HEMATOCRIT – IMPLICATIONS FOR BLOODSTAIN PATTERN ANALYSIS
Natasha ROGERS Bachelor of Science (Biology) Honours
Centre for Forensic Science University of Western Australia
This thesis is presented in partial fulfilment of the requirements for the Master of Forensic Science
September 2009
ii
DECLARATION
I declare that the research presented in this 36 point thesis, as part of the 96 point
Master degree in Forensic Science, at the University of Western Australia, is my own
work. The results of the work have not been submitted for assessment, in full or part,
within any other tertiary institute, except where due acknowledgement has been made in
the text.
…………………………………………………
Natasha Ellen Rogers
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ABSTRACT
Blood is one of the most common and important types of physical evidence present at a
crime scene. When liquid blood is acted upon by external physical forces, that blood is
often distributed through the air in the form of droplets, with bloodstains and bloodstain
patterns deposited on adjacent surfaces. Using the mathematical relationship that exists
between the blood droplet and resultant bloodstain’s length and width ratio, the angle at
which the blood droplet impacted the receiving surface can be determined. Using this
relationship, it becomes possible for Bloodstain Pattern Analysts to determine the three
dimensional Region of Origin for the blood source from which the bloodstains under
examination have originated. A Bloodstain Pattern Analyst performs angle of impact
calculations from bloodstains for the purpose of making a three dimensional
determination of blood source Region of Origin. The reliability of that determination is
based on an assumption that one of the most important biological properties of blood;
the amount of red blood cells or hematocrit value, has no influence over the length to
width ratio of a bloodstain. As a consequence the Impact angle = arcsine [width/length]
calculation has been assumed accurate regardless of the 'unknown' hematrocrit value.
This thesis investigated the effect of the hematocrit value on the angle of impact
calculation and thus the ability to determine the three dimensional blood source Region
of Origin.
Bloodstains were created by releasing a series of 18μL droplets, with ten different
hematocrit values, onto a ceramic tile at four different angles. The resultant bloodstain
length and width was measured and impact angle calculated. Evaluation of the research
data shows that the hematocrit value significantly affects the bloodstains length and
width. However, it is apparent that there is close agreement between the known and
calculated impact angles irrespective of the hematocrit value.
This thesis also examined bloodstain patterns with varying hematocrit values deposited
on adjacent vertical surfaces from impact spatter events. Five hematocrit values ranging
from 16.7 to 64.8% were used. The research showed that it is safe to conclude that a
Bloodstain Pattern Analyst is able to accurately determine the three dimensional Region
iv
of Origin for an impact spatter pattern regardless of the hematocrit value of the donor’s
blood.
This thesis compared the traditional manual measuring of bloodstains with a new
computer-based measurement technique that uses computer assisted ellipse fitting.
Bloodstains were measured manually and with the specifically designed Microsoft®
Office Excel 2003 Auto Shapes. The findings clearly demonstrate that the use of
Microsoft® Office Excel 2003 Auto Shapes to fit an ellipse and measure a bloodstain is
more accurate and reliable when compared to the current manual bloodstain
measurement technique.
The Tangent, String Line and BackTrack™ Images Methods are all current industry
accepted methods for determining the Region of Origin for impact spatter bloodstain
patterns. The impact angles of individual bloodstains are used to calculate the
horizontal and vertical location of the blood source [X, Y and Z coordinate values].
Results from this study suggest that all three reconstructive methods produce X, Y and
Z coordinate values [Region of Origin] within acceptable industry limits, thus allowing
for the positioning [laying, sitting or standing] of a victim during a bloodshed event.
v
ACKNOWLEDGEMENTS
Dr Mark Reynolds: Firstly thanks Reno for having faith in me and allowing me to join
the secret world of BPA. You provided me with the technical guidance and support I
needed to complete this thesis. I was never scared of the red pen and have spent since
September 2006 “striving to comprehend”. They say that you are only ever as good as
your teacher, I was lucky, I got the best.
Professor Ian Dadour: Thanks Ian, it has been a long time coming but it is finally
finished. You have been nothing but supportive throughout this journey. I am truly in
your debt.
Senior Constable Brett McCance, WA Police Crime Scene Investigations: Thanks
Brett for all your support, advice and taking the time to help me reconstruct 15 impact
spatter patterns. If manual stringing ever becomes an Olympic sport I have no doubt
that we would get gold in the mixed doubles.
Hope Percy: Hope, thank you for taking the time to assist me with labelling 1200
bloodstains. Thanks for having the patience to support me for what has seemed like
eternity. Sunny days will no longer be spent studying but rather living.
Denise Galvin, PathWest: What can I say Denise, you are a truly inspiration woman
who originally gave up time in your busy schedule to help a stranger. You have
provided me with assistance every time I asked and if I have gained anything from this
experience it is a friend. I will always be there for you should you need it, my friend.
Kevin Davey, PathWest Laboratory Medicine WA Swan Districts: Thanks Kevin for
teaching me how to analyse my own blood and allowing your staff to take my blood for
experimental purposes.
All staff of PathWest Laboratory Medicine WA Swan Districts: Thanks for taking my
blood and showing general concern with the amount taken, especially when I said, “I
just need a little bit more.”
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Sasha Voss: Thanks Sasha for putting up with my moments of sheer panic after not
sleeping and trying to master stats.
Phil Freegard: Thanks for lending me the angle board to complete the passive drop
experiment.
Team Five, WA Police Crime Scene Investigations: Mick, Brett, Lamby and Wardy,
my boys, I spend more time with you guys than I do my family. Thanks for supporting
me throughout the last 18 months.
Fiona McQuisten: Fi, Sorry for making you revise Bloodstain Pattern Analysis but it is
truly appreciated.
My Family and Friends: All I can say is thanks and don’t worry I will be turning up to
the next do……….never shall you hear the words, “I can’t till I finish my masters”
again.
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TABLE OF CONTENTS
DECLARATION ............................................................................................................. ii
ABSTRACT .................................................................................................................... iii
ACKNOWLEDGEMENTS ............................................................................................ v
TABLE OF CONTENTS .............................................................................................. vii
LIST OF FIGURES ........................................................................................................ x
LIST OF TABLES ....................................................................................................... xiv
1. GENERAL INTRODUCTION TO BLOODSTAIN PATTERN ANALYSIS .. 1
1.1 Introduction ....................................................................................................... 1
1.2 Bloodstain Pattern Analysis .............................................................................. 1
1.3 History of Bloodstain Pattern Analysis ............................................................. 3
1.4 Aims and Objectives ......................................................................................... 5
2. LITERATURE REVIEW ....................................................................................... 7
2.1 Biological and Physical Properties of Human Blood ........................................ 7
2.1.1 Section Introduction ............................................................................... 7
2.1.2 Functions of Human Blood .................................................................... 7
2.1.3 Blood Composition ................................................................................. 8
2.1.4 Physical Properties of Blood ............................................................... 12
2.1.5 The Interaction between Biological and Physical
Properties of Blood ............................................................................. 18
2.1.6 The Use of Human Blood for Experimental Purposes ......................... 22
2.1.7 Section Conclusions ............................................................................. 23
2.2 Blood Dynamics .............................................................................................. 24
2.2.1 Section Introduction ............................................................................. 24
2.2.2 Blood Droplet Dynamics in Flight ....................................................... 24
2.2.3 Blood Droplet Impact Dynamics.......................................................... 28
2.2.4 Section Conclusions ............................................................................. 30
2.3 Reconstructive Techniques for Impact Spatter Patterns ................................. 31
2.3.1 Section Introduction ............................................................................. 31
2.3.2 Objectives of Bloodshed Reconstruction.............................................. 31
2.3.3 Terminology and Categories of Bloodstains ........................................ 32
2.3.4 Area of Convergence[AOC]................................................................. 34
2.3.5 Region of Origin [ROO] ...................................................................... 35
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2.3.6 Angle of Impact .................................................................................... 36
2.3.7 Measuring Bloodstains ........................................................................ 38
2.3.8 Stain Selection ...................................................................................... 41
2.3.9 The String Line Method........................................................................ 43
2.3.10 The Trigonometric [Tangent] Method ............................................... 43
2.3.11 The Computer Assisted [BackTrack™] Method ................................ 45
2.3.12 Section Conclusions ........................................................................... 45
3. THE EFFECT OF HEMATOCRIT VALUE ON RESULTANT STAIN
PARAMETERS .................................................................................................... 47
3.1 Experimental Methods .................................................................................... 47
3.1.1 Introduction .......................................................................................... 47
3.1.2 Collection and Handling of Blood ....................................................... 47
3.1.3 Adjusting Hematocrit Values ............................................................... 48
3.1.4 Angle Board ......................................................................................... 49
3.1.5 Experimental Setup .............................................................................. 50
3.1.6 Photographic Recording Technique .................................................... 50
3.1.7 Manual Measurements of Bloodstains ................................................. 51
3.1.8 Computer Assisted Bloodstain Measurement Using Microsoft® Office
Excel 2003 Auto Shapes ...................................................................... 51
3.1.9 Statistical Analysis ............................................................................... 52
3.2 Results ............................................................................................................. 53
3.2.1 Impact Angle ........................................................................................ 53
3.2.2 Stain Width ........................................................................................... 57
3.2.3 Stain Length ......................................................................................... 61
3.2.4 Manual versus Microsoft® Office Excel 2003 Auto Shapes
measurement techniques / hematocrit value comparison ................... 65
3.2.5 Manual versus Microsoft® Office Excel 2003 Auto Shapes
measurement techniques / impact angle comparison.......................... 65
3.2.6 Manual measurement technique – impact angle and hematocrit value
comparison .......................................................................................... 66
3.2.7 Microsoft® Office Excel 2003 Auto Shapes measurement technique –
impact angle and hematocrit value comparison ................................. 66
3.2.8 General observations ........................................................................... 67
3.3 Discussion ....................................................................................................... 69
3.4 Conclusions ..................................................................................................... 73
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4. THE EFFECT OF HEMATOCRIT VALUES ON IMPACT SPATTER
