Quantitative Pharmaceutical Analysis Based on the PCA of Raman Spectra

Juan Carlos Martinez-Espinosa,Laura Madrid-Molina,Rafael Guzman-Cabrera,Osvaldo Rodriguez-Villalon,Teodoro Cordova-Fraga,Jose Rafael Guzman-Sepulveda,Eduardo Perez-Careta

doi:10.37358/rc.20.5.8143

Abstract

In the present work we examine the distribution of ciprofloxacin drug as an active ingredient in tablets samples from eight different laboratories. Additionally, control samples with different concentration were prepared for calibration. Raman spectra were recorded from all samples, including controls, and a principal component analysis (PCA) was implemented on them. The results of the PCA on control samples show that a clear discrimination among the different samples can be achieved and, more importantly, that the principal component of the Raman spectra has a well-defined, linear relation with the concentration of ciprofloxacin. Such a strong, statistically significant linear correlation obtained for the control samples constitutes our calibration for the quantitative analysis of commercial pharmaceutical compounds.

Highlights

Non-invasive techniques for the analysis of biological material are key to clinicians and researchers, yet there are few devices that can provide this capability
We demonstrate the use of Raman spectroscopy in combination with multivariate analysis as a powerful tool for pharmaceutical analysis and quantification
Our methodology consists on the application of principal component analysis (PCA) to the Raman spectral maps recorded, which allows measuring the mutual correlations between the different spectral groups

Summary

Introduction

Non-invasive techniques for the analysis of biological material are key to clinicians and researchers, yet there are few devices that can provide this capability. Some important aspects of Raman spectroscopy are that i) it can provide information that is distinct from that generated by light microscopy of stained preparations; ii) it has the potential to avoid human influence in the interpretation of the data collected e.g., as opposed to the skilled personnel required in stain-based microscopy; and iii) it permits an efficient identification and classification of samples, facilitating the identification of abnormal features. These capabilities have proved useful in outstanding applications such as cancer detection [3, 4] and diagnosis [5], as well as pharmaceutical analysis [6-8].

Methods

Results

Conclusion