Bias-invariant, detectability-aware dual-distance optical reflectance sensing for layered biological media

Optical reflectance sensors are widely used for non-invasive tissue assessment but are intrinsically biased by superficial absorbers, limiting reliability in layered media with variable surface composition. In skin-interfaced sensing, epidermal melanin is a dominant confounder that systematically modulates detected reflectance and undermines equitable performance. Here we introduce a bias-invariant optical sensing strategy based on a detectability-aware dual-distance reflectance observable that suppresses superficial absorption while preserving subsurface sensitivity. Using Monte Carlo photon transport simulations of layered skin, we show that conventional single-distance reflectance varies strongly with melanin, whereas a logarithmic near-far distance combination reduces melanin-induced variability by more than an order of magnitude. By jointly analyzing bias suppression, subsurface sensitivity, and signal feasibility, we derive design rules that identify buildable detector geometries near practical detectability limits. Although demonstrated here using epidermal melanin as a representative superficial absorber, the proposed framework is general and applies to layered biological media in which dominant superficial attenuation obscures deeper contrast. The approach enables bias-invariant, depth-selective optical sensing under practical detectability constraints, with subcutaneous injection-site assessment in diabetes care presented as a motivating application.