PATTERNS – IMPLICATIONS FOR RECONSTRUCTION ....................... 75
4.1 Experimental Methods .................................................................................... 75
4.1.1 Introduction .......................................................................................... 75
4.1.2 Collection and Handling of Blood ....................................................... 75
4.1.3 Adjusting Hematocrit Values ............................................................... 75
4.1.4 Experimental Setup .............................................................................. 76
4.1.5 Stain Measurement ............................................................................... 77
4.1.6 The Trigonometric [Tangent] Method ................................................. 78
4.1.7 The String Line Method........................................................................ 79
4.1.8 The BackTrack™ Method .................................................................... 80
4.1.9 Statistical Analysis ............................................................................... 81
4.2 Results ............................................................................................................. 82
4.2.1 Comparison of the X Coordinate ......................................................... 82
4.2.2 Comparison of the Y Coordinate ......................................................... 88
4.2.3 Comparison of the Z Coordinate ......................................................... 89
4.2.4 Comparison of the Stain Measurement Technique –
Tangent Method .................................................................................. 90
4.2.5 Comparison of Blood Hematocrit Value .............................................. 91
4.2.6 Comparison of the Coordinate Value .................................................. 91
4.2.7 Comparison of Reconstructive Technique ........................................... 91
4.3 Discussion ..................................................................................................... 102
4.4 Conclusions ................................................................................................... 105
4.5 Future Directions ........................................................................................... 105
6. BIBLIOGRAPHY ............................................................................................... 107
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LIST OF FIGURES
Chapter 2 Page
Figure 2.1 Components of whole human blood ........................................................ 9
Figure 2.2 Formation of a blood clot showing the red blood cells (red),
activated platelets (blue) and fibrin strands (yellow). .............................. 9
Figure 2.3 Representation of surface tension on the surface and cohesion
throughout a spherical droplet. ............................................................... 14
Figure 2.4 Influence of temperature on the surface tension of whole blood ........... 16
Figure 2.5 Effect of hematocrit on blood viscosity flowing through small
and large tubes........................................................................................ 18
Figure 2.6 Influence of hematocrit value on the relative viscosity of whole
human blood compared with that of plasma. ......................................... 19
Figure 2.7 Parabolic flight path position instants for a droplet with
gravitational force indicators (blue arrows) and air resistance
(pink arrows). ......................................................................................... 26
Figure 2.8 Prolate, spherical and oblate forms of an oscillating blood droplet
during flight ............................................................................................ 27
Figure 2.9 Major Bloodstain Pattern Categories. .................................................... 33
Figure 2.10 Bloodstain pattern analysis “Spatter” sub categories ............................. 34
Figure 2.11 Two dimensional Area of Convergence determination from
multiple bloodstains originating from a single impact ........................... 35
Figure 2.12 The X, Y and Z coordinate values. ........................................................ 36
Figure 2.13 Width to length ratio of a bloodstain equated to a right angle
triangle.................................................................................................... 37
Figure 2.14 Directionality of bloodstain established with location of the
leading and terminal edges ..................................................................... 39
Figure 2.15 Measurement of the width and length of an elongated bloodstain
by fitting a theoretical ellipse ................................................................. 40
Figure 2.16 Measurement of the width and length of a bloodstain using
Microsoft® Office Excel 2003 Auto Shapes ........................................... 41
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Chapter 2 (cont) Page
Figure 2.17 Basic right angled triangle and how this can be used by a
Bloodstain Pattern Analyst to determine the area of origin for a
impact pattern on a wall ......................................................................... 44
Chapter 3
Figure 3.1 Comparative impact angle for bloodstains created at known
impact angles with different hematocrit values, manually
measured and measured using Microsoft® Office Excel Auto
Shapes [Error bars represent ±1 Std Dev]. ............................................. 56
Figure 3.2 Comparative width analysis for bloodstains created at known
impact angles with different hematocrit values, manually
measured and measured using Microsoft® Office Excel Auto
Shapes [Error bars represent ±1 Std Dev]. ............................................. 60
Figure 3.3 Comparative length analysis for bloodstains created at known
impact angles with different hematocrit values, manually
measured and measured using Microsoft® Office Excel Auto
Shapes [Error bars represent ±1 Std Dev]. ............................................. 64
Figure 3.4 Impact angle, hematocrit value and stain shape. .................................... 68
Chapter 4
Figure 4.1 Author manually measuring the width and length of a bloodstain
using electronic calipers and OptiVisor headset .................................... 78
Figure 4.2 Stringing of a impact spatter pattern showing the estimated
Region of Origin in yellow .................................................................... 79
Figure 4.3 An example of a individual stain, with scale, plumb line and
major axis line photographed for use in both BackTrack™
Images and Microsoft® Office Excel 2003 Auto Shapes. ...................... 80
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Chapter 4 (cont) Page
Figure 4.4 Difference between the known and calculated X coordinate value
using the Tangent, String Line and BackTrack™ Methods to
determine the Region of Origin for 15 impact spatter patterns
with differing hematocrit values. ........................................................... 84
Figure 4.5 Difference between the known and calculated Y coordinate value
using the Tangent, String Line and BackTrack™ Methods to
determine the Region of Origin for 15 impact spatter patterns
with differing hematocrit values. ........................................................... 85
Figure 4.6 Difference between the known and calculated Z coordinate value
using the Tangent, String Line and BackTrack™ Methods to
determine the Region of Origin for 15 impact spatter patterns
with differing hematocrit values. ........................................................... 86
Figure 4.7 Difference between the known and calculated X coordinate value
using the Tangent Method for bloodstains measured using
Microsoft® Office Excel AutoShapes, Manual and BackTrack™
for 15 impact spatter patterns with differing hematocrit values. ........... 87
Figure 4.8 Impact Spatter Pattern 2 (16.7 Hematocrit) – actual blood source
Z value indicated. ................................................................................... 95
Figure 4.9 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of
experimentally derived Z coordinate values with actual blood
source Z coordinate value. ..................................................................... 95
Figure 4.10 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of
experimentally derived X coordinate values with actual blood
source X coordinate value. ..................................................................... 96
Figure 4.11 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of
experimentally derived Y coordinate values with actual blood
source Y coordinate value. ..................................................................... 96
Figure 4.12 Impact Spatter Pattern 2 (16.7 Hematocrit) – wooden blood
source support positioned as to indicate the actual blood source
location top of blood. ............................................................................. 97
Figure 4.13 Impact Spatter Pattern 15 (64.8 Hematocrit) – actual blood
source Z coordinate value indicated. ...................................................... 98
xiii
Chapter 4 (cont) Page
Figure 4.14 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of
experimentally derived Z coordinate values with actual blood
source Z coordinate value. ..................................................................... 98
Figure 4.15 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of
experimentally derived X coordinate values with actual blood
source X coordinate value. ..................................................................... 99
Figure 4.16 Impact Spatter Pattern 15 (64.8 Hematocrit) – wooden blood
source support positioned as to indicate the actual blood source
location top of block............................................................................... 99
Figure 4.17 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of
experimentally derived Y coordinate values with actual blood
source Y coordinate value. ................................................................... 100
Figure 4.18 Top view 2D representation of Impact Spatter Pattern 15 (64.8
Hematocrit) using BackTrack™. The red cross indicates the X
and Y coordinates. ................................................................................ 100
Figure 4.19 Side view 2D representation of Impact Spatter Pattern 15 (64.8
Hematocrit) using BackTrack™. The red cross indicates the X
and Z coordinates. ................................................................................ 101
Figure 4.20 End view 2D representation of Impact Spatter Pattern 15 (64.8
Hematocrit) using BackTrack™. The red cross indicates the Y
and Z coordinates. ................................................................................ 101
xiv
LIST OF TABLES
Chapter 2 Page
Table 2.1 Surface tension of some common fluids. ............................................... 15
Table 2.2 Hematocrit values (percent) for males and females throughout
childhood ................................................................................................ 22
Table 2.3 Normal hematocrit values for a variety of mammalian species ............. 22
Chapter 3 Table 3.1 Volumes of plasma and red blood cells pipette into labeled vials
with the corresponding predicted hematocrit value versus the
actual hematocrit value used for experimental purposes. ...................... 49
Table 3.2 Shows Manual and Microsoft® Office Excel Auto Shapes
(BOLDED) mean calculated impact angle, standard deviation
and minimum-maximum range for bloodstains with different
hematocrit values that have fallen on ceramic tiles offset from a
vertical at known angle values (n = 30 stains). ...................................... 55
Table 3.3 Shows Manual and Microsoft® Office Excel Auto Shapes
(BOLDED) mean width (mm), standard deviation and minimum-
maximum range for bloodstains with different hematocrit values
that have fallen on ceramic tiles offset from a vertical at known
angle values (n = 30 stains). ................................................................... 59
Table 3.4 Shows Manual and Microsoft® Office Excel Auto Shapes
(BOLDED) mean length (mm), standard deviation and
minimum-maximum range for bloodstains with different
hematocrit values that have fallen on ceramic tiles offset from a
vertical at known angle values (n = 30 stains). ...................................... 63
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Chapter 4 Page Table 4.1 Predicted hematocrit value versus the actual hematocrit value
used for experimental purposes for plasma and red blood cells. ........... 76
Table 4.2 Shows Microsoft® Office Excel Auto Shapes, Manual
(BRACKETS) and BackTrack™ (BOLDED) measurement data
for bloodstains to determine Region of Origin using Tangent,
String Line and BackTrack™ Methods for Impact Spatter Pattern
1 to 15. .................................................................................................... 83
Table 4.3 Shows Manual, Microsoft® Office Excel Auto Shapes (ITALICS),
BackTrack™ (BOLDED) measurement data for bloodstains for
Impact Spatter Patterns 1 to 15. ............................................................. 93
1
1. GENERAL INTRODUCTION TO BLOODSTAIN PATTERN ANALYSIS
1.1 Introduction This chapter introduces the topic of Bloodstain Pattern Analysis (BPA). Using the
available information in published literature, a brief history of BPA along with its
development of BPA as a forensic discipline will be discussed, including how such
scientific evidence has been evaluated by judicial proceedings. The last section details
the purpose and aims of this research thesis.
