Minimum Volume Constrained Non-negative Matrix Factorization Applied to the Monitoring of Active Cosmetic Ingredient into the Skin in Raman Imaging

摘要

The hyperspectral imaging is commonly used in the cosmetic area. Indeed, spectroscopic imaging techniques are usually employed to study the molecular composition of a cosmetic product, notably the Raman imaging. For this matter, Non-negative Constrained Least Square (NCLS), has been studied previously and has provided accurate distribution maps of Active Cosmetic Ingredient (ACI) with its associated penetration profile. However, it remains a supervised method since it requires an a priori knowledge of the Raman fingerprint of the ACI to track it and the availability of a large number of spectra from control data affects its performance. This work presents the comparison of a Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF) with the NCLS and a popular method in the chemometry community, Multivariate Curve Resolution Alternating Least Square (MCR-ALS) for hyperspectral image analysis. MVC-NMF proposes an unsupervised geometric approach to better fit a linear model to the data that provides lower modelling residuals. We also evaluate the parameter selection of the right number of constituent of Raman imaging from skin samples. It is shown that the MVC-NMF was able to accurately estimate the Raman spectrum of the ACI without supervision.