An Objective and Statistical Approach to Microscopic Human Hair Comparison: A Laboratory Exercise for the Forensic Science Undergraduate and Graduate Student
Keywords:
microscopic hair comparison, likelihood ratios, RGB color format, diameterAbstract
The following introduces a new approach to teaching microscopic hair examination in an academic instructional laboratory for forensic science undergraduate and graduate students. In the exercise, students are asked to determine the likelihood ratio of test hairs to assess the probability of encountering a hair with similar characteristics. Instead of relying on qualitative subjective assessment of morphological characteristics, students use two quantitative and objective parameters, namely diameter and color to characterize test hairs. With the use of software measurement tools, the diameter of each hair was measured in 3 locations along the hair shaft toward the middle of the hair and five RGB (red/green/blue) values were recorded at different points in the cortex approximately 3 um from the edge of the hair. Values are compared to a constructed hair database created from collected hairs vacuumed from heavily trafficked areas such as dining halls and lecture halls to determine a random match probability. A 95% upper bound confidence interval was determined from each random match probability and the reciprocal of this value was used to calculate a likelihood ratio which ranged from approximately 100 to 400 for randomly collected hairs. It is hoped that an important learning outcome of this exercise is that forensic science students will develop an awareness of the importance of providing statistical meaning to forensic science inclusions thus reducing the potential for scientific information to be misconstrued. This approach differs from most academic laboratory exercises of this nature which focus exclusively on matching unknowns to a closed set of standards.
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Copyright (c) 2022 Emma Redman, Casey Rech, Isabel Sandone, Victoria Echternach, Lawrence A Quarino
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.