The reconstruction of a bloodshed event has become synonymous with experimentation
in an effort to understand general blood dynamics and recreate bloodstain patterns
which are substantially similar to those found at a crime scene (Wonder 2001).
Currently, no blood source Region of Origin reconstructive methods take into account
the unknown hematocrit value of bloodstains and the possible effect of hematocrit
values on the reliable determination of that Region of Origin. The purpose of this study
is to identify the effect of hematocrit values on resultant stain parameters and assess the
level of uncertainty with regards to reconstruction of a bloodshed event at crime scenes.
The quantification of this uncertainity will give scene and experimental results
increased reliabilty, traceability and scientifc robustness as required by the courts
(Daubert 1993).
1.2 Bloodstain Pattern Analysis
Blood is one of the most common and important types of physical evidence present at a
crime scene (Raymond et al. 2001). When blood is acted upon by external physical
forces, the blood is often distributed through the air in the form of droplets. This results
in bloodstains and bloodstain patterns deposited on adjacent surfaces. A crime scene
investigator can gain valuable information from serology, immunology and
interpretation of bloodstains and bloodstain patterns (Pizzola et al. 1986a).
2
BPA involves the analysis of the size, shape and distribution of the bloodstains and
bloodstain patterns. This combined with knowledge of the underpinning sciences
(mathematics, biology and physics) provides information on the event or sequence of
events that resulted in the deposition of these bloodstains and patterns (Bevel and
Gardner 2002; MacDonell 2005; James et al. 2005). A bloodstain analyst is required to
examine the incident scene, take photographs, make sketches, examine clothing,
objects, weapons, and deceased individuals and read forensic pathology and biology
reports in significant detail (James et al. 2005). Bloodstains provide analysts with a
“window to the past” (Bevel and Gardner 2002), defining actions that have occurred
during the bloodshed event. After viewing all the evidence available the bloodstain
analyst will draw conclusions and produce a bloodstain pattern report that may be
presented for judicial proceedings as expert testimony.
Raymond et al. 2001; Bevel and Gardner 2002; James et al. 2005 state that the crime
scene reconstruction of a bloodshed event can provide information to determine:
• Areas of convergence and origin of bloodstains
• Type and direction of impact
• Mechanisms by which bloodstains and bloodstain patterns were produced
• Provide an understanding as to how blood was deposited onto particular items of
evidence
• The position of the victim, assailant, or items located within the scene during the
bloodshed event
• Possible movement of the victim, assailant, or items post bloodshed event
• Support or contradict statements given by the victim, assailant or witnesses
about the event
• Support, contradict or provide additional information for post mortem findings
• Correlation with other laboratory findings relevant to the investigation
The Federal Bureau of Investigation examiner training program (2004) states that the
basic purpose of bloodstain analysis is to identify the sequence in which the bloodstains
and bloodstain patterns are produced. These bloodstain patterns can yield valuable
information concerning the events which led to their creation. The information gained
can then be used for the reconstruction of the incident.
3
Reconstruction and experimentation of a bloodshed event can range from simplified
exercises such as walking in blood, dropping blood from a pipette onto various types of
fabrics or to the design of elaborate computer systems to assist the bloodstain analyst
with stain measurements and virtual stringing. This experimentation and subsequent
reporting to the bloodstain community allows the behaviour of blood and its associated
limitations to be understood. Experiments must be designed with consideration given to
materials, methods and replication. This limits areas of uncertainty and error giving the
results the traceability, and if found to be reliable, acceptance by the courts (Makita
2001). Furthermore, the reconstruction must be used in conjunction with an
understanding of the science [mathematics, biology, physics and chemistry] (Eckert and
James 1998).
1.3 History of Bloodstain Pattern Analysis
As to the issue of being a “new” discipline, the examination and consideration of
bloodstain patterns and their historical acceptance in forensic science is well
documented (Bevel and Gardner 2002).
One of the first studies of Bloodstain Pattern Analysis is credited to a Polish scientist
Dr. Eduard Piotrowski in 1895. Dr Piotrowski’s work ‘Uber Entstehung, Form,
Richtung und Ausbreitung der Blutspuren nach Hiebwunden des Kopfes’ translates to
‘Concerning the Origin, Shape, Direction and Distribution of the Bloodstains Following
Head Wounds Caused by Blows’ (James et al. 2005). Piotrowski’s experiments utilised
live rabbits as a blood source and applied a variety of impact methods including a
hammer, stone and hatchet to provide key elements of BPA including the shape, size
and distribution of bloodstains in cast off and spatter patterns (MacDonell 1993).
In 1939 Balthazard et al. delivered a paper at the XXII Congress of Forensic Medicine
titled ‘Etude des Gouttes de Sang Projecte’ or ‘Research of Blood Spatter’. Balthazard
et al. (1939) has been attributed as the first to recognise the length to width ratio of a
bloodstain as being a function of the angle at which a blood droplet impacts a receiving
surface. This research established the basis for current stringing techniques and first
identified the effect of surface texture on the formation of bloodstains (Eckert and
James 1998; Bevel and Gardner 2002; James et al. 2005).
4
In 1953 Dr Paul Kirk published the book ‘Crime Investigation’ in which was the
chapter ‘Blood Physical Investigation’, which discussed the application of BPA with
respect to crime scenes. In 1955 Kirk prepared an affidavit for the case State of Ohio
vs. Samuel Sheppard. Prepared for the Court of Common Pleas, this expert evidence
was considered a significant milestone, paving the way for the application of BPA in
courts of law. In this affidavit Kirk describes the position of the accused, the victim at
the time of the bloodshed event, and the hand which had administered the blows (Bevel
and Gardner 2002; James et al. 2005).
In 1960 three basic categories of bloodstains and bloodstain patterns emerged in a
publication by Dr Jozef Radziki; ‘Saldy Krwi w Praktyce Sledczej’ which translates as
‘Bloodstain Prints in the Practice of Technology’. These categories were based on their
mechanism of construction and recorded by Bevel and Gardner (2002) as:
• Bloodstains resulting directly from extravasation – Drops, gushes, and pools of
blood
• Bloodstains resulting from the application of various instruments – spatter, cast-
offs, and pattern resulting from direct contact
• Bloodstains resulting from wiping or removal of blood
In the 1960’s BPA started to become recognised as a forensic discipline, with Professor
Herbert Leon MacDonell completing extensive worldwide research, resulting in the
documentation of over five hundred science based references relating to bloodstain
patterns and their analysis. These references were written in a variety of languages
including English, German, French, Spanish, Italian, Japanese, Russian, Hungarian and
Polish, with the earliest reference dating back to 1514.
In 1971 MacDonell conducted extensive research using a Law Enforcement Assistance
Administration (LEAA) grant and co-authored ‘Flight Characteristics and Stain
Parameters of Human Blood’ (MacDonell and Bialousz 1971). This was closely
followed, in 1973, by MacDonell’s first formal bloodstain training course and
associated laboratory manual. Since then hundreds of people have been trained in both
basic and advanced BPA courses (Eckert and James 1998; Bevel and Gardner 2002;
James et al. 2005).
5
In 1983 the International Association of Bloodstain Pattern Analysis [IABPA] was
founded by MacDonell and other Bloodstain Pattern Analysts. The IABPA promotes
the knowledge, training, education, techniques and general understanding of BPA. The
IABPA has more than 800 members worldwide and publishes quarterly newsletters to
allow members to keep up to date with contemporary BPA issues. In 2002 the Federal
Bureau of Investigation funded the formation of a scientific working group on BPA
called the Scientific Working Group on BPA [SWGSTAIN]. SWGSTAIN addresses
issues within the BPA discipline including education, training, legal, quality assurance,
research, taxonomy and terminology. Since the inception of IABPA in 1983, and the
development of SWGSTAIN in 2002, significant advancements in BPA have been
achieved, with numerous publications written to assist and guide the bloodstain analyst
(James et al. 2005).
1.4 Aims and Objectives
At present no research has been undertaken to determine the effect of the biological
properties of blood on the reconstruction of impact bloodshed events. This thesis aims
to examine the effect of one biological property of blood. The main objectives of this
study are:
(i) To determine the effect of hematocrit value on the angle of impact
calculation theory using single drop experimentation (Chapter 3).
(ii) To examine any error associated with the calculation of angle of impact for
bloodstains generated with different blood hematocrit values using both
manual and computer assisted measurement techniques (Chapter3).
(iii) To determine if the ability to predict the ‘Region of Origin’ of the blood
source is influenced by hematocrit value. This will be conducted using
three different industry accepted reconstruction methods: the String Line
Method [combined with Microsoft® Office Excel 2003 Auto Shapes], the
Tangent Method [combined with Microsoft® Office Excel 2003 Auto
Shapes] and computer assisted Directional Analysis Method [BackTrackTM
Images] (Chapter 4).
6
There are two hypotheses that this thesis will test:
1. If the impact angle calculation is related to the hematocrit value [amount of red
blood cells] then varying the hematocrit value should result in a deviation of
the experimental impact angle from the expected impact angle.
2. If the impact angle calculation is affected by the hematocrit value then the
ability of the analysts to reliably determine the three dimensional Region of
Origin for a blood source will also be affected.
7
2. LITERATURE REVIEW
2.1 Biological and Physical Properties of Human Blood 2.1.1 Section Introduction
This section will describe the biological and physical properties of blood. The
functions, composition, use of blood for experimental purposes, physical properties and
their interaction will be discussed in detail. For BPA a fundamental understanding of
the physical and biological properties of blood is necessary for the correct interpretation
of bloodshed events (Bevel and Gardner 2002).
