Bridging Visible and SWIR Spectroscopy via Dual-Spectrometer for Skin Tissue Characterization

The optical response of skin tissue in the near-ultraviolet to short-wave infrared band (350–2350 nm) contains rich physiological information such as hemodynamics, hydration status, and lipid metabolism. However, due to limitations in detector technology, conventional spectroscopic systems often use a single type of detector: silicon-based CCD (effective wavelength ≤ 1100 nm) captures visible blood features but cannot cover the long-wave vibration bands of water/lipids, while InGaAs (effective wavelength ≥ 900 nm) is sensitive to long-wave water/lipid absorption but misses the strong blood absorption in the visible region. This spectral detector segmentation leads to a separation of blood features from tissue background signals. In addition, traditional single source-probe distance measurement only probes one depth and cannot observe tissue information at different depths by varying the source-probe distance. This study employs a dual-spectrometer splicing architecture to achieve seamless continuous spectral measurement across 350–2350 nm by amplitude registration and normalization in the overlapping region. Using the thumb, palm, and arm as representative sites, we systematically analyzed spectral differences under varying vascular densities and fat backgrounds and successfully extracted absorption features of major chromophores including blood, water, and lipids. The results showed that the dual-spectrometer splicing method achieved smooth transitions in the overlapping region across different sites, validating its reliability. Spectral differences across sites were mainly characterized by hemoglobin absorption features in the visible region and water/lipid vibrational absorption features in the short-wave infrared region. These findings demonstrate the feasibility of dual-spectrometer splicing for broadband tissue spectral acquisition, enabling simultaneous capture of multi-component signals, and providing a new technical approach for non-invasive tissue composition analysis.