Swept-source Raman spectroscopy of chemical and biological materials

Abstract

Significance: Raman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system.

Aim: We developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system.

Approach: The SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842 nm), a bandpass filter with a bandwidth of 1.5 nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples.

Results: We developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to $1200{\mathrm{cm}}^{-1}$ were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue.

Conclusions: We demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.

Keywords: Raman spectroscopy; acetaminophen; biological tissue mapping; glucose; hydroxyapatite; low-noise silicon photoreceiver; narrow bandwidth filter; phenylalanine; signal processing; swept-source laser; swine tissue.

Fig. 1

Schematic diagram of the SS-Raman spectroscopy system. A wavelength-swept laser, ranging from 822 to 842 nm, coupled with a multimode optical fiber, was utilized. The sample was illuminated with converging lenses and a mirror. Raman signals were collected through a collection lens and detected by a photoreceiver. The Raman spectra were acquired during a single sweep using a narrow-bandwidth bandpass filter. BPF, bandpass filter; CL, collection lens.

Fig. 2

Signal processing in SS-Raman spectroscopy. (a) Raw Raman spectrum of phenylalanine powder shows degradation due to the unstable output of the light source and the use of filters with a large bandwidth. (b) Application of Gaussian filtering effectively eliminates high-frequency noise. (c) Deconvolution enhances the spectral resolution. (d) Polynomial background removal mitigates the background signal arising from the low optical density of the filter.

Fig. 3

Raman spectra ($\sim 900$ to $1200{\mathrm{cm}}^{-1}$) of the chemical samples. (a)–(d) Raman spectra of phenylalanine, hydroxyapatite, glucose, and acetaminophen, respectively, obtained through the SS-Raman spectroscopy system, utilizing a wavelength sweep range from 822 to 842 nm, a power of 100 mW, and a wavelength sweep interval of 0.05 nm. (e)–(h) Corresponding Raman spectra obtained through dispersive Raman spectroscopy.

Fig. 4

Raman spectra of biological samples obtained through SS-Raman spectroscopy. (a) Pork belly slice with the scanning direction indicated by the white arrow. The scale bar represents 5 mm. (b) The biological tissue mapping plot of sample composition. Raman spectra of (c) fat and (d) muscle layer, obtained through the SS-Raman spectroscopy (red) and dispersive Raman spectroscopy (blue).