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Abstract：
Manual localization of bone fragments on the ground or on complex surfaces in relation to accidents or criminal activity may be time-consuming and challenging. It is here investigated whether combining a near-infrared hyperspectral camera and chemometric modeling with false color back-projection can be used for rapid localization of bone fragments. The approach is noninvasive and highlights the spatial distribution of various compounds/properties to facilitate manual inspection of surfaces. Discriminant partial least squares regression is used to classify between bone and nonbone spectra from the hyperspectral camera. A predictive model (>95% prediction ability) is constructed from raw chicken bones mixed with stone, sand, leaves, moss, and wood. The model uses features in the near-infrared spectrum which may be selective for bones in general and is able to identify a wide variety of bones from different animals and contexts, including aged and weathered bone.
摘要：
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