Estimating the Temperature of Heat‐exposed Bone via Machine Learning Analysis of SCI Color Values: A Pilot Study |
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| Authors: | Sebastian K.T.S. Wärmländer Ph.D. Liivi Varul M.A. Juuso Koskinen M.A. Ragnar Saage M.A. Stefan Schlager Ph.D. |
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| Affiliation: | 1. Department of Biochemistry and Biophysics, Stockholm University, 106 91 Stockholm, Sweden;2. Division of Commercial and Business Law, Link?ping University, 581 83 Link?ping, Sweden;3. UCLA/Getty Conservation Programme, Cotsen Institute of Archaeology, UCLA, Los Angeles, CA, 90095Corresponding authors: Sebastian K.T.S. W?rml?nder, Ph.D. E‐mail: and Stefan Schlager Ph.D. E‐mail:;4. Institute of History and Archaeology, University of Tartu, 50090 Tartu, Estonia;5. School of Humanities, Tallinn University, 10120 Tallinn, Estonia;6. Department of Philosophy, History, Culture and Art Studies, University of Helsinki, 00014 Helsinki, Finland;7. Department of Anthropology, Medizinische Fakult?t der Albert Ludwigs, University of Freiburg, 79085 Freiburg, GermanyCorresponding authors: Sebastian K.T.S. W?rml?nder, Ph.D. E‐mail: and Stefan Schlager Ph.D. E‐mail: |
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| Abstract: | Determining maximum heating temperatures of burnt bones is a long‐standing problem in forensic science and archaeology. In this pilot study, controlled experiments were used to heat 14 fleshed and defleshed pig vertebrae (wet bones) and archaeological human vertebrae (dry bones) to temperatures of 400, 600, 800, and 1000°C. Specular component included (SCI) color values were recorded from the bone surfaces with a Konica‐Minolta cm‐2600d spectrophotometer. These color values were regressed onto heating temperature, using both a traditional linear model and the k‐nearest neighbor (k‐NN) machine‐learning algorithm. Mean absolute errors (MAE) were computed for 1000 rounds of temperature prediction. With the k‐NN approach, the median MAE prediction errors were 41.6°C for the entire sample, and 20.9°C for the subsample of wet bones. These results indicate that spectrophotometric color measurements combined with machine learning methods can be a viable tool for estimating bone heating temperature. |
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| Keywords: | forensic science forensic anthropology cremains burned bone color measurement regression analysis |
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