Presentation Information
[16a-PB1-2]Improvement of the method for estimation of absorbing components in skin using multiple regression analysis
〇Yuki Horikomi1, Atsumu Miyatsu1, Kubota Wataru1, Kojima Iori1, Kikuchi Kumiko2, Yuasa Tomonori1, Aizu Yoshihisa1 (1.Muroran Univ., 2.Shiseido.)
Keywords:
multiple regression analysis,human skin,Monte Carlo simulation
In this study, a non-invasive method was investigated for measuring and estimating transcutaneous absorbing components by combining Monte Carlo simulation and multiple regression analysis. In the previous studies, it has been reported that melanin concentration, hemoglobin concentration, and oxygen saturation can be estimated in a two-layered skin model using a two-step multiple regression analysis based on the modified Lambert–Beer law. In this study, we examined whether this method is applicable to a nine-layered skin model that more closely represents actual skin structure. In addition, a new method was investigated for estimating oxygen saturation using the estimated hemoglobin concentrations instead of the regression coefficients. As a result, the effectiveness of nonlinear correction using multiple regression analysis was confirmed even in the nine-layered skin model, and the proposed method was shown to improve the estimation accuracy of oxygen saturation.