Comparative analysis of Signal-to-Noise ratio in Raman microscopy using conventional cuvette based measurements and Hollow-Core microstructured polymer optical fibers
Year: 2025
Type: article
Abstract: • Hollow-core polymer optical fibers enhance Raman signal-to-noise ratio. • Selectively filled medium fibers with 532 nm give the strongest Raman signals. • Non-selective filling offers SNR gains with easier fiber preparation. • Higher magnification increases Raman signals but reduces measurement stability. • The study links SNR to fiber geometry, filling method, and numerical aperture. Confocal Raman microscopy is a powerful tool for chemical analysis, but achieving a high signal-to-noise ratio (SNR) remains a challenge, especially for weak signals. In this study, we compare conventional cuvette-based measurements with those performed using hollow-core microstructured polymer optical fibers (mPOFs), using both selective and non-selective filling methods. Three types of mPOFs with different sizes were employed. Potassium ferricyanide, characterized by its distinct Raman band at 2140 cm –1 , and two laser wavelengths (532 nm and 785 nm) at three different magnifications were used to evaluate the performance and stability of each configuration. Our results show that mPOFs made of poly(methyl methacrylate) (PMMA) significantly improve SNR compared to traditional cuvette setups, with the selectively filled medium-size fiber providing the best performance. Furthermore, even non-selectively filled fibers, simply cleaved and immersed, achieve SNR enhancements over cuvette measurements without any specialized handling. Although higher magnifications in fiber setups improve light confinement and interaction volume, they can also introduce stability issues such as liquid evaporation, especially in smaller fibers. Analysis also confirms that Raman signal power in fiber-based systems depends on factors such as fiber diameter, length, and numerical aperture.
Source: Optical Fiber Technology
Institution: University of the Basque Country
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,360
OpenAlex ID: W4413851544