Background
Fourier Transform-Infrared Spectroscopy (FTIR) is an analytical method that employs two distinct yet depended techniques. Infrared spectroscopy entails use of the infrared region of the electromagnetic spectrum in identification and study of matter. A mathematically derived algorithm called the Fourier Transform is then used to convert the raw data into a spectrum (Banwell & McCash 1994, p.35). Initially compound analysis utilized dispersive techniques, which were laboriously sluggish in scanning the whole IR spectrum. As such, the need to develop a method that would simultaneously scan all the IR with reasonable speed resulted in the discovery of Fourier Transform-Infrared Spectroscopy.
Principle of operation
The use of Infrared spectroscopy is based on the fact that, each compound is made up of different bonds that vibrate in a unique manner specific to that compound, producing a spectrum that is specific to that compound. The spectrum is like a fingerprint as such its specific to that compound and it can be used in its identification purposes since no two compounds can have a similar spectrum just the way no two people can share a fingerprint (Griffiths & Hasseth 2007, p.120).
The basic set up of a FTIR system is composed of a source of Infra red radiation, an encoding system called the Interferometer, a detector and a computer system for decoding the signal. Initially, the infrared source produces an infrared beam composed of a myriad of frequencies. The beam is then channelled to the Interferometer, which has two mirror systems that are involved in splitting the beam into two distinct beams.
In the Interferometer, the two beams are then fused into one signal that emerges from it. This signal is called the Interferogram and it contains information on each infrared frequency emanating from the infrared source. As such, the measurement of the Interferogram translates into measurement of all infrared frequencies concurrently leading to speedy measurements. The resultant beam is then projected onto the sample, which in turn emits infrared signals that are specific to it. Since the analyser cannot directly deduce these signals, they are channelled to a decoding system that produces a readable spectrum (White 1990, p.55).
This decoding system entails use of a mathematical algorithm by a computer to generate human interface data in form of spectra that can be interpreted. This technique is called Fourier Transformation. The information obtained from the spectra can be used in rapid detection and identification of industrially relevant microorganisms.
Factors that contribute to sensitivity
The sensitivity of FTIR arises from two sources. First in FTIR, there is a shorter scan time because of simultaneous converging of all wavelengths. This factor is called Fellgett’s advantage. Secondly, another factor called Jacquinot’s advantage contributes to sensitivity of FTIR. This entails unrestricted passage of many wavelengths over a given period (Nishikida, Nishio & Hannah 1995, p.63)
Factors that contribute to specificity
Specificity of FTIR arises from the inversion of the results such that there is increased resolution between two close wavelengths that enables accurate differentiation of two closely related compounds.
Advantages and disadvantages
FTIR is widely used because of some advantages it posses over other analytical techniques. FTIR is a non-destructive analytical method with high sensitivity. Furthermore, results obtained by FTIR do not require any external endorsement. The whole system is easier to maintain since it has only one component that moves. FTIR is a fast method that allows of scanning several wavelengths in seconds with the highest accuracy (Connes advantage). The main disadvantage of FTIR is its inability to utilize complex electronic filtering techniques in signal-to-noise ratio reduction (Rohman, Che Ismail & Puziah 2011, p.735).
List of References
Banwell, C., & McCash, M., 1994. Fundamentals of molecular spectroscopy, McGraw-Hill: New York.
Griffiths, P., & Hasseth, J., 2007. Fourier transform infrared spectrometry 2nd ed. Wiley-Blackwell: New Jersey.
Nishikida, K., Nishio, E., & Hannah, W., 1995. Selected applications of FT-IR techniques. Gordon and Breach: New York.
Rohman, A., Che, Y., Ismail, A., & Puziah, H., 2011. FTIR spectroscopy combined with multivariate calibration for analysis of cod liver oil in binary mixture with corn oil. International Food Research Journal, 18(1), pp.736-740.
White, R., 1990. Chromatography/Fourier transform infrared spectroscopy and its applications. Marcel Dekker: New York.