Estimating the Temperature of Heat-exposed Bone via Machine Learning Analysis of SCI Color Values: A Pilot Study

• Wärmländer, Sebastian K.T.S. ; Varul, Liivi ^[4] ; Koskinen, Juuso ^[1] ; Saage, Ragnar ^[2] ; Schlager, Stefan ^[3]
  1. [1] University of Helsinki

     University of Helsinki

     Helsinki, Finlandia

     
  2. [2] University of Tartu

     University of Tartu

     Tartu linn, Estonia

     
  3. [3] University of Freiburg

     University of Freiburg

     Stadtkreis Freiburg im Breisgau, Alemania

     
  4. [4] University of Tartu.Estonia

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• Localización: Journal of forensic sciences, ISSN-e 1556-4029, ISSN 0022-1198, Vol. 64, Nº. 1, 2019, págs. 190-195
• Idioma: inglés
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• Resumen

  • 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 verte- brae (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.