2.1.2 Functions of Human Blood
It has long been recognised that blood is a truly unique substance that is the essence of,
and essential in, maintaining life (Marieb 2003). To date nothing artificially
manufactured has been able to replace blood and maintain life. One of the first written
accounts about blood can be attributed to Hippocratic writings from about 400 B.C.
which describe the body as a composite of four humors; black bile, blood, phlegm and
yellow bile. Ill health and disease were thought to be caused by upset in the balance of
these humors (McKenzie 1988). Modern medicine now recognises that a difference in
blood composition is a valid indicator of disease rather than the cause (McKenzie
1988).
Blood is a complex biological fluid that has the primary role of oxygen transportation
about the body. In addition, it performs the vital function of waste removal. Blood also
plays a significant role in wound healing as it is the primary carrier of immunity
responders and mediators of inflammation. Blood loss is prevented by a process known
as hemostasis (Dailey 2001). Hemostasis occurs via Vascular Spasm
[vasoconstriction], Platelet Plug Formation, Coagulation Protein Activation and Clot
Lysis [slow dissolution] (James et al. 2005). When these functions are not effective,
blood is lost from the closed vascular system to its external environment and can be
determined to be a blood shedding event.
8
2.1.3 Blood Composition
2.1.3.1 Introduction
Blood makes up an average of 7% to 8% of the human body weight [70ml ± 10ml per
kg of body weight] with average blood volumes ranging between four to five litres in
females and five to six litres in males (Eckert and James 1998). Blood is basically a
fluid with a suspension of solid particles [formed elements] in a liquid component
[plasma] (Ciofalo et al. 2002). With the invention of the microscope the varying
cellular components of blood were recognized and in 1852 Karl Vierordt published the
first quantitative blood cell analysis results (McKenzie 1988). There are three types of
formed cellular elements in blood [red blood cells, white blood cells and platelets],
which account for approximately 45% of total blood volume; the plasma component
contributes the remaining 55% (Rodak 2002). For every 500 red blood cells there are
about 30 platelets and 1 white blood cell (Passmore and Robson 1968). Erythrocytes
[red blood cells], leukocytes [white blood cells] and thrombocytes [platelets] are
morphologically different with each type of cell performing a set of specialized
functions within the body (Dailey 2001). The various components of whole blood can
be seen in Figure 2.1 and formation of a blood clot in Figure 2.2. After birth and
throughout life, mature blood cells are ‘made’ by the process of hematopoisis in the
bone marrow stem cells (Rodak 2002).
2.1.3.2 Red Blood Cells
Red blood cells [erythrocytes or red corpuscles] contain hemoglobin and transport
oxygen from the lungs throughout the body via the closed circulatory system. There are
approximately 4.2 x 106 – 6.2 x 106 red blood cells per mm3 of blood. This accounts for
99% of the formed element component. Woodcock (1976) estimates that 70% of a red
blood cell is water and 30% is hemoglobin. Red blood cells are non-nucleated, flexible,
bi-concave disks with a diameter of 7μm and a thickness of 2μm (Dailey 2001). The
volume of red blood cells varies between 80 x 10-15 to 100 x 10-15 L (McKenzie 1988).
Oxygenated blood travels via the arterial system, and is bright red in colour due to its
richness in hemoglobin. Blood containing carbon dioxide is returned to the lungs for re-
oxygenating via the venous system. The blood returning to the lungs is recognised by
being darker blue in colour due to the hemoglobin / carbon dioxide combination (James
et al. 2005).
9
Figure 2.1 Components of whole human blood (James et al. 2005:43).
Figure 2.2 Formation of a blood clot showing the red blood cells (red), activated platelets
(blue) and fibrin strands (yellow) (James et al. 2005:48).
10
The flexibility of a red blood cell is attributed to the complex chemical structure and
composition of its cellular membrane. The area of the flexible membrane is larger than
the minimum required to hold the cellular contents (Jay 1973), thus the membrane has
the ability to deform and change shape from 7μm to 3μm to assist flow in the small
capillaries such as those that exist within the spleen (McKenzie 1988). Damage or
alteration to the cellular membrane will cause premature death of the cell as red blood
cells lack the enzymes and cellular organelles required for cellular repair (McKenzie
1988).
Because red blood cells are non-nucleated they cannot be used for Deoxyribonucleic
Acid [DNA] analysis and do not undergo cellular division (Gardner 2005). The lifespan
of a red blood cell is 100 to 120 days. Red blood cells are heavier than plasma and this
can be seen when blood is left standing or has undergone centrifuging. This increased
weight can also be observed after death with the settling of the red blood cells, under
gravity, to the lowest region in the body. This settling is known as post mortem lividity
(Chmiel and Walitza 1980).
2.1.3.3 White Blood Cells
White blood cells [leukocytes or white corpuscles] fight infection and destroy old
cellular material. They can be divided into two sub-categories granulocytes and
nongranulocytes. Granulocytes are produced in the primitive stem cells in the bone
marrow and include neutrophils, eosinophils, and basophils, whereas nongranulocytes
are produced in the lymph nodes and include lymphocytes and monocytes. Each type of
white blood cell has a specific role to locate and destroy antigens [i.e. bacteria, viruses,
parasites] (Dailey 2001).
There are 4.5 x 103 – 11 x 103 white blood cells per mm3 of blood. Lower levels are
often observed in elderly individuals (Dailey 2001). Due to the relatively low number
of white blood cells, they have little effect on the physiological flow properties of
blood. They are the only nucleated cells within the cellular components of blood, thus
providing usefulness in both nucleic and mitochondrial DNA analysis for forensic
purposes (Gardner 2005).
11
2.1.3.4 Platelets
Platelets [thrombocytes] are the final cellular component of blood. The role of platelets
in blood coagulation was first established by Bizozero in 1882 (McKenzie 1998).
When platelets detect a damaged vessel they undergo a morphological response by
increasing in size and becoming adhesive to form a platelet plug (Dailey 2001).
Platelets amass at the damaged vessel wall creating a platelet plug that attempts to
restrict or stop blood flow from the circulatory system. Platelets develop in bone
marrow, are discoid in shape and range in size from 2μm to 4μm. Like red blood cells
platelets are non-nucleated cells and are unsuitable for DNA analysis. There are
approximately 150 x 103 – 450 x 103 platelets per mm3 of blood. The low percentage of
platelets in whole blood means they have little effect on the physical properties of blood
flow other than during a hemostatic event (Ganong 1991).
2.1.3.5 Plasma
Blood plasma is extra-cellular material that is the non-living liquid component of blood.
Plasma provides the medium for circulation allowing the transportation of blood cells
and solutes to the required tissue. Plasma accounts for approximately 4% of an
individual’s body weight or about 3.2 litres for an 80 kg adult. Plasma is comprised of
approximately 91% water, 8% soluble protein, 1% organic acids and salts (Bevel and
Gardner 2002). Albumin accounts for 60% of the plasma protein and contributes to
osmotic pressure (Marieb 2003). One type of protein is fibrinogen, which after physical
disruption of the blood vessel, is converted to fibrin and forms a gelatinous mass [clot]
(James et al. 2005). To provide stability, the formed clot undergoes constriction to
squeeze out the liquid plasma. Once the clotting factors are removed from plasma by
constriction, the liquid is then called serum and can be seen as a pale yellow liquid
around a retracted blood clot (Eckert and James 1998; James et al. 2005). From a
rheological perspective if the cellular component of blood is removed, the non living
plasma can be regarded as a Newtonian fluid and has a viscosity about 1.6 times that of
water (Ciofalo et al. 2002).
12
2.1.4 Physical Properties of Blood
2.1.4.1 Introduction
Students undertaking high school chemistry are taught that a liquid has a definite
volume but no shape, with liquids having the ability to flow and assume the shape of
their holding vessel (Zumdahl 1989). Vogel (1996) suggests ‘a fluid is just a synonym
for a liquid’ whilst Walker (2000) defines a fluid as: ‘any gas, liquid or particulate
solid that flows and can offer no permanent resistance to change of shape’.
The study of fluid deformation and flow is called Rheology and is inclusive of fluid
properties such as elasticity, plasticity, viscosity and Newtonian / Non-Newtonian
behaviour. BioRheology is the specialised study of the fluid dynamics of biological
fluid, including blood (Vogel 1996). Blood and other biological fluids are defined by
Vogel (1996) as complex, multidimensional continuum of Non-Newtonian fluids and
viscoelastic solids. To provide a basic understanding of blood dynamics with respect to
blood droplets during flight and the subsequent impact by a blood droplet with a
receiving surface the following physical properties will be discussed:
• Newtonian and Non-Newtonian Fluid Behaviour
• Surface Tension
• Relative Density
• Viscosity
2.1.4.2 Blood - A Non-Newtonian Fluid
A Newtonian fluid shows a linear relationship between applied shear stress and strain
rate [resultant deformation] (Vogel 1996). Water, petrol and ink are common examples
of Newtonian fluids and when the cellular components of blood are removed, the
remaining plasma can be regarded as a Newtonian fluid. In Non-Newtonian fluids such
as oil and paint the shear stress and strain rate are non linear, thus there is no constant
coefficient for viscosity (Vogel 1996).
13
Blood is also a Non-Newtonian fluid with its suspension of elastics cells [red blood
cells] in fluid [plasma] and a dynamic viscosity that is not dependent on shear strain
rate. Blood does not obey Newton’s Law, meaning that shear stress is not proportional
to the shear rate (Eckmann et al. 2000). Shear is a type of deformation in which parallel
planes in a volume of material remain parallel but are only displaced with respect to
each other. Measurement of viscosity of Non-Newtonian fluids is considered
meaningless unless it is related to the rate of shear under which the viscosity
measurement was taken (Ford and Furmidge 1963).
