Confocal Raman spectroscopy assisted by chemometric tools: A green approach for classification and quantification of octyl p-methoxycinnamate in oil-in-water microemulsions

Abstract

This work proposes a green analytical method based on confocal Raman spectrometry and chemometrics tools for the qualitative and quantitative analysis of oil in water microemulsions loaded with the UVB filter octyl p-methoxycinnamate (OMC). The method does not use reagents and only 10 µL of sample are needed. The analyzed microemulsion samples were synthetized in the laboratory using decaethylene glycol mono-dodecyl ether (21.9 %) as non-ionic surfactant, ethyl alcohol (7.3 %) as co-surfactant, oleic acid (1.5 %) as oil phase and water (69.3 %). A physicochemical characterization of the samples was carried out obtaining expected values for droplet size (<20 nm), polydispersity index (<0.290) and conductivity (0.04–0.07 mS cm−1), among others. Linear discriminant analysis (LDA) after selection of variables using the successive projections algorithm (SPA) and soft independent modelling of class analogy (SIMCA) were employed to classify microemulsions with different concentrations of OMC (1.0 to 10.0 %). In the case of LDA, seven Raman spectral variables were previously selected by SPA and after this SPA-LDA model resulted in one error in the prediction set achieving an accuracy of 97.8 %. The SIMCA model (α = 0.05) presented an explained variance higher 97 % using four principal components and it allowed the correct classification of 100 % of the samples (N = 15). In the quantitative analysis, partial least squares (PLS) was used to determine OMC in a range according to international legislation. The model presented optimal statistical parameters (R2 = 0.9699; RMSEP = 0.54 %) and the prediction of samples were in close agreement with HPLC method. Moreover, the greenery of the method was estimated using the AGREE metric and an optimal value of 0.85 was obtained demonstrating the proposed analytical method results environmentally friendly.