In vivo confocal Raman spectroscopy has become the measurement technique of choice for skin health and skin care related communities as a way of measuring functional chemistry aspects of skin that are key indicators for care and treatment of various skin conditions. Chief among these techniques are stratum corneum water content, a critical health indicator for severe skin condition related to dryness, and natural moisturizing factor components that are associated with skin protection and barrier health. In addition, in vivo Raman spectroscopy has proven to be a rapid and effective method for quantifying component penetration in skin for topically applied skin care formulations. The benefit of such a capability is that noninvasive analytical chemistry can be performed in vivo in a clinical setting, significantly simplifying studies aimed at evaluating product performance. This presumes, however, that the data and analysis methods used are compatible and appropriate for the intended purpose. The standard analysis method used by most researchers for in vivo Raman data is ordinary least squares (OLS) regression. The focus of work described in this paper is the applicability of OLS for in vivo Raman analysis with particular attention given to use for non-ideal data that often violate the inherent limitations and deficiencies associated with proper application of OLS. We then describe a newly developed in vivo Raman spectroscopic analysis methodology called multivariate curve resolution-augmented ordinary least squares (MCR-OLS), a relatively simple route to addressing many of the issues with OLS. The method is compared with the standard OLS method using the same in vivo Raman data set and using both qualitative and quantitative comparisons based on model fit error, adherence to known data constraints, and performance against calibration samples. A clear improvement is shown in each comparison for MCR-OLS over standard OLS, thus supporting the premise that the MCR-OLS method is better suited for general-purpose multicomponent analysis of in vivo Raman spectral data. This suggests that the methodology is more readily adaptable to a wide range of component systems and is thus more generally applicable than standard OLS.

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