In the present study, an innovative and highly efficient near-infrared hyperspectral imaging (NIR-HSI) method is proposed to provide spectral maps able to reveal collagen distribution in large-size bones, also offering semi-quantitative estimations. A recently introduced method for the construction of chemical maps, based on Normalized Difference Images (NDI), is declined in an innovative approach, through the exploitation of the NDI values computed for each pixel of the hyperspectral image to localize collagen and to extract information on its content by a direct comparison with known reference samples. The developed approach addresses an urgent issue of the analytical chemistry applied to bioarcheology researches, which rely on well-preserved collagen in bones to obtain key information on chronology, paleoecology and taxonomy. Indeed, the high demand for large-sample datasets and the consequent application of a wide variety of destructive analytical methods led to the considerable destruction of precious bone samples. NIR-HSI pre-screening allows researchers to properly select the sampling points for subsequent specific analyses, to minimize costs and time and to preserve integrity of archaeological bones (which are available in a very limited amount), providing further opportunities to understand our past.

在本研究中，提出了一种创新且高效的近红外高光谱成像 (NIR-HSI) 方法，以提供能够揭示大尺寸骨骼中胶原分布的光谱图，并提供半定量估计。最近引入的基于归一化差异图像 (NDI) 的化学图构建方法在创新方法中被拒绝，通过利用为高光谱图像的每个像素计算的 NDI 值来定位胶原蛋白并提取信息通过与已知参考样品的直接比较来确定其含量。所开发的方法解决了应用于生物考古学研究的分析化学的一个紧迫问题，该研究依靠骨骼中保存完好的胶原蛋白来获取有关年代学、古生态学和分类学的关键信息。的确，对大样本数据集的高需求以及随之而来的各种破坏性分析方法的应用导致珍贵骨骼样本的大量破坏。NIR-HSI 预筛选使研究人员能够为后续的特定分析正确选择采样点，最大限度地减少成本和时间，并保持考古骨骼（数量非常有限）的完整性，为了解我们的过去提供更多机会。