Blood exhibits complex rheological properties, such as viscoelasticity [because of its
viscous behaviour], elasticity [due to the deformation of red blood cells], thixotropic
response [the longer the fluid undergoes shear stress the lower the viscosity] and shear
thinning [decreasing viscosity with increased shear rate]. The viscoelasticity,
thixotropic and shear thinning behaviour of blood are all derived from the
microdynamics of the red blood cells (Ciofalo et al. 1999).
2.1.4.3 Surface Tension
Drop formation of a Non-Newtonian fluid such as blood can be attributed to the
following:
• surface tension [forces acting on the surface]
• cohesion [attractive forces between like molecules acting within the blood]
• viscosity (Wonder 2001)
This is shown in Figure 2.3.
14
Figure 2.3 Representation of surface tension on the surface and cohesion throughout a
spherical droplet (Wonder 2001:27).
Surface tension results from the molecules at the surface of the liquid experiencing a net
force directionally into the liquid, thus the unbalanced molecular cohesive forces near
the surface of the liquid give the appearance that the liquid surface is covered by an
elastic membrane (James et al. 2005).
Within the body, blood surface tension is considered a crucial part of many vital
functions, but few publications exist examining blood surface tension in any detail
(Rosina et al. 2007). This may be due to the fact that most studies investigate factors
effecting blood flow in a closed vessel of fixed diameter and its impact for
cardiopulmonary anaesthesia (Guber et al. 1999; Paut and Bissonnette 2001). In a
forensic context, Raymond (1997) suggests that the physical effect of surface tension in
a blood droplet upon leaving the body is also crucial. Surface tension is measured in
newtons per meter [N/m] and is the energy required to stretch a unit of change of a
surface area at the blood droplet’s surface and air interface (Raymond et al. 1996). In
the absence of any opposing forces, surface tension will form a drop of liquid into a
sphere, as a sphere offers the smallest surface area for a definite volume and prevents
the droplet from separating (Dillard and Goldberg 1978). Surface tension is expressed
as a force per unit length. Blood has a surface tension of 50dyn/cm whilst water is
72.5dyn/cm at 20°C (James et al. 2005). Raymond (1997) states that both the flight of
15
the blood droplet through air and the shape of the stain on the receiving surface are
directly influenced by surface tension.
Table 2.1 Surface tension of some common fluids (James et al. 2005:52).
Common Fluids Typical Surface Tension @ 20°C
(dynes/cm)
Ethanol 22.3
Soap 25
Olive Oil 32
Blood 50
Glycerine 63.1
Water 72.5
Mercury 465.0
The average blood temperature for a healthy adult is 37°C (Marieb 2003). Rosina et al.
(2007) investigated the effect of temperature on surface tension of blood for fifteen
human subjects. The influence of an increase in temperature of the blood resulting in a
decrease in the surface tension is shown in Figure 2.4. The results published by Rosina
et al. (2007) were further supported by Hrncir and Rosina (1997), who found a
significant, inverse linear relationship between surface tension and temperature which
could not be correlated to age, sex, red cell sedimentation rate, blood hemoglobin levels,
cholesterol or the number of red blood cells. Rosina et al. (2007) concluded that surface
tension monitoring in patients could assist in the appropriate adjustment of rheological
pharmaceuticals for different areas of biomedical investigations.
16
Figure 2.4 Influence of temperature on the surface tension of whole blood (Rosina et al. 2007).
2.1.4.4 Relative Density
With respect to a fluid, the term relative density [which has replaced the term specific
gravity (James et al. 2005)] is the measure of its weight per unit of volume (d = m/v).
Whole human blood has a density of 1.060g/cm3, which is higher than that of water
which has a relative density of 1.0g/cm3 (James et al. 2005).
2.1.4.5 Viscosity
Kalbunde (2005) states that the internal friction of adjacent layers sliding past one
another in a liquid, as well as the friction generated between the fluid and the wall of the
vessel, is called viscosity. The frictional force that exists between adjacent layers of
fluid as they move past one another creates the resistance to flow (Chaplin 2007). A
deformation [shear strain] occurs when force [shear stress] is applied to a volume of
material (Chaplin 2007). Increasing the concentration of a dissolved substance in a
fluid generally increases the viscosity (Chaplin 2007). For example increasing the
number of red blood cells suspended in a constant volume of plasma medium makes the
blood thicker thus increasing its viscosity.
17
There are several different coefficients of viscosity within a fluid. Vogel (1996)
describes dynamic viscosity with respect to a stack of individual sheets of paper.
Dynamic viscosity is the friction encountered between the sheets and can be referred to
as the interlamellar stickiness of the fluid. Dynamic viscosity is shortened to viscosity,
but is sometimes referred to as absolute viscosity. If the dynamic viscosity of a fluid is
independent of shear strain rate the fluid is defined as Newtonian and if the shear strain
rate was plotted against shear stress the resultant graph would be linear passing through
the origin (Kim 2002; Chaplin 2007). Blood is a Non-Newtonian fluid with viscosity
depending on shear rate, with viscosity decreasing as the shear rate increases (Wonder
2001; Eckmann et al. 2000). The ratio of dynamic viscosity to density is called
Kinematic Viscosity (Vogel 1996).
Viscosity [η] can be defined as Equation 2.1
η = shear stress [Pa s] shear rate The units are either pascal seconds [Pa s] or the poise [P]
The viscosity of blood undergoing circulation is dependenton the shear rate within the
vessel it is travelling, that is the size of the vessel through which it flows. This is
known as the Fahraeus-Lindquist effect (Eckmann et al. 2000; Paut and Bissonnette
2002). For example, in large arteries with large blood flows the shear rates are high
compared to shear rates in microcirculation (Eckmann et al. 2000). Blood flowing
through a vessel of 2mm in internal diameter has a viscosity that is close to plasma
(Paut and Bissonnette 2002). The ability of blood to change viscosity according to the
vessel through which it is flowing has been attributed to the colloidal properties [the
suspension of cells within plasma] of blood which prevent clumping or coagulation.
Figure 2.5 shows the effect of hematocrit percent on blood viscosity through two
different size vessels.
18
Blood traveling within blood vessels undergoes a process known as stratified axial
streaming (Wonder 2001). The red blood cells cluster along the blood vessel core,
whilst the plasma and platelets circulate around the circumference closer to the
endothelium. Most of the friction in a flowing fluid occurs near the endothelium where
the rate of shear is higher. The different layers travel through a vessel at different
velocities thus experiencing different viscosities. The axial streaming reduces the
power required for blood to travel around the circulatory system (Johnson 1999), and
provides stability reducing turbulence and preventing droplet instability (Wonder 2001).
Figure 2.5 Effect of hematocrit on blood viscosity flowing through small and large tubes
(Johnson 1999:135).
2.1.5 The Interaction between Biological and Physical Properties of Blood
2.1.5.1 Hematocrit Value and Viscosity
Different fluids have different viscosity. Blood is 5 times more viscous than water
(Marieb 2003) and plasma is about 1.6 times the viscosity of water at 37°C (Kalbunde
2005). However, blood viscosity is not constant (Lowe 1988) and is dependenton
hematocrit percent, plasma, time, temperature and shear rate (Johnson 1999; Wonder
19
2001; Eckmann et al. 2000; Paut and Bissonnette 2002). Therefore, viscosity is
considered a function of hematocrit value (Raymond 1997; Bevel and Gardner 2002).
Wonder (2001) stated that two components, the plasma and the red blood cells, are
important for BPA. The volume of red blood cells in relation to the volume of plasma
is commonly expressed as a percentage called Hematocrit [Hct] or Packed Cell Volume
[PCV] (Rodak 2002). In adults, hematocrit values can range from 35% to 54%. The
hematocrit value can be used as a rough indictor for the oxygen carrying capacity of
blood (Dailey 2001).
The Fahraeus-Lindquist effect confirms that the viscosity of whole blood varies with the
hematocrit value (Johnson 1999; Paut and Bissonnette 2002). As the hematocrit value
increases there is a disproportional increase in viscosity; for example a 50% increase in
the hematocrit value can result in a 100% increase in blood viscosity. At a hematocrit
value of 40% the viscosity is 4 whilst a hematocrit value of 60% the viscosity is 8
(Kalbunde 2005). The influence of the hematocrit value on relative viscosity of whole
blood is shown in Figure 2.6.
Figure 2.6 Influence of hematocrit value on the relative viscosity of whole human blood
compared with that of plasma (Klabunde 2005).
The viscosity of human blood increases at lower temperatures, especially those below
15°C (Eckmann et al. 2000). Klabunde (2005) found that a temperature decrease of
20
1°C increases viscosity by approximately 2%. This increase is due to a corresponding
decrease in red blood cell deformability and an increase in plasma viscosity (Kim 2002).
During specialised surgical procedures, such as a cardio pulmonary bypass, the body
temperature of the patient is often lowered to increase the viscosity of the blood
preventing excessive blood flow (Gruber et al. 1999).
2.1.5.2 Variance of Hematocrit values within the Population
Hematocrit value can vary according to age, sex, ethnicity, illness and environment with
normal variation occurring between healthy individuals. Males have a range of 40% to
54%, whereas for females the range is 35% to 40% (Rodak 2002).
It is difficult to obtain a ‘normal range’ or ‘normal value’ for hematology results
obtained from a population. This problem arises due to the variation in health amongst
individuals at the time of blood donation (Dacey and Lewis 1991). It is for this reason
that the terms ‘reference values’ and ‘reference ranges’ are often used in association
with the physiological variables of a sample population. Dacey and Lewis (1991) found
that a number of factors can affect the reference ranges and normal values in
hematology. These include:
• Physical parameters of the sampled subjects, sex, age, build, and ethnic
background
• Sampling conditions
• Specimen collection, timing and storage
• Inherent variation in analytical methods and instrumentation
Donors of blood at a crime scene can differ in hematocrit values from that of the
suggested “healthy or reference range”. Alcoholics, drug and steroid abusers, women
following miscarriages, child birth or abortions, malnourished people and the elderly
can have decreased hematocrit value ranging from 29% to 15%. When the total red
blood cell amount, mass, hemoglobin and hematocrit values decreases by more than
10% a person is diagnosed with anemia (Rodak 2002).
