IR and FTIR Spectroscopy: FTIR Analysis and FTIR Spectrometer Operation

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Due to its exceptional mix of sensitivity, flexibility, specificity, and resilience, Fourier transforms infrared (FTIR) spectroscopy is a tremendously popular technique today. It is now one of the most frequently used analytical instrumental techniques in science and can handle solid, liquid, and gaseous analytes. Even though FTIR has some known drawbacks, such as a relative intolerance of water and sensitivity to the physical characteristics of the analysis matrix, it is still widely used in a variety of fields, including the food and beverage industry, engineering, the environment, pharmaceuticals, biomass, and clinical settings.5 Appropriate instrumentation now comes in benchtop, handheld, and online real-time devices.

Describe IR spectroscopy.
Only a small portion of electromagnetic radiation’s much wider spectrum can be seen by the human eye (Figure 1). The ultraviolet (UV) part of the visible spectrum is on its high-energy side, and the infrared area is on its low-energy side (IR). The IR wavelength range that is most advantageous for the study of organic substances typically ranges from 2,500 to 16,000 nm. The term “molecular spectroscopy” refers to the far-, mid-, and near-IR (NIR) spectrum.

The inset of Figure 1 shows the sub-region of the electromagnetic spectrum that is characteristic of infrared spectroscopy techniques.

The study of how IR light interacts with matter, which has a wavenumber range of 12,800 to 10 cm1, is known as IR spectroscopy. In the past, it has been customary to refer to infrared radiation (IR) in terms of “wavenumber,” where any wavenumber is inversely proportional to its wavelength. As a result, a shorter wavelength will have a greater wavenumber, indicating that more waves may fit within a specific distance. Mid-IR is often defined as radiation between 4,000 and 500 cm1, while NIR is typically between 10,000 and 4,000 cm1. Far-IR is typically described as radiation between 500 and 20 cm1.

Depending on the molecular bonds between atoms and the kinds of atoms at the ends of the links, molecules absorb IR light at particular frequencies. Covalently bound atoms are excited vibrationally by photon energy in the IR region. Many people think of these covalent bonds as stiff springs that can stretch, bend, rotate, and scissors (Figure 2). When energy is absorbed by molecules from higher-energy mid-IR radiation, fundamental vibrations are excited, moving the molecules from the ground state to the first vibrational state. In contrast, NIR spectroscopy uses a variety of bands called “overtones” that are created from those fundamental vibrations. The reader is also pointed in the direction of helpful supplementary introductory materials offered by the Royal Society of Chemistry.

Figure 2: Animation demonstrating the 3-dimensional motions that molecules with atomic bonds can experience while being stimulated by IR light. The IR spectral absorbance bands that we see are caused by these motions. taken from YouTube at https://www.youtube.com/watch, with credit. v=0S bt3JI150

What does FTIR spectroscopy entail, and how does it differ from IR spectroscopy?
FTIR is produced from an interferogram as the raw signal, which is how it differs from IR in that regard. This depicts the light intensity as a function of a mirror’s position inside the interferometer rather than a function of wavelength (as occurs in dispersive instruments). Here is the “FT.” The intensity as a function of wavenumber can only be produced after the signal has been Fourier-transformed (FT).


By convention, we consider FTIR to operate in the mid-IR band when we discuss it. FT equipment is nevertheless accessible for both UV and NIR spectral types. Although FTIR and FT-NIR are potentially complementary techniques, the analyst typically has to choose which to employ for a given application, so it is important to take into account their respective advantages and disadvantages.


FTIR spectra can be acquired significantly more quickly than with traditional dispersive devices. As the wavelength scale is calibrated using a very exact reference laser, the FT approach gives higher wavelength precision than IR and yields spectra that exhibit a far better signal-to-noise ratio.

