A recent study examines how Raman spectroscopy and UV-vis spectroscopy can be used in the quality control of over-the-counter medications (OTCMs), ensuring their authenticity.
Over-the-counter medications (OTCMs) are an essential component to health care. They serve as the first attempt to treat symptoms related to numerous common illnesses, such as colds, fevers, and allergy-related ailments. Because OTCMs do not require a prescription to obtain, consumers have easier access to acquiring them.
However, this is a double-edged sword. Because OTCMs are popular and easily accessible, they are susceptible to fraud. Despite strict guidelines set by the U.S. Food and Drug Administration, bad actors still manage to get counterfeit OTCMs on the market (1). The counterfeit drugs not only mislead consumers, but they can also result in adverse health effects because of incorrect dosages or harmful ingredients.
A new study led by Sayo O. Fakayode and comprised of researchers from Georgia College and State University and Purdue University explored this topic in a recent study. Published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, the research addresses the growing global issue of counterfeit OTCMs, which pose serious health risks to consumers and introduces a quality control method that could significantly enhance the safety and authenticity of over-the-counter medications (OTCMs), particularly oral liquid syrups (1).
In the study, Fakayode and the team combined Raman and UV-visible spectroscopy with principal component analysis (PCA) and partial least square (PLS) regression. The team specifically focused on key active ingredients in these medications, including acetaminophen, guaifenesin, dextromethorphan HBr, and phenylephrine HCl, which are often found in flavored oral liquid syrups (1).
Using PCA and PLS regression, the study was able to quantify the concentrations of these active ingredients with high accuracy, ranging from 88% to 94% (1). The PLS method demonstrated good linearity with an R² value of over 0.9784, meaning the data model could predict outcomes with great accuracy (1). Additionally, the technique showed high sensitivity, detecting active components such as acetaminophen and guaifenesin at concentrations as low as 0.02 mg/mL (1).
The method demonstrated in this study demonstrated several key benefits. First, the method eliminated the need for sample extraction, which is typically a time-consuming and costly step in drug analysis (1). By analyzing the syrup formulations directly, the method offered a more efficient solution that can be scaled for in situ or field analyses. This is particularly useful for drug manufacturers and regulatory agencies that require robust quality control measures to detect counterfeits or ensure product consistency (1).
In the pharmaceutical industry, maintaining accurate multicomponent quantification of active ingredients is essential. Fakayode's study provides a powerful tool for this purpose. Raman and UV-visible spectral profiling are highly effective for monitoring the signal responses of both single and multicomponent analyte mixtures (1,2). PCA, a mathematical modeling technique, plays a crucial role in simplifying complex data sets by identifying patterns and reducing variables while maintaining the integrity of the original data (3). This allows for quick pattern recognition that might not be easily observed in large numerical data sets (1,3).
PLS regression, another statistical method used in the study, establishes mathematical relationships between the X variables (the independent variables or the spectral data), and the Y variables (the dependent variables or the concentration data). (1). This method enables researchers to draw more precise conclusions about the concentrations of active components in different syrup formulations (1).
The overarching goal of this research is to address some of the existing challenges in current quality assurance protocols for OTCMs. Fakayode’s method is not only rapid and cost-effective, but it also highly adaptable for real-world applications (1). As a result, the study highlights the practicality of using the combined Raman and UV-visible spectroscopy, PCA, and PLS regression techniques for the analysis of flavored oral syrups, which have identical matrices to the drug products sold in pharmacies.
Raman Spectroscopy to Detect Lung Cancer and Monitor Vaccine Effects
January 20th 2025A new study highlights the use of Raman spectroscopy to detect lung cancer and evaluate the effects of the PCV13 vaccine. Researchers found distinct biochemical changes in lung cancer patients and healthy individuals, revealing the vaccine's significant impact on immune response.
An Inside Look at the Fundamentals and Principles of Two-Dimensional Correlation Spectroscopy
January 17th 2025Spectroscopy recently sat down with Isao Noda of the University of Delaware and Young Mee Jung of Kangwon National University to talk about the principles of two-dimensional correlation spectroscopy (2D-COS) and its key applications.
Nanometer-Scale Studies Using Tip Enhanced Raman Spectroscopy
February 8th 2013Volker Deckert, the winner of the 2013 Charles Mann Award, is advancing the use of tip enhanced Raman spectroscopy (TERS) to push the lateral resolution of vibrational spectroscopy well below the Abbe limit, to achieve single-molecule sensitivity. Because the tip can be moved with sub-nanometer precision, structural information with unmatched spatial resolution can be achieved without the need of specific labels.
New SERS-Microfluidic Platform Classifies Leukemia Using Machine Learning
January 14th 2025A combination of surface-enhanced Raman spectroscopy (SERS) and machine learning on microfluidic chips has achieved an impressive 98.6% accuracy in classifying leukemia cell subtypes, offering a fast, highly sensitive tool for clinical diagnosis.
Machine Learning-Enhanced SERS Technology Advances Cancer Detection
January 13th 2025Researchers at the Chinese Academy of Sciences have developed an optical detection strategy for circulating tumor cells (CTCs), combining machine learning (ML) and dual-modal surface-enhanced Raman spectroscopy (SERS). This approach offers high sensitivity, specificity, and efficiency, potentially advancing early cancer diagnosis.