Anemia results in a decrease in oxygen transport due to reduced oxygen carrying
capacity of blood (Birchard 1997) and a corresponding decrease in blood viscosity
(Eckmann et al. 2000). Insufficient red blood cells are present to provide adequate
oxygen transportation to body tissues. Anemia is not a disease, but rather an indicator
21
of a disease somewhere in the body (Marieb 2003). Iron deficiency, autoimmune
diseases, rupturing of the red cell membrane and vitamin deficiencies are all causes of
anemia. Symptoms of anemia include weakness, dyspnea, headache, heart palpitations
and sluggishness, however some suffers will not display any symptoms and often are
unaware of the condition (Dailey 2001). Anemia can also occur as a result of
chemotherapy, during pregnancy and following massive blood loss.
Conversely, raised hematocrit values indicate excessive numbers of red blood cells,
which make the blood thick, increases blood viscosity and impairs circulation (Marieb
2003). These raised values can occur if an individual is dehydrated, in shock, living at
high altitudes, experiencing or about to experience a heart attack, suffering from
hypothermia and following extreme physical activity (Wonder 2001).
Abnormal increases in the number of red blood cells is known as Polycythemia vera
[PV] and can also be experienced by neoplastic blood conditions and prolonged
exposure to high altitudes, where less oxygen is available for cellular intake (McKenzie
1988). In order to maintain the levels of oxygen required for survival, the body
increases red cell production which increases the total amount of red blood cells
compared to plasma [hematocrit value] (Ge Miao 2003). In cases of reduced
atmospheric pressure the body responds by the renal tissue producing the hormone
erythropoietin, which is released into the peripheral blood and stimulates erythropoiesis
[red cell production] in the bone marrow (McKenzie 1988). Although it has been
documented that people living at high altitudes have a higher hematocrit value their
plasma volume is similar to those living at lower altitudes (Claydon et al. 2004).
Hematocrit values also vary with age. Rodak (2002) describes that healthy or normal
hematocrit values are naturally high at birth (45% to 65%), but fluctuate throughout
childhood until about fifteen when the adult average is reached (see Table 2.2).
22
Table 2.2 Hematocrit values (percent) for males and females throughout childhood (Rodak
2002:162).
Age Hematocrit (%)
Male Female Birth to 1 week 45 - 65 45 - 65 1 week to 2 month 37 - 54 37 - 54 2 month to 12 months 31 - 42 31 - 42 12 months to 3 years 33 - 45 33 - 45 3 years to 8 years 32 - 43 32 - 43 8 years to 15 years 33 - 48 33 - 48 15 years to adult 40 - 54 37 – 47
2.1.6 The Use of Human Blood for Experimental Purposes The primary reasons for the use of human blood substitutes, such as ink, paint, glycerol,
dye and animal blood, in past experimental studies are twofold: the inherent dangers of
handling human blood and its availability (Raymond 1997). Blood borne pathogens
such as AIDS and hepatitis are a constant threat to both researchers and crime scene
investigators (Christman 1996; Dailey 2001). As a result, animal blood is most
frequently used for experimental purposes. Validation studies using equine, bovine,
ovine, and porcine blood have been undertaken. The results from these studies indicate
that the viscosity, density and surface tension are relatively similar to human blood,
whilst the “normal range” of hematocrit values differs as shown in Table 2.3 (Raymond
1997; Christman 1996).
Table 2.3 Normal hematocrit values for a variety of mammalian species (Christman 1996).
Species Hematocrit (%)
Male Female Human 42 - 50 40 - 48 Equine 32 - 52 Not Available Bovine 24 - 46 Not Available Porcine 24 - 50 Not Available Ovine 35 - 45 Not Available
Whilst Christman (1996) and Raymond (1997) have demonstrated that for experimental
purposes, animal blood is a suitable alternative to human blood, Wonder (2001) states
that no other substitute behaves like human blood for experimental research purposes.
23
2.1.7 Section Conclusions
This section discussed both the biological and physical properties of blood. Blood is a
complex biological fluid that is essential in maintaining life and to date, no suitable
substitute exists. To truly understand the behaviour of blood at crime scenes, a
comprehensive knowledge of the effect of blood’s underpinning biological and physical
properties is imperative. One of the most important biological properties of blood is its
hematocrit value [amount of red blood cells], but the effect on the formation of
bloodstains and bloodstain pattern has not been sufficiently investigated in the past.
24
2.2 Blood Dynamics
2.2.1 Section Introduction In the past extensive research has been undertaken into aqueous droplet dynamics and in
the early applications of BPA this knowledge was applied to, but not replicated to the
same extent, with blood droplets. As noted in Section 2.1, blood is a complex biological
fluid, which once outside the body, is influenced by a number of complex mechanisms.
The purpose of this section is to provide the reader with a basic understanding of blood
droplet formation, flight characteristics and surface impact dynamics.
2.2.2 Blood Droplet Dynamics in Flight
Raymond et al. (1996) describes five factors which are known to influence the shape of
a liquid droplet:
• surface tension
• hydrostatic pressure
• aerodynamic pressure
• internal circulation
• electric stress
Surface tension has previously been discussed in Section 2.1.4.3; internal circulation
and electrical stress are also believed to have minimal effect on droplet shape
(Pruppacher and Beard 1970; Raymond et al. 1996). Both hydrostatic and
aerodynamic pressures for bloodshed at a crime scene are more complicated than the
aqueous droplet model due to changes in droplet velocity and direction during flight
(Raymond et al. 1996). For both projected and passive blood droplets during flight two
main factors affect trajectory; gravity and air resistance (Carter and Podworthy 1991;
Carter 2003).
It has long been understood that gravity draws everything towards the Earth’s centre
(Wonder 2001). The law of universal gravitation was first published by Newton in
1687 and states that: every point mass attracts every other point mass by a force
pointing along the line intersecting both points. The force is proportional to the
product of the two masses and inversely proportional to the square of the distance
25
between the point masses. Downward acceleration is experienced by all objects due to
the gravitational force exerted upon it by the Earth. The Earth’s force of gravity,
ignoring air resistance, has an approximate value of 9.81m/s² (Serway and Beichner
2000). That means that the acceleration for a free falling blood droplet, falling near the
Earth’s surface, increases by approximately 9.81 meters per second, for every second in
flight. If an object falls in a vacuum the rate of fall is not dependenton the mass of the
object. If that same object falls in air, the rate of fall is dependenton both the mass and
shape of the object [the effect of air resistance] (Wonder 2001).
Air resistance [air friction] is the force that resists the movement of an object due to the
viscosity of air (Carter and Podworthy 1991). Air resistance is related to both the size
and speed of the droplet. An increase in droplet size corresponds to a resulting increase
in droplet mass and momentum when travelling through air. Thus, a larger drop created
on the same trajectory and initiation velocity will travel further because it has increased
momentum and is less affected by air resistance (Reynolds 2008). The direction of
force of air resistance is opposite to the direction of motion of the droplet and changes
as the droplet proceeds along its curved flight path. Given enough time, the droplet
falling through air will reach a constant velocity [terminal velocity]. The droplet will
cease to accelerate as the air resistance is equal to the gravitational pull on the droplet.
According to James et al. (2005) the terminal velocity of 50µL drop of blood is 25.1
ft/sec. Smaller drops will reach terminal velocity quicker than larger drops.
The net result of the forces of gravity and air resistance on the ballistic path of a blood
droplet, projected with some horizontal and vertical velocity, is always represented as
some portion of a parabola [ballistic curve] (Giancoli 1991). Figure 2.7 shows
parabolic arc for a droplet affected by both gravity and air resistance.
26
X
Z
Z0
X0
Parabolic Arc (2 dimensions)
Y
Z
X(x,z,y)
Figure 2.7 Parabolic flight path position instants for a droplet with gravitational force
indicators (blue arrows) and air resistance (pink arrows) (adapted from Carter
2003:8 by Reynolds 2008).
As mentioned in Section 2.1.4.3, surface tension and strong internal cohesive forces
cause a blood droplet to adopt a spherical shape. However, in their early flight history,
blood droplets deviate from this spherical shape due to oscillations (Pizzola et al. 1986;
Pizzola et al. 1986a; Raymond et al. 1996a). An oscillation is cyclical movement
within the mass of the liquid drop resulting in distortion from the spherical shape
(Pizzola et al. 1986; Raymond et al. 1996a; Bevel and Gardner 1997). Figure 2.8 shows
the sphere undergoing an alteration of its length [oblate phase] through to an alteration
of its width [prolate phase] during flight.
27
Figure 2.8 Prolate, spherical and oblate forms of an oscillating blood droplet during flight
(adapted from Raymond et al 1996a: image by Natasha Rogers).
Using high speed photographic recording techniques, Raymond et al. (1996a) were able
obtain width and length measurements of an oscillating blood droplet through its prolate
and oblate flight phases. Both free falling [passive] and projected blood droplets
oscillated strongly during the first few centimeters of flight (Raymond et al. 1996a).
For free falling droplets no detectable oscillation was present after 40cm compared with
100cm for projected blood droplets. A 13% to 22% droplet distortion from the rest
position was observed, with the period of oscillation decreasing as droplet size
decreased. Droplet viscosity is also thought to assist in the dampening of oscillations,
with a blood droplet’s oscillation dampening approximately four times faster than a
water droplet (Bevel and Gardner 1997). Due to the effect of oscillation, an elliptical
blood droplet impacting a perpendicular surface during the prolate oscillation phase
could potentially produce a stain that is similar to that produced by a spherical blood
droplet impacting an angular surface. Subsequently, a blood droplet contacting a
surface too close to release is still likely to be undergoing periodic oscillation and this
may produce a stain of unpredictable shape (Raymond et al. 1996a). Measurement of
these stains will lead to calculation error, therefore the distance from the Region of
Origin [ROO] should be considered when determining appropriateness of bloodstains
for reconstruction measurements.
