Raman Spectroscopy Applications in Hematological Analysis
Summary
Raman spectroscopy has emerged as a powerful, non-destructive analytical tool for probing the molecular composition of blood and its cellular components. By illuminating samples with monochromatic light and analysing the characteristic frequency shifts in scattered photons, this technique yields a distinctive vibrational fingerprint of haemoglobin, membrane lipids and intracellular proteins. In hematological analysis, Raman methods enable direct assessment of red and white blood cell biochemistry, detection of haemoglobinopathies, monitoring of oxygenation states and discrimination of blood types without the need for dyes or labels. Advances in instrumentation, such as surface-enhanced Raman spectroscopy (SERS) and optical tweezers, have significantly increased sensitivity, permitting single-cell investigations and rapid screening of complex fluids. Integration with microfluidics and artificial-intelligence algorithms now underpins high-throughput assays for transfusion medicine, disease diagnostics and therapeutic monitoring. Globally, these developments promise point-of-care applications in both resource-rich and resource-limited settings, offering new routes to early detection of anaemias, sickle-cell disorders and infectious agents that alter blood composition.
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Raman Spectroscopy Applications in Hematological Analysis publication trend
The graph below shows the total number of articles in raman spectroscopy applications in hematological analysis across all publications each year (not limited to Nature Index journals).
Technical terms
Raman spectroscopy: A laser-based technique that measures inelastic scattering of light to identify molecular vibrational modes.
Surface-enhanced Raman spectroscopy (SERS): A method that amplifies Raman signals via metallic nanostructures to detect low-abundance species.
Optical tweezers: A focused-laser trap used to immobilise single cells or particles for spectroscopic analysis.
Microfluidic flow cell: A device with microscopic channels that precisely controls fluid streams for high-throughput measurements.
Label-free: An analytical approach that does not rely on external dyes or markers to detect target molecules.
Machine learning classification: Computational algorithms that analyse spectral data to automatically assign biological categories, such as blood types.
References
- A Comprehensive Review on Raman Spectroscopy Applications. Chemosensors (2021).
- Raman Tweezers Spectroscopy of Live, Single Red and White Blood Cells. PLOS ONE (2010).
- Label‐Free Blood Typing by Raman Spectroscopy and Artificial Intelligence. Advanced Materials Technologies (2023).
- Trends in biomedical analysis of red blood cells – Raman spectroscopy against other spectroscopic, microscopic and classical techniques. TrAC Trends in Analytical Chemistry (2022).
- Flow cell for high throughput Raman spectroscopy of non-transparent solutions. Lab on a Chip (2024).
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