How is FTIR put to use?
Midway through the 1940s, infrared spectrophotometers were created. At first, the majority of their uses were restricted to petrochemical research on organic molecules. The initial tools were dispersive scanning spectrophotometers, which were sluggish (Figure 3). Due to their ability to be more easily shrunk and produced at much lower costs to create small, palm-sized packages with straightforward operating systems run on mobile phones, dispersive instruments are still in use today.

Figure 3: Schematic illustration of the dispersive IR spectrophotometer’s layout

The majority of mid-IR instruments used today for research and development are of the FT type. They can be dated to the 1890s and the work of Albert Michelson, who created the “interferometer,” for which he won the Nobel Prize while researching the speed of light. An interferometer is used by an FTIR instrument, and it comprises a source, a beam splitter, two mirrors, a laser, and a detector (Figure 4). The beam splitter divides the energy coming from the source into two portions. The other is reflected onto a stationary mirror, while one half is transferred to a moving mirror. The response of the calibrating laser causes the moving mirror to go back and forth steadily. The interference pattern is transmitted through the sample compartment—and, if present, the sample where absorption occurs—to the detector after the two beams are reflected from the mirrors and mixed at the beam splitter once more. The FT function is then applied to this signal to produce a spectrum.

The FT instrument generates the ensuing interference waveform, referred to as an “interferogram” (explained below), and encodes all the data across all the detected wavelengths. However, the signal must first go through a computationally demanding Fourier transform mathematical function in order to produce an interpretable spectrum. The “rapid Fourier transform,” sometimes known as the FFT, was invented in 1966 as a result of the development of the Coey-Tukey algorithm (6, 7). This enabled the launch of the first commercial FTIR, the FTS-14, in 1968, along with the development of the first commercial computing systems (Figure 5).

Figure 4: Schematic diagram showing the working principle of an interferometer.

Figure 5 is a schematic diagram illustrating how an FTIR spectrometer operates. The steps of the FTIR analysis described below are denoted by numbers.

The analysis with an FTIR goes like this:

The source: a luminous black-body source emits infrared light, and the beam passes through an aperture that regulates the energy output.
The interferometer: The IR beam enters the interferometer, where, as previously mentioned, “spectral encoding” occurs. A reference laser is used by the interferometer to calibrate the wavelengths precisely.
The sample: The IR beam enters the sample compartment and passes through or is reflected off the sample’s surface. The sample absorbs particular frequencies of energy that are specifically unique to the sample.
The beam ultimately travels to the detector, where it is measured to completion.
The signal is digitalized by the computer, the FFT computation is performed, and the user is then shown the finished infrared spectrum.
FTIR analysis and FTIR data collection
Today’s wide variety of FTIR equipment and adaptable, replaceable attachments enable the analysis of gaseous, liquid, and solid samples of various sizes and forms using a single basic instrument. All instrument optics and sampling attachments must be made from other acceptable IR optical materials because regular glass is not mid-IR transmissible. The analyte had to be ground and combined with IR-transmissible substrates, frequently potassium bromide (KBr), under high pressure to form a tiny solid transparent disc in the early procedures established for solid samples. To measure transmission, these were then attached to a holder. Liquid (non-water-containing) samples were frequently created as thin films with a short spacer between two of these IR-transmissible discs. This approach had problems with both time and reproducibility.

The acceptance of alternatives has increased over the past 30 years, particularly the now-ubiquitous “ATR” strategies (attenuated total reflectance). A spectrum can be obtained in a matter of seconds with this device, which can handle small volumes of liquid or solid sample deposited onto a crystal window without the need for significant sample preparation. When solids are evaluated, a top-fixing clamp firmly presses them into the crystal window (Figure 6). This type of gadget is presently used in the majority of reported applications for solid samples. For particular applications, other types of devices are also available, such as a reflecting hemisphere for diffuse reflectance or gas sample-sealed cells. Even 96-position microtiter size plates manufactured of IR-compatible materials like gold are available, enabling high-throughput screening with uniquely designed FTIR accessory equipment.

