Multivariate regression model of impedance of normal and chemically irritated skin shows predictive ability

We present predictive models that can foresee how skin will react when exposed to chemicals. Skin impedance spectra, 31 frequencies between 1 and 1000 kHz at five depth settings, were collected before and after application of chemicals on volar forearms of volunteers. Tegobetaine and sodium lauryl sulphate were used to induce the irritations. The spectra were filtered using orthogonal signal correction (OSC). The relation between skin impedance of normal and chemically irritated skin was modelled using partial least squares regression (PLS). The predictive ability of this model is demonstrated for two irritants, and additional studies are required to establish this property for other chemicals.