Imaging and Identification of Materials Using FT-IR Spectroscopy

IR spectroscopy allows studying large sample areas with very high local resolution. A new detection system examines all points on the image simultaneously, ensuring average measuring time of only a few minutes. Moreover, since a complete IR spectrum is obtained for every image point, the information content is very high. IR imaging has become a standard procedure for analyzing tissues and single cells in biomedical applications and for studying polymer mixtures in chemical applications. The infrared or heat radiation falls at the longer wavelength end of the visible spectrum.

Oppositely to UV/VIS spectroscopy, which measures electronic transitions, infrared (IR) spectroscopy determines the vibration and rotation of molecules as absorption phenomena. The functional groups of organic molecules show characteristic vibrations which can be directly associated to specific absorption bands. The high information content of the method and its suitability for a large range of applications are responsible for the success of IR spectroscopy.

Principle of the Method

IR spectroscopy has been utilized along with traditional microscopy for more than a quarter of a century. From the visible image, the interesting areas of the object under scrutiny can be located and the (bio-) chemical composition studied spectroscopically. The method does not require additional preparations such as staining or chemical modification of the sample. Only the layer thickness needs to be adjusted to the IR radiation penetration depth. Till now, to enhance the local resolution, field stops were utilized to limit the ‘field of vision’ of the infrared beam to the interesting areas of the sample.

In order to determine large sample areas with high spatial resolution, the sample was moved step by step past the field stop using a motorized sample support, with the spectra of the different areas taken one after the other. This traditional procedure, often referred to as ‘mapping’, was very time consuming. For instance, the assessment of a 0.5 x 0.5 mm2 sample area with a 15 μm resolution would take about 10 h.

This considerably restricted the use of conventional IR imaging. Especially for biological samples such as tissue or cells, it is practically very difficult to ensure that the conditions of the sample (e.g. temperature, degree of hydration etc.) remain constant throughout the entire period of determination. To solve this problem, multi-element detectors are used for the new IR imaging technique. The surface of these detectors consists of a square grid of detector elements. This enables the simultaneous measurement of large surface areas as opposed to a step by step mapping. Using this new detection system, it has also been possible to improve the spatial resolution up to the diffraction limit, since field stops are no longer required. The technique enables simultaneous characterisation of areas of 340 x 340 μm2 with a resolution of 2.7 μm. As per conventional IR microscopy, transmission, reflection and attenuated total reflection (ATR) are available modes of measurement.

IR imaging of a sample of human skin tissue. Middle panel: transverse section (250 x 250 μm2; 15μm), measured using a Bruker HYPERION (8 cm-1 resolution, 15x, transmission). Upper panel: integrated signal intensity in the CH stretch vibration range (3000 – 2800 cm-1) = lipid distribution. Lower panel: integrated signal intensity in amide range (1720 – 1480 cm-1) = protein distribution.

IR imaging of a sample of human skin tissue. Middle panel: transverse section (250 x 250 μm2; 15μm), measured using a Bruker HYPERION (8 cm-1 resolution, 15x, transmission). Upper panel: integrated signal intensity in the CH stretch vibration range (3000 – 2800 cm-1) = lipid distribution. Lower panel: integrated signal intensity in amide range (1720 – 1480 cm-1) = protein distribution.

Figure 1. IR imaging of a sample of human skin tissue. Middle panel: transverse section (250 x 250 μm2; 15μm), measured using a Bruker HYPERION (8 cm-1 resolution, 15x, transmission). Upper panel: integrated signal intensity in the CH stretch vibration range (3000 – 2800 cm-1) = lipid distribution. Lower panel: integrated signal intensity in amide range (1720 – 1480 cm-1) = protein distribution.

Applications

For each measurement, which takes about two minutes, a complete IR absorption spectrum is obtained for each image pixel (number = number of detector elements). In contrast to VIS and fluorescence microscopy, which result in only one data point per image point, between 300 and 600 data points per image pixel are available, according to the spectral resolution specified. The obtained data is then selectively processed so that the information can be displayed in the form of two- and three-dimensional images.