Using an ATR sampling attachment is shown in Figure 6.

It is first necessary to gather a background “blank” spectrum in a typical mode of operation (Figure 7). The absorbance values from the whole light beam path will be included in this (optics and atmospheric). The spectral responses specific to the sample alone are then obtained by analysing the sample and subtracting the blank spectrum from it. Co-added scans (usually 8 to 64) and wavenumber resolution (generally 4 to 16 cm1) both require application-specific adjustment to produce a good signal-to-noise ratio. With co-adding scans and background spectrum subtraction, analytical operations for a single sample on an ATR device can be completed in less than 2 minutes. Individual scans are quick, often taking less than 1 second on contemporary instruments. For measuring 100s or 1000s of samples in manufacturing or screening applications, such as metabolomic fingerprinting, this makes FTIR-ATR particularly useful.

Figure 7 shows the process for creating a typical spectrum.


Understanding the meaning of an IR spectrum and an FTIR spectrum
FTIR spectra provide a wealth of information, but because of this, using and interpreting them can be difficult. Here is a helpful introduction to FTIR interpretation. Vanillin (Figure 8) is an example of a simple, pure, single-compound sample with several peaks in its spectrum. In these situations, library matching methods to an authenticated standard may be able to locate it in a mixture of a single component, but this would be unfeasible in mixes of many different molecules. The issue is that the same multiple absorption peaks overlap between compounds since the majority of organics comprise combinations of carbon (C), hydrogen (H), nitrogen (N), or oxygen (O) atoms on single or double bonds. Even for a seasoned analyst, simply trying to “eyeball” a large number of spectra to determine whether or how the samples they came from are different in any manner quickly becomes an overwhelming challenge. To solve this issue, FTIR data is typically combined with statistical modelling techniques like multivariate analysis (MVA), sometimes known as “chemometrics” in the context of chemistry.

Figure 8 shows the vanillin FTIR spectrum with the significant peak wavenumbers highlighted. Wavenumber (cm1) is plotted against absorbance (Y axis) on the X axis. Author credit.

When using MVA approaches, which essentially only call for numerous spectra to be collected from each sample and assembled into a single data matrix, FTIR spectral data is particularly receptive. The full spectrum of each sample is represented in each table row, and the aligned absorbance for certain successive wavenumbers across all samples is represented in each column. In this format, methods like principle components analysis (PCA) can be used to effectively investigate and illustrate any potential class-based correlations between the spectral responses of various sample groups. It is exceedingly challenging to determine sample differences by merely superimposing the spectra from various sample classes, yet doing so provides an immediate “interpretability” of sample differences. Figure 9 illustrates a PCA score plot as an illustration of how to differentiate between raw and treated bio-oil samples.

Figure 9: Bio-oil samples comparing unprocessed (green) and treated (red) material. Shown are the resulting PCA scores plot and the FTIR spectra (left and right, respectively). Author credit.

Quantitative value-based calibration predictions for properties like chemical concentrations can be built using MVA approaches, such as partial least-squares regression, when quantitation is the goal (e.g., concentration values). These approaches use information previously gathered on each sample’s composition from other assay techniques. The algorithmic approach uses these known values to select the spectral properties that most closely match the relevant external value. This latter strategy is highly favoured because, when well designed and confirmed, it can successfully enable FTIR to replace wet chemistry assays for new, unidentified samples (of the same types), saving time and money. One benefit of creating MVA models is that the most significant wavelength used in the model’s construction may be recognised using the statistical output table and sample plots that are produced. It is frequently possible to deduce some immediate chemical conclusions from this data.

FTIR is almost universally thought to be “mid”-FTIR. This wide spectrum cannot be produced by non-FT mid-IR dispersive sensors due to their sluggish scan rate and weaker power (signal-to-noise ratio). But because NIR has so much more energy, a non-FTIR dispersive device can yield spectra that are comparable to those of mid-FTIR instruments. However, because it would take longer, the resolution (as measured by the number of real wavenumbers) would often be lower.

