Getting to the Bottom of Baseline: New Study Compares Approaches to Correcting Infrared Spectroscopy Artifacts

A recent study from the University of Wisconsin-Madison demonstrates how to best address baseline artifacts in infrared (IR) absorption spectroscopy, a widely used technique in chemical analysis. The study, conducted by Haruna Okada and Scott T. Sanders from the university's Department of Mechanical Engineering, compared two distinct methods—frequency-domain polynomial fitting and time-domain molecular free induction decay (m-FID)—to determine which approach is more effective in reducing baseline errors in IR spectra. The findings, published in the journal Applied Spectroscopy, emphasize the importance of choosing the right baseline correction approach for each specific application (1).

Infrared absorption spectroscopy is a powerful tool for analyzing chemical substances by measuring how much infrared radiation is absorbed by a sample at different frequencies. This allows researchers to infer chemical and physical properties and molecular structures of sample materials. However, baseline artifacts—distortions in the IR spectrum due to factors like instrument misalignment, light scattering of the sample, temperature fluctuations, or optical fouling—can lead to inaccuracies in measurement. Correcting these artifacts is crucial for reliable analysis, especially when working with complex mixtures or when making measurements under challenging operating or sampling conditions (1).

The study's main goal was to evaluate the effectiveness of the two baseline correction approaches across various scenarios. The researchers focused on different baseline complexities, noise levels, and spectral resolutions, using test mixtures containing up to 464 components. In the frequency-domain approach, a ninth-order polynomial was used to fit and correct the baseline artifacts, while the time-domain approach relied on transforming the spectrum into the time domain and then discarding the early portion of the signal to minimize baseline influence (1).

Read More: Baseline Correction in Raman Spectroscopy

The results showed that the time-domain approach generally yielded better results when dealing with complex baselines and low noise levels. However, as noise levels increased, the frequency-domain approach exhibited superior performance. Additionally, the frequency-domain approach proved more stable when spectral resolution was varied through peak broadening (1)

These findings have significant implications for researchers and industry professionals using infrared absorption spectroscopy. The choice between frequency-domain and time-domain baseline correction approaches depends on the specific application, baseline complexity, and noise levels (1). The study suggests that when working with high-noise environments or lower spectral resolutions, the frequency-domain approach might be more reliable. In contrast, for complex baselines with low noise levels, the time-domain approach could be the better option.

The authors emphasized that researchers should carefully consider their baseline correction approach and recommend numerical testing to identify the best method for a given application. They also noted that baseline correction methods might require further refinement depending on the specific mixture being analyzed and the environmental conditions.

Overall, the study contributes valuable insights into the ongoing quest to improve the accuracy and reliability of infrared absorption spectroscopy, a key technique in fields ranging from materials science to environmental analysis. By understanding the strengths and limitations of baseline correction methods, researchers can make better-informed decisions, ultimately leading to more accurate and efficient chemical analyses. Other recent papers demonstrate the strategies involved in baseline correction of vibrational spectra (2,3).

References

1. Okada, H.; Sanders, S. T. Comparison of Frequency-Domain and Time-Domain Baseline Correction Approaches for Infrared Absorption Spectroscopy of Mixtures Containing Up to 464 Components. Appl. Spectrosc. 2024, 78 (4), 376–386. DOI:10.1177/00037028241226989
2. Li, Y.; Wang, X.; Yu, H.; Du, W. Pattern-Coupled Baseline Correction Method for Near-Infrared Spectroscopy Multivariate Modeling. IEEE Trans. Instrum. Meas. 2023, 72, 1–9. DOI: 10.1109/TIM.2023.3265101
3. Jiao, Q.; Guo, X.; Liu, M.; Kong, L.; Hui, M.; Dong, L.; Zhao, Y. Deep Learning Baseline Correction Method via Multi-Scale Analysis and Regression. Chemom. Intell. Lab. Syst. 2023, 235, p.104779. DOI: 10.1016/j.chemolab.2023.104779