Prolate Spherical Oblate
28
2.2.3 Blood Droplet Impact Dynamics
Pizzola et al. (1986) were the first to recognise, and specifically describe, a series of
different phases of the dynamic collision of a blood droplet with the solid surface.
Through the use of slow motion photography, Pizzola et al. (1986) formulated the
following description: at contact with the impacted surface, the distortion of the drop is
limited to the lower area but it gradually collapses downwards with respect to the target
surface. The fluid is displaced and forced out forming a rim around the circumference.
The surface tension stops the lateral spreading of the drop with the centre of the stain
depressed. Following the depression the fluid retracts and progresses forward to the
leading edge. If the velocity is sufficiently high, a droplet can separate from the parent
stain creating a satellite spatter.
More recently Bevel and Gardner (2002) separated the impact of a blood droplet on
surface into four distinct phases:
1. Contact and Collapse: The first phase is where a droplet contacts with the
receiving surface and begins to collapse from the bottom up. As the collapse
occurs the blood is forced outwards to the edge of the droplet rim.
2. Displacement: Once the bloodstain has collapsed at the displacement stage, the
majority of the blood has dispersed from the middle of the stain to the boundary.
On the boundary rim are short spines or dimples. These dimples have the
potential to form satellite spatter in the next phase. The area at this stage will
determine the final overall dimensions of the resultant stain. The impact angle
effects the development of dimples. With acute impact angles dimples develop
at the forward stain edge.
3. Dispersion and Expansion: Occurs as the blood is forced into the rims and rises
upwards in an opposite direction of momentum. If the liquid becomes unstable
small droplets of blood may detach and form satellite spatter.
4. Retraction: Due to the surface tension, the droplet will attempt to pull back or
retract from the boundary to a single form. If surface tension is overcome, a
29
portion of the stain will radiate away from the centre causing a spine. If this
liquid breaks away a satellite spatter is formed.
These phases, especially contact and displacement, are dependenton the droplet’s
volume, velocity, height, surface texture of the impact surface and the angle of impact
(Bevel and Gardner 2002). These influencing factors have been shown to be consistent
across most fluids (Yarin 2006). Balthazard et al. (1939) was the first to identify the
relationship between dropping distance, volume and the resultant bloodstain shape.
Droplets produced from lower heights caused circular stains, whilst increasing the
dropping distance resulted in an increased number of spines on the fringe of the stain.
Balthazard et al. (1939) were unable to separate the effects of height and drop volume
on the resultant stain.
Laber (1985) stated that there was no standard volume of a blood droplet generated by a
blood letting event and indicated that when the volume of the droplet increased the
diameter of the resultant stain also increased. MacDonell (1990) proposed that because
different surfaces and circumstances produced blood droplets of differing volumes, the
height from which a droplet originated could not be determined. Pizzola et al. (1996)
and Raymond (1997) supported Laber’s proposition that a standard drop volume was
non existent, rather the volume of any drop is a function of the type, shape and area of
the object from which the droplet falls.
As with dropping height, the velocity of a blood droplet cannot be determined by the
diameter of the resultant stain, as the size of the stain depends on the combined factors
of velocity and drop height (Hulse-Smith et al. 2005). Hulse-Smith et al. (2005)
showed that a 3.7mm diameter droplet released from 30.5cm above the target surface
produces a bloodstain almost identical to a bloodstain produced by a 3.0mm droplet
falling from 121.9cm.
Of all the factors affecting resultant stain shape on a target surface, texture has been
shown to have the greatest influence (Raymond 1997; Eckert and James 1998; Bevel
and Gardner 2002; Hulse-Smith et al. 2005). Wonder (2001) states that surfaces such
as carpet, concrete and clothing produce irregular shaped bloodstains that are spiked
along the outer edge. In contrast, smooth surfaces such a gloss painted walls and floor
tiles produce bloodstains with smooth clearly defined edges. Hulse-Smith et al. (2005)
30
demonstrated that surface roughness promoted fluid instabilities leading to the creation
of spines and that increasing the roughness of a receiving surface reduced both the
resultant stain diameter and number of spines. The decrease in spine number was
proposed to be a result of the analyst’s inability to distinguish individual spines. Hulse-
Smith and Illes (2007) created surface specific equations, using the bloodstain
parameters and the number of spines, to determine impact velocity and droplet volume.
Knock and Davison (2007) stated that it was possible to determine the impact velocity
and position of the blood source for an impact on paper by measuring the stain size and
number of spines, especially for angled surfaces.
2.2.4 Section Conclusions
Gravity, air resistance and droplet oscillation are all important factors influencing a
blood droplet during flight and the resultant bloodstain shape; therefore, all must be
considered when selecting stains for crime scene reconstructive purposes. Using stain
diameter and number of spines, analytical models have been developed to determine
velocities, drop volume and drop height for particular surfaces. However, the properties
of the receiving surface have been shown to have the greatest effect on resultant stain
diameter and spine production. As time proceeds, an increasing number of papers are
being published concerning droplet flight and impact dynamics but none mention the
potential errors introduced in to the calculations by uncertainties in the biological and
physical properties of blood.
31
2.3 Reconstructive Techniques for Impact Spatter Patterns 2.3.1 Section Introduction Bevel and Gardner (2002) define reconstruction as:
‘...the end purpose of crime scene analysis; it requires not only
consideration of events identified, but whenever possible the sequence of
those events’.
A Bloodstain Pattern Analyst must have the underpinning skills, knowledge and
training to enable the recognition, collection and formal analysis of the evidence (Bevel
and Gardner 2002). This first part of this Section will focus on the foundations that
form the basis of 2-Dimensional [2D] and 3-Dimensional [3D] reconstructions, these
include: pattern identification, calculation of impact angle, bloodstain measuring and
selection. The final part of this section discusses the various reconstructive techniques
available to the Bloodstain Pattern Analyst to determine the blood source origin in
either 2D or 3D space and achieve the objectives of bloodshed reconstruction.
2.3.2 Objectives of Bloodshed Reconstruction
Eckert and James (1998) and Bevel and Gardner (2002) propose that, with careful
examination of an impact spatter pattern the Bloodstain Pattern Analyst may be able to
determine the following:
• The direction a given droplet was travelling at the time of impact
• The angle of impact
• The probable distance from the target from which the droplet originated
• The nature/direction of the force involved in the bloodshed
• The nature of any object used in applying the force
• The approximate number of blows struck during the incident
• The relative position within the scene of the suspect, victim, or other objects
during the incident
• The sequence of multiple events associated with the incident
32
For bloodstain reconstructive purposes it is important for an analyst to determine the
blood source location at the time of pattern creation (Bevel and Gardner 2002). It may
be possible for a bloodstain analyst to determine the blood source location within 2D
[Area of Convergence [AOC]] or 3D [ROO] space.
The correct interpretation of physical evidence is crucial for crime scene reconstruction
(Raymond et al. 2001). Raymond (1997) suggests when attempting to determine a
ROO within a crime scene, the Bloodstain Pattern Analyst may use techniques based on
a number of fundamental assumptions, namely;
• That, just prior to impact on a target surface, the blood droplets are spherical
• That the blood droplets do not coalesce or fragment in flight
• That any spin imparted on the drops does not cause significant trajectory
variation
• That error associated with the measurement of the droplet stains both, from the
measurement system itself, and the decision as to what actually constitutes the
measurement length of any stain is insignificant
• The application of [sin-1 (width/length)] equation for deriving a blood droplet
angle of impact is an accurate representation of the relationship irrespective of
resultant stain parameters
2.3.3 Terminology and Categories of Bloodstains
In order to understand the types of stains and their probable causes, it is useful to
classify stains into categories so that they can be assessed in groups with some degree of
similarity. The use of consistent terminology enables analysts to communicate
effectively around the world. Originally bloodstains and bloodstain patterns were
classified according to the amount of force that created them [high, medium and low]
(Eckert and James 1998; Bevel and Gardner 2002; MacDonnell 2005). More recently a
bloodstain pattern can be identified and classified from the stain’s physical appearance
(size, shape, distribution and location) and the potential mechanism by which the stain
was created (James et al. 2005). Recently, James et al. (2005) developed a taxonomic
key for bloodstain patterns which divides bloodstains into three main categories;
passive, spatter and altered (Figure 2.9).
33
Figure 2.9 Major Bloodstain Pattern Categories (James et al. 2005: 69).
Passive stains are created without any significant outside force other than friction and
gravity; for example, a droplet of blood falling from an exposed wound. Altered
patterns, as the name suggests, have undergone a physical or physiological change, such
as blood and water combination dripping from a washed hand. Spatter patterns are
created when a liquid source of blood is subject to an external force and that force is
sufficient to overcome the physical properties of blood [surface tension and viscosity],
resulting in its distribution through air in droplet form. Spatter can be created by a
variety of mechanisms (James et al. 2005) ranging from an application of force [impact]
to a liquid blood source or liquid blood dripping into blood [satellite stain].
When the Bloodstain Pattern Analyst assesses a bloodstain pattern and it is classified as
spatter, the spatter can further be divided into three categories; secondary, impact and
projected (James et al. 2005) (Figure 2.10). It is the category of impact spatter that is of
most interest for 2D AOC and 3D ROO determinations. Impact patterns result when a
source of liquid blood receives an application of force [impact] resulting in the random
distribution of smaller droplets through the air until their eventual deposition on
adjacent receiving surfaces. The combination of all the individual stains generated by
the same impact or applied force is called a pattern (James et al. 2005). These spatter
patterns will exhibit features that can be used by the analyst to determine the mechanism
of pattern construction, the AOC and/or ROO.
34
Figure 2.10 Bloodstain pattern analysis “Spatter” sub categories (James et al. 2005:108).
2.3.4 Area of Convergence [AOC]
The AOC as defined by Raymond et al. (1997) is the
‘…area to which a bloodstain pattern can be projected on a two-
dimensional surface. This point or area is determined by tracing the
long axis of well-defined bloodstains back to an axis constructed through
the point or Area of Convergence’ (Figure 2.11).
