Advanced Raman Spectroscopy Method Boosts Precision in Drug Component Detection

Recently, a collaborative study conducted by researchers from Guangdong University of Technology, Guangzhou Zhengtian Technology Company, and the Huangpu Customs Technical Center explored a new method capable of accurately identifying active components in complex pharmaceutical formulations. This study, which was published in Optics Communications, showcases how Raman spectroscopy, when combined with data processing algorithms, can help advance pharmaceutical analysis (1).

Raman spectroscopy is being routinely applied in drug detection and analysis. For example, Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) have been routinely used to analyze and monitor drug levels in blood (2). Raman spectroscopy has also helped address important needs such as detecting counterfeit drugs and analyzing drug formulations (2). Part of the reason why Raman spectroscopy is popular in this space is because it requires minimal sample preparation and is a rapid, non-invasive technique (1,3). However, in composite medications—formulations that contain multiple active ingredients—its effectiveness has been hampered by persistent issues like spectral noise and fluorescence interference (1). Traditional strategies to address these issues often involve hardware modifications, such as applying filters or adjusting laser frequencies (1). Although these methods can improve signal clarity, they often fall short in handling the intricate spectral overlaps found in compound medications (1).

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In this study, Gao and his team looked to advance the application of Raman spectroscopy in identifying active components in pharmaceutical formulations. Their analytical method involved using Raman spectroscopy conducted at an excitation wavelength of 785 nm. By using this technique, the researchers avoided needing sample preparation, and the effect of that was that it significantly reduced the overall time required for analysis (1). From sample handling to detection, each experiment took no more than 3 min while the system demonstrates a remarkably quick response time of 4 s and delivers an optical resolution of up to 0.30 nm, with a signal-to-noise (S/N) ratio as high as 800:1 (1).

One of the key components of their method was integrating algorithms with Raman spectroscopy to improve the spectral output. By using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, the researchers used the advanced noise reduction tool proved especially effective in analyzing antondine injection, which is a liquid drug formulation containing antipyrine as its active ingredient (1). By applying airPLS, the team successfully smoothed out background noise and clarified the target compound’s Raman signature (1).

The researchers did encounter challenges analyzing the Amka Huangmin Tablet. Highlighted in the study, this formulation contains paracetamol and lincomycin-lidocaine gel, where lidocaine is the active component. These samples exhibited strong fluorescence interference, a common problem in Raman spectroscopy that can distort or obscure critical spectral features (1). To address this, Gao's team developed a novel dual-algorithm approach. They combined the airPLS algorithm with an interpolation peak-valley method, which identifies spectral peaks and valleys and uses piecewise cubic Hermite interpolating polynomial (PCHIP) interpolation to reconstruct a more accurate spectral baseline (1).

The combined algorithm eliminates background noise, but it also resolved baseline drift and preserved the integrity of characteristic peaks, leading to a more accurate identification of target compounds. The study’s use of density functional theory (DFT) further supported spectral interpretation by providing theoretical validation of experimental Raman shifts, especially in complex matrix environments (1).

Significantly, the proposed method was validated across three types of pharmaceutical forms—solid, liquid, and gel. Doing so demonstrated its versatility and practical applicability (1). The ability to accurately and quickly detect active ingredients like antipyrine, paracetamol, and lidocaine across formulations is integral to improving pharmaceutical quality control and forensic drug analysis, and this method proposed by Gao and his team can potentially help meet this demand and possibly pave the way for broader adoption of Raman spectroscopy in routine pharmaceutical testing.

References

1. Zhang, Y.; Gao, P.; Zhang, N.; et al. Efficient Detection of Specific Pharmaceutical Components in Compound Medications Based on Raman Spectroscopy. Opt. Commun. 2025, 577, 131470. DOI: 10.1016/j.optcom.2024.131470
2. Workman, Jr., J. New AI-Powered Raman Spectroscopy Method Enables Rapid Drug Detection in Blood. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/new-ai-powered-raman-spectroscopy-method-enables-rapid-drug-detection-in-blood (accessed 2025-04-04).
3. Horiba, Raman Spectroscopy for Pharmaceutical. Horiba. Available at: https://www.horiba.com/int/scientific/products/raman-imaging-and-spectrometers/raman-application-corner/raman-spectroscopy-for-pharmaceutical/#:~:text=Raman%20spectroscopy%20plays%20a%20crucial,standards%20and%20safeguarding%20public%20health. (accessed 2025-04-04).