For example, the signal intensities of certain IR frequencies, which can be clearly assigned to a functional group, are plotted across the sample surface (chemical mapping). IR imaging has already been established as an investigation technique for animal and vegetable tissues. As an example, Figure 1 shows the imaging of a sample of human skin. For the selected area (250 x 250 μm2), the visible image of a transverse section (15 μm) is shown in the middle. The upper image is a false-color image of the intensity determined in the C-H stretch vibration range (3000 – 2800 cm-1) and in which the distribution of the lipids is recognizable.

To illustrate the protein distribution within the tissue, the signal intensity in the amide range used to detect proteins (1720 – 1480 cm-1) is shown over the sample (Fig. 1, lower panel). The biochemical composition of tissues can be studied in a non-destructive manner. This method, therefore, holds great potential particularly for the diagnosis of cancer. Often, however, the absorption bands of various functional groups are superimposed hindering the correlation of the spectral properties with the biochemical composition of the sample. In such cases, modern multivariate techniques can be used for data reduction.

To illustrate this, Figure 2 shows the analysis of microbeads. Such polystyrene beads are used in combinatorial chemistry for solid phase synthesis. Although these beads have been specifically chemically modified at the surface, the visual image shows no difference. The beads were taken from 4 successive steps of a synthesis reaction. The spectra of a number of representative image points in Figure 2b show that the spectral differences between the various modified beads are very small. Principal component analysis as a multivariate data processing tool was therefore applied for evaluation. This method enables a wide range of dependent variables to be converted into a small number of independent variables, or principal components.

Figure 2c shows, in false colors, the similarities of the original spectra to three principal components. They offer relevant spectral information, enabling to differentiate the beads. If the three images are combined into a red-green-blue (RGB) image, the different beads are seen with their own specific colors and the chemical differences become obvious.

IR image of polystyrene beads (8cm-1 resolution, 15x, transmission). 2a: Beads look very similar in the visible image. 2b: Representative IR spectra for a number of detector pixels. 2c: Similarity of the original spectra to the three relevant principal components; 2d: RGB image made up of the three relevant principal components.

IR image of polystyrene beads (8cm-1 resolution, 15x, transmission). 2a: Beads look very similar in the visible image. 2b: Representative IR spectra for a number of detector pixels. 2c: Similarity of the original spectra to the three relevant principal components; 2d: RGB image made up of the three relevant principal components.

IR image of polystyrene beads (8cm-1 resolution, 15x, transmission). 2a: Beads look very similar in the visible image. 2b: Representative IR spectra for a number of detector pixels. 2c: Similarity of the original spectra to the three relevant principal components; 2d: RGB image made up of the three relevant principal components.

IR image of polystyrene beads (8cm-1 resolution, 15x, transmission). 2a: Beads look very similar in the visible image. 2b: Representative IR spectra for a number of detector pixels. 2c: Similarity of the original spectra to the three relevant principal components; 2d: RGB image made up of the three relevant principal components.

Figure 2. IR image of polystyrene beads (8cm-1 resolution, 15x, transmission). 2a: Beads look very similar in the visible image. 2b: Representative IR spectra for a number of detector pixels. 2c: Similarity of the original spectra to the three relevant principal components; 2d: RGB image made up of the three relevant principal components.

This information has been sourced, reviewed and adapted from materials provided by Bruker Optics.

For more information on this source, please visit Bruker Optics.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Bruker Optics. (2019, August 28). Imaging and Identification of Materials Using FT-IR Spectroscopy. AZoM. Retrieved on October 22, 2019 from https://www.azom.com/article.aspx?ArticleID=5956.

  • MLA

    Bruker Optics. "Imaging and Identification of Materials Using FT-IR Spectroscopy". AZoM. 22 October 2019. <https://www.azom.com/article.aspx?ArticleID=5956>.

  • Chicago

    Bruker Optics. "Imaging and Identification of Materials Using FT-IR Spectroscopy". AZoM. https://www.azom.com/article.aspx?ArticleID=5956. (accessed October 22, 2019).

  • Harvard

    Bruker Optics. 2019. Imaging and Identification of Materials Using FT-IR Spectroscopy. AZoM, viewed 22 October 2019, https://www.azom.com/article.aspx?ArticleID=5956.

Ask A Question

Do you have a question you'd like to ask regarding this article?

Leave your feedback
Submit