Determination of florfenicol by Raman spectroscopy with principal component analysis (PCA) and partial least squares regression (PLSR)

Abstract

Raman spectroscopy is an important analytical technique because of its use for the quantification of different antibiotics used for the treatment of different diseases. Florfenicol is considered a broad-spectrum antibiotic drug that is used for bacterial infection and livestock species, including porcine, bovine, and chicken. In the current study, the qualitative and quantitative evolution of different concentrations of florfenicol drug along with a number of excipients has been done by using Raman spectroscopy. Some major Raman peaks such as 379 cm⁻¹, 478 cm⁻¹, 878 cm⁻¹, 918 cm⁻¹, 1341 cm⁻¹ and 1457 cm⁻¹ are related to the excipient and 630 cm⁻¹, 769 cm⁻¹, 973 cm⁻¹, 1142 cm⁻¹ and 1681 cm⁻¹ are related to the API concentrations and their intensities are increased and decreased with different concentration of excipient and API. The determination of Raman spectral data set of different concentrations of florfenicol drug has been done by using principal component analysis and partial least square regression model. The results of this study can lead to establishing an effective and reliable method to verify florfenicol contents in pharmaceutical samples which may be employed on an industrial scale.

Keywords:

• Florfenicol
• partial least squares regression (PLSR)
• principal component analysis (PCA)
• Raman spectroscopy

Disclosure statement

The authors declare that they have no conflicts of interest.