Advancements and Applications in FT-IR Spectroscopy

Fourier transform infrared (FT-IR) spectroscopy is an essential, non-destructive analytical tool that measures molecular vibrations to provide rapid qualitative and quantitative chemical characterization.^1,2 Today, advanced FT-IR methods and chemometrics solve complex challenges in pharmaceuticals, food science, environmental monitoring, and medicine, among other applications.

In this Q&A, we explore the advanced techniques, methods, and real-world applications of FT-IR spectroscopy.

What are the fundamental principles of FT-IR, and what advantages does it offer over older dispersive instruments?

FT-IR spectroscopy measures how polar covalent bonds (such as C=O, O–H, and N–H) absorb infrared (IR) light during vibrational transitions.² Instead of scanning wavelengths sequentially, an FT-IR spectrometer passes broadband light through a Michelson interferometer.² A moving mirror produces an interferogram encoding all frequencies simultaneously, which a fast Fourier transform (FFT) algorithm converts into a spectrum.²

This approach yields three major advantages over dispersive systems highlighted below:

1. Fellgett’s (multiplex) advantage: Measuring all wavelengths simultaneously boosts the signal-to-noise ratio and collection speed.²
2. Jacquinot’s (throughput) advantage: The absence of narrow slits delivers much higher energy throughput.²
3. Connes’ advantage: High-precision wavelength calibration is maintained using a stable internal laser reference.²

What are the primary sampling configurations used in modern FT-IR analysis?

Modern FT-IR instruments feature several new configurations tailored to specific sample matrices. These configurations include the following:

1. Transmission: Passes light directly through thin films, gas cells, or KBr pellets, requiring careful thickness control.^2,3
2. Attenuated Total Reflectance (ATR): Guides the IR beam through a crystal (like diamond or ZnSe) at the sample interface. With a 1–2 µm penetration depth, it allows direct, preparation-free analysis of solids, liquids, and gels.^2,4
3. Diffuse Reflectance (DRIFTS): Gathers scattered light from powders and rough surfaces, making it ideal for catalysts, soils, or asphalt binders.^1,2
4. Specular Reflection and Microspectroscopy (µ-FT-IR): Enables surface analyses of thin films and micro-scale particles like environmental microplastics.²

How is the food industry utilizing FT-IR to detect adulteration and verify origins?

We are also seeing FT-IR spectroscopy being combined with chemometric methods to aid in the detection of food adulteration, a growing challenge in the current global economy. There are clear benefits of this technological integration. For one, FT-IR paired with chemometrics enables high-throughput food screening and authentication. As an example, diffuse reflectance (DRIFTS) with support vector machine-discrimination analysis (SVM-DA) detects and quantifies low-cost cassia, black pepper, or clove in high-value cinnamon.¹ Another recent example is that benchtop FT-IR with data-driven soft independent modeling of class analogy (DD-SIMCA) identifies allergenic nutshells in cumin powder.¹

For origin authentication, there are a few recent examples that can be highlighted. In one example, ATR FT-IR with partial least squares discriminant analysis (PLS-DA) discriminated carrot varieties to verify Protected Geographical Indication specialties.¹ Similarly, FT-IR fingerprinting with linear discriminant analysis (LDA) clustered virgin olive oils from Morocco with up to 100% accuracy.¹ And finally, in dairy processing, portable FT-IR was recently used to analyze Turkish white cheese ripening by examining soluble fractions, predicting fatty acid and amino acid levels to replace chromatography.¹

Mixtures are complex. What advanced methods does FT-IR employ to resolve them?

Analyzing mixtures is challenging because traditional spectral subtraction often accumulates noise and fails during environmental shifts. Modern FT-IR overcomes this through two methods: spatial separation (IR microscopy) and spectral separation (multicomponent searching).

So how do these two methods work? In spatial separation, imaging microscopes collect spectral images at high resolution (25 µm).⁴ Software digitizes these images to calculate area percentages, enabling calibration-free, semiquantitative mapping of components in granular mixtures like pharmaceutical tablets.⁴ In spectral separation, programs like OMNIC Specta conduct cumulative searches of composite spectra without subtraction, accurately identifying constituents in additive gaseous or homogeneous solid mixtures.⁴

How is FT-IR used in clinical diagnostics and biophysics?

