Advancing the Frontiers of Molecular Spectroscopy

Raman spectroscopy is one of the most talked-about techniques in spectroscopy for good reason. Because of this technique’s nondestructive nature and overall versatility, Raman spectroscopy has been useful for several key application areas, including biomedical, pharmaceutical, food and beverage, oil and gas, and environmental analysis.

In this Q&A overview, we explore how Raman spectroscopy is being explored today, and some of the most important trends and developments involving the technique that researchers should be aware of.

Raman spectroscopy has been a cornerstone of analytical science for decades. What are some of the most significant recent shifts in how the technology is applied today?

One of the largest shifts that we are currently seeing in Raman spectroscopy is the move away from single-technique analysis. In its place, multimodal Raman microscopy is becoming a larger part of the typical analytical workflow. This indicates a growing mindset in the spectroscopy community about the general lack of satisfaction with only a basic understanding the chemical composition of samples. Instead, researchers want to understand details of the chemistry, structure, and function simultaneously.¹ Modern platforms now integrate Raman spectroscopy with techniques like fluorescence lifetime imaging (FLIM), phosphorescence lifetime imaging (PLIM), and second harmonic generation (SHG).¹ This integration allows for richer data sets and better correlation without the need to relocate the same sample region across different instruments.¹ Furthermore, we are seeing Raman transition from a supportive tool to a central enabling technology in biopharmaceutical manufacturing, where it is used for real-time prediction of critical quality attributes and inline control of complex bioprocesses.²

A persistent challenge in biological Raman spectroscopy is autofluorescence. What new strategies are being used to overcome this?

Autofluorescence can often overwhelm the weak Raman signal in biological samples.³ Although traditional methods like switching to longer excitation wavelengths (such as 785 nm or 1064 nm) remain common, they have drawbacks like increased cost or reduced sensitivity.³ As a result, we are seeing how custom neural networks are being used to improve post-processing, which means that these tools are programmed to remove fluorescence and shot noise while preserving the Raman peak intensities.³ This allows for improved-quality data extraction from highly fluorescent samples like yeast fermentation products.³ Additionally, the use of confocal detection schemes in compact systems dramatically improves the Raman-to-background ratio by rejecting out-of-focus signals.³

Particle correlated Raman spectroscopy (PCRS) is mentioned as a major workflow advancement. How does it change particle analysis?

Traditionally, optical imaging and Raman microscopy were disconnected; one was used to size particles and the other to identify a small subset of them. PCRS has changed particle analysis by doing both simultaneously. PCRS integrates both optical imaging and Raman spectroscopy into a single, automated workflow that pairs high-resolution imaging with spatially correlated Raman microspectroscopy.⁴ This creates a particle-centric data structure where every individual particle’s image, morphological descriptors (like size, shape, and circularity), and chemical signature are permanently linked and traceable.⁴ This is critical for interpreting heterogeneous systems where morphology alone is insufficient to infer identity, such as differentiating between active ingredients and excipients in pharmaceutical nasal sprays.⁴

Beyond the laboratory, how is Raman spectroscopy being utilized in industrial and environmental sectors?

In the energy sector, Raman is replacing complex traditional methods like gas chromatography (GC) for Liquefied Natural Gas (LNG) custody transfer. Unlike GC, which requires a problematic vaporizer stage, Raman probes can be inserted directly into the cryogenic LNG stream for in-situ, real-time analysis.⁵ This provides a snapshot of the composition in less than 10 seconds, which is vital for verifying calorific value during the transfer of high-value loads.⁵ In environmental science, Raman is being enhanced with machine learning (ML) to improve the detection and identification of microplastics in aquatic samples, supporting more accurate source attribution.^5,6

Are there any notable advancements in how Raman is used to characterize materials we use every day, like glass or carbon?

Yes, Raman has been instrumental in the development of modern precision materials. For two recent examples, it is used to guide the engineering of Gorilla Glass by documenting the diffusion of potassium ions during the ion-exchange toughening process.⁷ It is also being applied to cutting-edge challenges like nuclear waste vitrification, where materials engineers use Raman to optimize glass formulas for the long-term, impermeable storage of radioactive isotopes.⁷

References

1. Flack, A. Why Researchers are Turning to Multimodal Raman Microscopy. Spectroscopy 2026, 41 (3), 25. Available at: https://www.spectroscopyonline.com/view/rms1000-multimodal-confocal-raman-microscope-one-instrument-infinite-possibilities
2. Workman, Jr., J. Top 10 Most Influential Articles on Raman Spectroscopy in Biopharmaceutical Applications during 2023–2025. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/top-10-most-influential-articles-on-raman-spectroscopy-in-biopharmaceutical-applications-during-2023-2025 (Accessed June 15^th, 2026).
3. Bingemann, D.; Creasey, D. Improving Raman Spectral Quality in Autofluorescent Biological Samples. Spectroscopy 2026, 41 (3), 42–48. Available at: https://www.spectroscopyonline.com/view/improving-raman-spectral-quality-in-autofluorescent-biological-samples 
4. Sestak, M. Particle Correlated Raman Spectroscopy (PCRS): A Workflow for Correlating Particle Morphology with Chemical Identification. Spectroscopy 2026, 41 (3), 8–14. Available at: https://www.spectroscopyonline.com/view/particle-correlated-raman-spectroscopy-pcrs-a-workflow-for-correlating-particle-morphology-with-chemical-identification
5. Garza, A.; Miller, S.; Sutherland, S. LNG: Adapting to a More Strategic Role Calls for Effective Quality Analysis. Spectroscopy 2026, 41 (3), 35.
6. Workman, Jr., J. Machine Learning Enhanced Raman Spectroscopy for Microplastics Detection in Environmental Samples: A Practical Tutorial. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/machine-learning-enhanced-raman-spectroscopy-for-microplastics-detection-in-environmental-samples-a-practical-tutorial (Accessed June 15^th, 2026).
7. Adar, F. Raman Spectra of Glass: Its Structure and Contemporary Uses. Spectroscopy 2026, 41 (3), 26–33. Available at: https://www.spectroscopyonline.com/view/raman-spectra-of-glass-its-structure-and-contemporary-uses