Key Takeaways
- Researchers developed a Raman method with advanced algorithms to accurately identify active ingredients in multi-component pharmaceutical formulations without sample prep.
- The method successfully analyzed liquid, solid, and gel drug samples, detecting antipyrine, paracetamol, and lidocaine in just 4 seconds per test.
- Key innovations included using the airPLS algorithm and a hybrid peak-valley interpolation technique to reduce noise and correct fluorescence interference in complex samples.
- Density functional theory (DFT) modeling validated detection accuracy, supporting future integration of this technique into real-time pharmaceutical manufacturing and quality control.
A recent study published in Optics Communications tested a new technique that could help streamline and improve the detection of active ingredients in complex pharmaceutical formulations. This technique involved the use of Raman spectroscopy and advanced spectral processing algorithms. This integration of both methods helped improve the accuracy and efficiency of component identification in composite medications (1).
What Is Raman Spectroscopy?
Raman spectroscopy is a molecular analysis technique based on the Raman scattering effect discovered by physicist C.V. Raman (2,3). This technique helps researchers discover the vibrational and rotational energy levels of molecules by analyzing how monochromatic laser light scatters after interacting with a sample (2,3). Known for its non-destructive nature and ability to produce unique molecular “fingerprint spectra,” Raman spectroscopy is increasingly being used in drug analysis (1–3). However, drug analysis is changing rapidly as multi-component drug formulations are becoming more common and more widely used. This trend poses challenges to using Raman spectroscopy for this type of analysis because of spectral noise and fluorescence interference.
What Did The Researchers Do In Their Study?
In this study, researcher Xiangdong Gao of Guangdong University of Technology and his team proposed a novel Raman-based detection method that does not require sample preparation, reducing analysis time and enabling immediate detection. The system uses a 785 nm excitation wavelength and achieves an average response time of just 4 seconds, with an optical resolution of up to 0.30 nm and a signal-to-noise ratio reaching 800:1 (1).
Their study concentrated on using their method to analyze three composite drug samples. The three composite drug samples selected for this study were Antondine Injection, Amka Huangmin Tablet, and lincomycin-lidocaine gel, which exist in liquid, solid, and gel forms, respectively (1). As part of their experimental procedure, the research team specifically targeted and successfully identified the active ingredients antipyrine, paracetamol, and lidocaine in each formulation (1).
What Are The Important Takeaways Of This Study?
The most important takeaways of this study explain why the method proposed in this study was effective. One of the main takeaways was the successful integration of the adaptive iteratively reweighted penalized least squares (airPLS) algorithm (1). Using airPLS allowed the researchers to reduce noise in Raman spectral data. For more complex fluorescence interference scenarios, the researchers also innovatively combined airPLS with an interpolation peak-valley algorithm (1). This hybrid technique allowed for baseline correction by identifying local spectral peaks and valleys and applying piecewise cubic Hermite interpolating polynomial (PCHIP) interpolation (1).
For the liquid formulation, which was the Antondine injection, noise interference was managed effectively using the airPLS algorithm alone. However, in the Amka Huangmin tablet and lincomycin-lidocaine gel samples, where strong fluorescence interference caused baseline drift and obliterated peaks, the combined algorithmic approach restored clarity to the spectra and successfully revealed the signature peaks of paracetamol and lidocaine (1).
Another takeaway from this study was how using density functional theory (DFT) improved the method. The researchers demonstrated that DFT simulations predicted the theoretical Raman spectra, which were then compared with experimental results to validate the detection accuracy (1). By merging experimental data with DFT modeling, we can verify that the observed spectral features truly belong to the target molecules.
Overall, this study demonstrates that this method can be used to analyze different drug formulations without altering the detection protocol. Because Raman spectroscopy requires no sample preparation and can conduct its analysis quickly, the method proposed by the researchers in this study is ideal for pharmaceutical manufacturing and quality assurance (1).
In an industry where fast, accurate, and non-invasive testing is essential, this research underscores the growing utility of Raman spectroscopy enhanced by intelligent data processing. The researchers believe that future studies could help automate and integrate their method into real-time pharmaceutical production lines (1).
References
- 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/joptcom.2024.131470
- Workman, Jr., J. A New Radiation: C.V. Raman and the Dawn of Quantum Spectroscopy, Part I. Spectroscopy 2025, 40 (4), 30–33. DOI: 10.56530/pectroscopy.yo1483v7
- Workman, Jr., J. A New Radiation: C.V. Raman and the Dawn of Quantum Spectroscopy, Part II. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/a-new-radiation-c-v-raman-and-the-dawn-of-quantum-spectroscopy-part-ii (accessed 2025-05-19).
