Determination of main raw material source in bar soaps using mid-infrared spectroscopy combined with classification tools

Abstract

The personal care industry is one of the fastest-growing markets in the world in terms of revenue and sales volume. In addition, this market has been going through some modifications to serve its consumers, a fact that has generated the development of a new line of products. Nowadays several products, including bar soaps, are commercialized as free of animal and synthetic materials. In this context, the development of methodologies to verify the source of the raw material (animal, synthetic, or vegetable) used in the production of soaps can be a valuable tool to supplement the quality control routines. Infrared spectroscopy combined with pattern recognition methods, i.e. Principal Component Analysis (PCA), Partial Least Squares Discriminatory Analysis (PLS-DA) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), were applied to verify the main source of raw materials used in the soaps production. Analyzing the scores plot for the first two Principal Components of PCA it was possible to observe distinct clusters of soap samples grouped based on the main sources of raw material. Both supervised methods, PLS-DA and DD-SIMCA, were able to correctly classify all samples into their corresponding classes. The most influential variables for the discrimination of groups by PLS-DA were the spectral bands related to changes in the carbon chains and in the amount of carboxyl or glycerol groups in the chemical structure. While DD-SIMCA demonstrated similarity between soaps containing mainly animal and vegetal raw material. This study indicates the potential of chemometric tools in the development of robust predictive models that may be integrated to identify the raw materials used in bar soaps.