FT-IR is regularly used in clinical diagnostics and biophysics because of its ability to provide label-free, non-invasive screening of biofluids.^1,5–7 In diabetes research, FT-IR spectroscopy tracks red blood cell (RBC) membrane remodeling. Protein secondary structure is monitored via the amide I (1600–1700 cm⁻¹, C=O stretching) and amide II (1500–1600 cm⁻¹, N-H bending/C-N stretching) bands.⁵ Under diabetic stress, membrane proteins misfold, shifting from ordered α-helices to rigid β-sheets.⁵

To detect these subtle changes in whole cells without isolating membranes, researchers apply second-derivative processing to sharpen overlapping peaks and eliminate baseline drift.^5,6 Paired with orthogonal partial least squares discriminant analysis (oPLS-DA) and Variable Importance in Projection (VIP) scoring, these features establish biomarkers to stage diabetes progression in intact RBCs.⁶ In nanotechnology, transmission-mode FT-IR and second-derivative curve-fitting similarly track structural stability and protein intercalation in α-lactalbumin-infused nickel hydroxide nanoparticles.⁴³

What role does FT-IR play in environmental monitoring?

FT-IR spectroscopy has also been used for remote sensing of atmospheric greenhouse gases like carbon dioxide and methane in open-path or extractive setups.^1,2 Additionally, micro-FT-IR (µ-FT-IR) is a standardized tool to detect microplastics in water and sediment.^1,2 Coupling raw spectra with one-dimensional (1D) convolutional neural networks (CNNs) achieves up to 97.44% classification accuracy for environmental microplastics.¹

References

1. Workman, Jr., J. A Review of the Latest Research Applications Using FT-IR Spectroscopy. Spectrosc. Suppl. 2024, 39 (s8), 22–28. DOI: 10.56530/spectroscopy.ak9689m8
2. Workman, Jr., J. FT-IR Spectroscopy Mini-Tutorial: Principles, Practice, and Applications Across Disciplines. Spectroscopy 2026, 41 (1), 36–39. DOI: 10.56530/spectroscopy.gm5083p5
3. Selvaraj, E. J.; Lakshmanan, P. UV–FT-IR Studies on α-Lactalbumin-Infused Nickel Hydroxide Nanoparticles. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/uv-ft-ir-studies-on--lactalbumin-infused-nickel-hydroxide-nanoparticles (accessed September 1, 2026).
4. Bradley, M.; Izzia, F.; Nunn, S. Analysis of Mixtures by FT-IR: Spatial and Spectral Separation of Complex Samples. Spectrosc. Suppl. 2008, 23 (s8). Available at: https://www.spectroscopyonline.com/view/analysis-mixtures-ft-ir-spatial-and-spectral-separation-complex-samples
5. Marzac, K.; Perez-Guaita, D.; Wajda, A.; Wilk, A.; Wilkosz, N.; Chwiej, J.; Chodaczek, G.; Chasse, J. Sex- and Age-Dependent Red Blood Cell Remodeling in Type 2 Diabetes Revealed by FT-IR and Raman Spectroscopy: Part I. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/sex--and-age-dependent-red-blood-cell-remodeling-in-type-2-diabetes-revealed-by-ft-ir-and-raman-spectroscopy-part-i (accessed September 1, 2026).
6. Marzac, K.; Perez-Guaita, D.; Wajda, A.; Wilk, A.; Wilkosz, N.; Chwiej, J.; Chodaczek, G.; Chasse, J. Sex- and Age-Dependent Red Blood Cell Remodeling in Type 2 Diabetes Revealed by FT-IR and Raman Spectroscopy: Part II. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/sex--and-age-dependent-red-blood-cell-remodeling-in-type-2-diabetes-revealed-by-ft-ir-and-raman-spectroscopy-part-ii (accessed September 1, 2026).
7. Marzac, K.; Perez-Guaita, D.; Wajda, A.; Wilk, A.; Wilkosz, N.; Chwiej, J.; Chodaczek, G.; Chasse, J. Sex- and Age-Dependent Red Blood Cell Remodeling in Type 2 Diabetes Revealed by FT-IR and Raman Spectroscopy: Part III. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/sex--and-age-dependent-red-blood-cell-remodeling-in-type-2-diabetes-revealed-by-ft-ir-and-raman-spectroscopy-part-iii (accessed September 1, 2026).