Microscopic Saw Mark Analysis: An Empirical Approach

Abstract

Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82%.

Micro-CT for saw mark analysis on human bone

“…7.). This not only confirms the findings from previous studies [8,[10][11][12] but also expands on existing work through the addition of toolmark properties not yet explored in the literature i.e. wall and trough angles.…”

“…Toolmark analysis is an important forensic tool particularly in cases of victim dismemberment. Previous studies have explored the use of saw mark properties such as width and shape to determine the tools used to create them [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In the current study 270 saw marks were created over two experiments using eight different tools.…”

“…In toolmark analysis, microscopy techniques are used to measure toolmark properties on defleshed bone surfaces. Previous studies have used a wide range of experimental approaches for the study of saw marks [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The differences between these studies include; i) tissue types (human, animal and synthetic analog), ii) and whether the bone was fleshed, semi-fleshed or defleshed, iii) sawing actions (controlled actions such as using miter saw, free saw actions using an unrestricted human volunteer), iv) number of volunteers, v) range of saws tested both within and between class, vi) number of toolmarks created, vii) imaging methods applied (Digital microscopy, Scanning Electron Microscopy, Epifluorescence, stereomicroscopy, radiography), and viii) the method of analysis (e.g., correlation, regression, decision trees, classification trees and random forest classifiers).…”

“…For categorical properties in Experiment 1, profile shapes for each tool were consistent: Tool 1 was 100% square; tool 2 was 100% W with 58% being truncated; tool 3 was 89% truncated W; tool 4 was 97% W with 27% being truncated; tool 5 was 100% square; tool 6 was 100% U; tool 7 was 89% Y; and tool 8 was 100% V. Edge shapes were also consistent with the exception of tool 1 where the difference between necked and straight edges was marginal. Although categorical properties were not included in any statistical analysis in this study, previous work has demonstrated their diagnostic capability [7,10,12].…”

Analysis of Class Characteristics of Reciprocating Saws,

“…Sharp force trauma to bone differs greatly depending upon the implement in question, with some weapons creating trauma with features of both blunt and sharp categories (4)(5)(6)(7)(8). Sharp force trauma research has incorporated a variety of advancements in the study of bone biomechanics and analytical techniques (9)(10)(11)(12)(13)(14)(15). Questions of directionality, microscopic and macroscopic analysis, use of scanning electron microscopy, and statistical modeling of kerf characteristics have been addressed in studies concerning hacking, cutting, and sawing (5,8,(16)(17)(18).…”