Near-infrared spectroscopy: Comparison of techniques

Significance of the Topic

Near-infrared spectroscopy (NIRS) plays a crucial role in both research and industrial settings for the rapid identification of raw materials and the quantification of chemical components. Its non-destructive nature, minimal sample preparation, and compatibility with online process monitoring make it indispensable for quality assurance, process control, and raw material verification across pharmaceuticals, food, agriculture, and chemical industries.

Objectives and Overview of the Study

This white paper aims to clarify the experimental differences and similarities between dispersive and Fourier transform (FT) NIR spectrometers. It reviews their underlying physical principles, instrument design, and key performance parameters—wavelength range, spectral resolution, wavelength accuracy, acquisition speed, and signal-to-noise (S/N) ratio—to guide the selection of the optimal technique for specific applications.

Methodology and Instrumentation

The two NIR approaches differ fundamentally in how they isolate and detect wavelengths:

• Dispersive spectrometers employ a polychromatic light source, a diffraction grating, and precision exit slits. The grating angle is varied by a motor and digital encoder to select each wavelength sequentially. This produces independent absorbance measurements with constant noise characteristics.
• Fourier transform spectrometers use a Michelson interferometer with a beam splitter, a fixed mirror, and a movable mirror. The interference pattern (interferogram) generated by varying the mirror path difference is converted to a spectrum via Fourier transformation. A reference laser tracks mirror displacement for wavelength precision.

Instrumentation Used

The study compares:

• Metrohm NIRS XDS and DS2500 dispersive spectrometers featuring off-axis digital synchronous monochromators, a fixed 8.75 nm resolution, and a spectral range from 400 to 2,500 nm without desiccant requirements.
• An FT-NIR instrument equipped with a Michelson interferometer, typical resolutions of 8 and 16 cm⁻¹, and humidity control via desiccants to protect sensitive optics.

Main Results and Discussion

Key performance comparisons reveal:

• Wavelength Range: Dispersive systems cover 400–2,500 nm, including the visible region; FT-NIR typically spans 800–2,500 nm due to optical material limits.
• Resolution: Dispersive instruments offer a stable ~8.75 nm resolution tuned by slit width and grating; FT resolution is adjustable via mirror stroke (Connes’ advantage), but higher resolution substantially increases noise.
• Wavelength Accuracy and Precision: Both techniques achieve sub-nanometer accuracy (<0.2 nm) using digital encoders (dispersive) or water-vapor calibration (FT).
• Signal-to-Noise Ratio: Dispersive systems maintain a nearly constant, low noise level across the spectrum, yielding 2–60× higher S/N ratios compared to FT, whose noise rises near spectral limits.
• Acquisition Speed: Both technologies can acquire full spectra in under one second, supporting real-time analysis.
• Robustness: Dispersive units are less sensitive to vibration and require minimal maintenance. FT systems need periodic desiccant replacement and laser upkeep and may suffer phase errors without expert user intervention.

Benefits and Practical Applications of the Method

Dispersive NIR spectrometers are particularly well suited for quantitative analysis, spectral library development, and high-throughput screening due to their high S/N, wide wavelength coverage, and operational robustness. FT-NIR remains valuable for applications requiring variable resolution and high throughput in stable laboratory environments, especially in mid-infrared research contexts.

Future Trends and Potential Applications

Emerging directions include:

• Integration of advanced detectors and compact dispersive modules for inline and at-line process analytics.
• Hybrid instrument designs combining dispersive and FT elements to balance resolution and noise.
• Enhanced chemometric modeling and artificial intelligence algorithms for automated quality control and predictive maintenance.
• Expansion of spectral libraries through high-quality measurements enabled by superior S/N ratios.

Conclusion

Both dispersive and FT-NIR spectrometers have reached a mature state of performance. The choice between them should be driven by specific application needs: spectral range, resolution versus noise, speed, instrument robustness, maintenance demands, and overall cost of ownership. Dispersive systems excel in routine quantitative and process-monitoring tasks, while FT systems offer flexibility in resolution and are established tools in fundamental research.

Reference

1. Bertrand D., et al., Infrared Spectroscopy and Its Analytical Applications, Tec&Doc, 2000.
2. Shaw R.A. and Mantsch H.H., Near-IR Spectrometers, in Encyclopedia of Spectroscopy and Spectrometry, Academic Press, 1999.
3. Meyer T., et al., Suppression of Mechanical Noise and Optimal Resolution in FT-NIR, NIR News, 2006.
4. Kolomiets O., et al., Influence of Spectral Resolution on Quantitative NIR Analysis, Journal of Near Infrared Spectroscopy, 2004.
5. FOSS, NIR Spectrometer Technology Comparison, White Paper, 2013.
6. Ciurczak E., et al., Examination of NIR Spectrometers: Dispersive vs. Interferometric, Amer. Pharm. Rev., 2008.
7. Voigtman E., The Multiplex Disadvantage and Low-Frequency Noise, Applied Spectroscopy, 1987.
8. Grandmont F., Development of an FT Spectrometer for Astronomy, Doctoral Dissertation, Université Laval, 2006.
9. Reeves J.B. and Zapf C.M., Discriminant Analysis of Food Ingredients by NIR, J. Near Infrared Spectroscopy, 1997.
10. Cozzolino D., et al., Visible and NIR Reflectance to Predict Pork Muscle Colour, LWT Food Science and Technology, 2003.
11. Armstrong P.R., et al., Comparison of Dispersive and FT NIR Instruments for Grain and Flour, Applied Engineering in Agriculture, 2006.
12. Kazeminy A., et al., Method Development on Different NIR Spectrophotometers, Journal of Near Infrared Spectroscopy, 2009.
13. Mouazen A., NIR for Agricultural Materials: Instrument Comparison, Journal of Near Infrared Spectroscopy, 2005.
14. Chalus P., et al., Comparison of NIR Spectrometers for Active Ingredient Determination, Spectra Analyse, 2005.