the layer thickness or the particle size. Initially data extraction is used to determine the sample size. Polystyrene beads with 1 µm size were placed in a gold reflective slide and a line map was measured across each individual bead. The line map’s step size is 0.1 micron. With the help of a peak area calculation and by the construction of a profile on this basis the full width half max of the profile peak yields the particle size as shown in Figure 1.

Line map across a single 1 micron polystyrene bead. The polystyrene peak located at 1001 cm-1 was used in a mathematic profile of the peak area. The blue profile shows the scale of the peak area, the local maxima verify that the bead is 1 micron in size.

Figure 1. Line map across a single 1 micron polystyrene bead. The polystyrene peak located at 1001 cm-1 was used in a mathematic profile of the peak area. The blue profile shows the scale of the peak area, the local maxima verify that the bead is 1 micron in size.

Nicolet iN10 Infrared Microscope

A correlation function is performed on the data for simple component identification as shown in Figure 2. Four unique layers are present in this multi- layered packaging sample. A thick cross section of 30 µm was made in order to prepare the sample for analysis after which an area map was collected. A correlation profile was used to identify the layers. The layer thickness is calculated using the X-Z profile of the layer as shown inTable 2.

Table 2. Layer identification and thickness; as determined by spectral correlation

Layer Identification Thickness
1 PET 30 μm
2 PE + Pigment (carbon black) 85 μm
3 and 5 PE 35 and 65 μm
4 and 6 Nylon 6 + EVA 30 and 10 μm

When a sample is analyzed for a small defect percentage or a contaminant, sophisticated mathematics is used for the quantification of components. In this sample, vapor deposition of poly methyl methacrylate (PMMA) and phase-separated polystyrene (PS) were done on a silicon substrate and vacuum deposition of single-walled nanotubes was done on the surface. Multivariate curve resolution analysis shows the relative distribution of the carbon nanotubes and polystyrene on the sample surface. The component identification of the sample set is provided by the MCR output, which can be treated using image analysis.

Multi layer film. The area map of the cross section was collected using 532 nm excitation. Each layer was determined through the use of spectral correlation, each layer was then identified using library searching.

Figure 2. Multi layer film. The area map of the cross section was collected using 532 nm excitation. Each layer was determined through the use of spectral correlation, each layer was then identified using library searching.

Table 3. Percentage of sample area covered by polystyrene and single-walled carbon nanotubes.

Polystyrene Single Walled Carbon Nanotubes
Total feature area (sq microns) 5357.29 358.16
Total Image area (sq microns) 14177.17 6837.39
Feature percentage (%) 37.79 5.24

Chemical map of the sample, with the red, yellow, and green areas representing polystyrene regions. Image analysis of the chemical map images with colored areas representing the polystyrene and carbon nanotube distribution on the sample surface.

Figure 3. Chemical map of the sample, with the red, yellow, and green areas representing polystyrene regions. Image analysis of the chemical map images with colored areas representing the polystyrene and carbon nanotube distribution on the sample surface.

It is possible to use vibrational spectroscopy for tissue analysis. A video image of a thin cross section cross section of healthy rat brain tissue is shown in Figure 4. It is also possible to use these images to determine growth of malignant tissue. Baseline correction of these data was done. As the individual spectra were of high quality, no derivatives were taken. Further processing of the data was done using MCR and the RGB plot in Figure 4 showing the relative coverage of each component. Some non-brain tissue are seen in the figure. The red areas show some non-brain tissue in this figure. The green and blue components show different concentration of lipid in the tissue.

Analysis of healthy rat brain tissue. (A) Shows the second derivatives of the spectral components generated from the multivariate curve resolution. (B) Is the RGB plot displaying the relative coverage of the components seen in A along with the video capture of the sample. (C) Shows the relative coverage of the three components generated from the multivariate curve resolution.

Figure 4. Analysis of healthy rat brain tissue. (A) Shows the second derivatives of the spectral components generated from the multivariate curve resolution. (B) Is the RGB plot displaying the relative coverage of the components seen in A along with the video capture of the sample. (C) Shows the relative coverage of the three components generated from the multivariate curve resolution.

Conclusion

OMNIC Atlµs software is advanced image processing software, which is combined with the data collection of OMNIC. Rapid and easy identification and feature sizing of components are enabled while highly powerful sophisticated analysis such as Multivariate curve resolution and principal component analyses enable determining the exact location, quantification and identification of components.

These features enable rapid analysis of large chemical map data sets. Since it is possible to use intense analytical methods from within the OMNIC software, significant speeding up of the processing of large data sets is possible.

This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Materials & Structural Analysis.

For more information on this source, please visit Thermo Fisher Scientific – Materials & Structural Analysis.

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Comments

  1. Abdal Rahem Shtawa Abdal Rahem Shtawa United Kingdom says:

    HOW TO USE multivariate curve resolution

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