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• W3093435253 startingPage "110544" @default.
• W3093435253 abstract "This study evaluates to what degree soil samples associated in characteristic space are also close in geographical space, i.e., the possible location from which an unknown sample was obtained in a forensic investigation. The study compares similarity computed from Munsell colors, RGB colors, and full visible-near infrared (vis-NIR) spectra by the spectral angle mapper with similarity based on six easily-measured physio-chemical properties. The reference area is Anhui Province, China with three scales of datasets: provincial, county, and field. Ten diverse “unknown” samples were selected by the Kennard-Stone algorithm from the field-scale dataset and their matches in characteristic space from the several datasets were found by the different methods. The geographic distances of the matches to the “unknowns” were used to evaluate the source identification ability. When a detailed library with local samples is present, a limited set of physio-chemical properties achieved higher geographic accuracy than the color and spectral methods. However, with a regional library the spectral and color methods are superior. Distances in RGB space reveal finer differences than exact matching in Munsell space, but whole-spectra matching outperforms both, because of the rich information influenced by more soil properties than influencing color. We recommend the use of soil vis-NIR spectra as a priority indicator for forensic soil analysis because of its success in this study and its ability to work non-destructively on small quantities of soil." @default.
• W3093435253 created "2020-10-22" @default.
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• W3093435253 date "2020-12-01" @default.
• W3093435253 modified "2023-10-18" @default.
• W3093435253 title "Forensic soil source identification: comparing matching by color, vis-NIR spectroscopy and easily-measured physio-chemical properties" @default.
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• W3093435253 doi "https://doi.org/10.1016/j.forsciint.2020.110544" @default.
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