IR spectrum diagram
The bands that the major functional groups produce are depicted in the graph below (Figure 10). (1500 cm-1 and above). The fingerprint area, which lies in the mid-IR region between 500 and 1500 cm-1, offers molecular fingerprints peculiar to particular compounds that cannot be imitated.

Figure 10: Diagram displaying the primary functional groups’ IR bands and the region that serves as each compound’s unique fingerprint.


Benefits, drawbacks, and applications of mid-IR vs. near-IR/FTIR spectroscopy
Mid-IR spectra have sharper and more defined spectral absorption bands (Figure 11) for organic species, which makes the technique useful for structural elucidation and compound identification. The shape and structure of mid-IR spectra and NIR spectra are very distinct from one another. Additionally, extensive data tables of typical molecular function group wavenumber regions have been compiled and published over time, many of them in relation to particular application fields. With only a small amount of sample material, excellent spectra can be obtained due to the significant mid-IR absorption of organic molecules (e.g., a few powder grains). Its resistance to water is merely one of its drawbacks (which quenches the IR signal even when present at just a few percent). The resultant spectrum will also only be from a few microns of sample penetration and will only indicate limited homogeny because organics typically absorb mid-IR so strongly. As a result, far more meticulous sample preparation or analytical replication is required.


One benefit of NIR is that it responds strongly to the sample’s chemical and physical characteristics (e.g., making it useful for overall sample grading applications). Increased sampling volume may boost sensitivity, obtain greater homogeny, and require much less sample preparation for a measurement since NIR light achieves considerably more sample penetration because it is only poorly absorbed. Chemical specificity is linked to the main drawbacks of NIR. The majority of NIR molecular reactions are first-order (or higher) overtones, which exhibit some signal overlap and may reduce the ability to discriminate. Mid-IR and NIR devices are both commonly utilised for various applications, however what is regarded an advantage vs a disadvantage depends largely on the application.

Figure 11 shows an illustration of typical NIR and mid-IR spectra. Notably, the mid-IR resolves extra spectral characteristics. In order to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis, samples were dried mosquitoes. Reproduced with permission from Mwanga, EP; Mapua, SA; and Siria, DJ. according to the Creative Commons Attribution 4.0 International License, et al.


Applications of FTIR in the Present and Future
Regarding the price of the equipment, its simplicity of use, and the information it can produce, FTIR occupies a very special “sweet spot.” Due to its adaptability, it has been used in more contexts than this article has space to cover. There are numerous pharmaceutical and medicinal applications that are becoming more prevalent.


In addition, the emergence and development of chemical imaging based on FTIR video chips (focal place arrays, or “FPA”) for microscope use during the past 20 years has been a particularly fascinating technical development. Even though typical FTIR microscopes have been in use since the 1970s, they only feature a single-point IR detector, making imaging only possible via stitching together a significant number of the single spatial observations. It would take hours to gather such photos, even of something tiny like a cm-2 piece of sliced tissue. The size of common 128 × 128 pixel arrays on modern mid-infrared imaging circuits, though higher resolutions are also possible, have now become the norm. In one scan, a 128×128 array generates 16,000 spatially resolved spectra. FTIR-FPA can be applied to situations where chemical signals need to be understood within a broad spatial context, such as in forensics, archaeological artefacts, physical contaminants like microplastics, pharmaceutical pressed tablet testing, and tissue biopsy screening for disease state and diagnostic prediction.13, 14 FTIR-FPA has become widely used, and images can be acquired in under 30–60 seconds.

References

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4. Morris P, Thain SC, Allison GG, et al. quantified lignin and hydroxycinnamic acids in perennial fodder and energy grasses using partial least squares regression and Fourier-transform infrared spectroscopy. Biology Technology 2009;100(3):1252-1261. doi:10.1016/j.biortech.2008.07.043

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