The lines will intersect in an area from which the droplets have originated
demonstrating a 2D geographical location. It is highly unlikely this will be a point, as
no two drops originate from exactly the same point of a blood source (Wonder 2001;
Bevel and Gardner 2002; James et al. 2005).
It is often possible to determine multiple areas of convergence if the blood source
moved between each impact however if the blood source remained in the same position
during repeated force applications those multiple areas of impact will be
indistinguishable. Wonder (2001) states that constructing a accurate AOC is less time
consuming, easier and more accurate for blood / tissue mixes than individual stain
measurements and 3D determination. AOC are always on surfaces whilst the ROO is
located in 3D space (Wonder 2001).
35
Figure 2.11 Two dimensional Area of Convergence [AOC] determination from multiple
bloodstains originating from a single impact (James et al. 2005:218).
2.3.5 Region of Origin [ROO]
The ROO as defined by Raymond et al. (2001) is ‘the three-dimensional area from
which the blood that produced a bloodstain originated. This is determined by
projecting angles of impact from well-defined bloodstains back to an axis constructed
through the point or Area of Convergence. To determine the ROO it is possible to
combine the area convergence with calculated impact angles of selected stains to
determine a ROO in 3D space [X, Y and Z coordinates]. The X coordinate value relates
to the distance away from the spatter bearing surface. The Y coordinate value is vertical
distance from a reference point, usually the spatter bearing surface intersection with the
left wall. The Z coordinate value is the horizontal plane height above a horizontal
reference surface, which is usually the floor (Figure 2.12).
The three techniques for determining ROO are the Tangent Method, String Line Method
or the computer assisted Directional Analysis Method using BackTrack™. Selection of
technique is usually determined by the analyst’s personal preference, availability of
computer assisted methods or limitations associated with the crime scene (James et al.
2005).
36
Figure 2.12 The X, Y and Z coordinate values (image by Natasha Rogers).
2.3.6 Angle of Impact
The acute angle that is formed between the direction of a blood drop and the plane of
the surface it strikes is referred to as the Angle of Impact (Eckert and James 1998;
James et al. 2005) with the trigonometric relationship between the angle at which a
blood droplet impacts a surface and the resultant stain length/width ratio being well
documented (Balthazard et al. 1939; Eckert and James 1998; Bevel and Gardner 2002;
James et al. 2005).
According to MacDonell (1993) it was Florence and Fricon in 1900 who first
investigated the “cause and effect” relationship between angle of impact and stain
length/width ratio. However, the introduction of the theory which became essential in
BPA has been attributed to Balthazard et al. (1939). Their experimentation showed that
a predictable relationship exists between the width to length ratio of a stain and the
angle of impact. MacDonell and Bialosz (1971) also demonstrated the predictable
relationship between the length/width ratio of the resulting bloodstain and the angle at
which the blood droplet struck the static surface.
Z
X
Blood source
Spatter Bearing Surface
Y
37
Figure 2.13 Width to length ratio of a bloodstain equated to a right angle triangle (James et al.
2005:221).
Figure 2.13 illustrates the mathematical relationship between the blood droplet and
resultant bloodstain. Using the properties of the generated right angle triangle the angle
of impact [θ] at B is the same as at point A. The line termed AB [hypotenuse] is equal
to the length of the stain whilst the line BC equates to the width. The 90° angle is
created at point C. The sine of angle θ can then be determined by Equation 2.2.
Sine of angle theta [θ] = opposite / hypotenuse Equation 2.2
But to determine the angle of impact the arcsine of the angle θ is determine by Equation
2.3.
Angle of Impact = arcsine (width / length) Equation 2.3
(MacDonell and Bialousz 1971; Pizzola et al. 1996a; Eckert and James 1998; Carter
2001; Willis et al. 2001; Raymond et al. 2001; Bevel and Gardner 2002; James et al.
2005).
38
The shape of a bloodstain on the impacted surface depends on the angle of impact. The
largest possible angle generated by a droplet surface impact is 90°, which occurs when
the droplet impacts a perpendicular surface. At an impact angle of 90° the shape of the
resultant stain is circular with the length and width measurement being equal (Wonder
2001; James et al. 2005). As the angle of impact becomes more acute the resultant stain
becomes more elongated with a longer measurement along the line of travel [long axis]
(Wonder 2001; James et al. 2005). This long axis, along with the presence of satellite
stains, scallops and spines allows the directionality of a stain to be determined (Bevel
and Gardner 2002). Directionality will be more accurately established when the
resultant stain is more elliptically-shaped on smooth target surface (Bevel and Gardner
2002).
Even though angle of impact can be determined by stain parameters, Pizzola et al.
(1986a) conducted a series of experiments comparing blood droplets that fell onto a
stationary inclined surface with those that fell on a target surface moving horizontally.
They constructed a belt device moving horizontally with an adjustable velocity. Pizzola
et al. (1986a) concluded that the stain parameters produced by a droplet falling
vertically and striking a horizontal surface has the equivalence of stain parameters
produced by a droplet projected onto a moving horizontal surface with adjustable
velocity. Therefore if the target surface is subject to motion it is difficult to establish
true angle of impact, thus the stain may have limited use for reconstructive purposes.
2.3.7 Measuring Bloodstains
One of the most important skills a Bloodstain Pattern Analyst can possess is the ability
to accurately measure the width and length of bloodstains in order to calculate the angle
of impact (Chafe 2003). Before the stain parameters can be measured the analyst must
determine the leading edge of the stain using directionality indicators (James et al.
2005) (Figure 2.14).
39
Figure 2.14 Directionality of bloodstain established with location of the leading and terminal
edges (scale in mm, photograph by Natasha Rogers).
The next step is to determine the maximum width for the bloodstain. Once the
maximum width for the bloodstain has been determined the distance between the centre
point of the bloodstain width line and the leading edge can be found. This distance
between the centre point of the width line and the leading edge is doubled and the
ellipse is superimposed on the bloodstain (Figure 2.15) (Reynolds 2008). Only the
main body of the stain is measured with any scallops, spines or satellites excluded
(Bevel and Gardner 2002; Chafe 2003). The alternative technique of manual
measurement involves the use of electronic calipers [or scaled loupe] with or without
optical assistance (Bevel and Gardner 2002).
Leading Edge
Terminal Edge
40
Figure 2.15 Measurement of the width and length of an elongated bloodstain by fitting a
theoretical ellipse (scale in mm, photograph by Natasha Rogers).
Unfortunately the analyst must judge the best fitting ellipse or manually measure the
length and width and this subjectivity having associated analyst error (Bevel and
Gardner 2002; James et al. 2005). Improper and inaccurate stain measurement can
greatly effect the calculated impact angle. Laturnus (1994) found that the greatest
source of error for manual measurement is the “overestimation” of ellipse length, hence
an “underestimation” of the angle of impact. Bevel and Gardner (2002) suggest that an
error rate for angle of impact calculations of between 5° and 7° is acceptable for
bloodstain reconstruction purposes.
Willis et al. (2001) mathematically determined the potential error rates in the estimation
of angle of impact using a variety of angles of impact 15°, 30°, 45°, 60° and 75°. He
concluded that for reconstructive purposes, stains which impacted the surface at low
angles should be chosen to reduce the potential error rate. These results were supported
by McGuire and Rowe (2004) who stated that stains produced from an impact angle of
80° to 90°, when manually measured, deviated by as much as 19° from the known angle
of impact and therefore should not be used for reconstruction purposes. In the same
study McGuire and Rowe (2004) also disproved a previous hypothesis that stated
impact angles of 10° also deviated considerably from the known impact angle.
41
A recently developed alternative to the manual measurement of bloodstain uses the
Microsoft® Office Excel 2003 Auto Shapes to improve the levels of accuracy and
precision of bloodstain measurement (Reynolds and Raymond 2008). Unlike other
specifically developed computer software programs this program is readily accessible
and requires minimal training (Reynolds and Raymond 2008).
The bloodstain to be measured is first photographed and then imported into a
specifically designed Excel worksheet. The image can be cropped and adjusted
according to the analyst’s requirements. A series of user-friendly macro’s allows grid
lines, a long axis line and ellipse to be placed over the bloodstain (Figure 2.16). The
width to length ratio is established and placed into a tabular master worksheet. The
master worksheet can then be used for statistical analysis or presentation in court should
the specific measurements be required or questioned (Reynolds and Raymond 2008).
Figure 2.16 Measurement of the width and length of a bloodstain using Microsoft® Office
Excel 2003 Auto Shapes (image by Natasha Rogers).
2.3.8 Stain Selection
James et al. (2005) states that there is no ‘magic’ number of bloodstains that should be
selected in order to make a determination of the AOC or ROO. Raymond et al. (2001)
42
suggests at least 12, whilst Carter (2001) advises a greater number of between 18 to 20.
Although analysts cannot agree on the number of stains that should be selected they do
all state that a representative sample of the whole impact spatter pattern should be
obtained by selecting equal numbers of bloodstains from each side of the impact spatter
pattern (Reynolds 2008).
All methods used for determining the ROO using trigonometry, assume that droplet
flight paths are straight lines (James et al. 2005). The determination of the suitability of
a bloodstain for reconstructive purposes can be established by the relative position of
the bloodstain within the pattern and the geometry of the bloodstain itself. Thus for
reconstructive purposes those well formed elongated bloodstains whose flight paths are
approximating a straight line trajectory should be selected (Raymond et al. 2001; Bevel
and Gardner 2002; James et al. 2005). If a clock face was applied to the impact
bloodstain pattern, elongated bloodstains should be selected between the ‘10 and 2
hands of the clock’ (Reynolds 2008). Carter (2001) suggests that stains approaching a
glancing angle approaching 0° [12 on the clock face] should not be used for
reconstructive purposes as it becomes difficult to determine if the blood droplet was
under the influence of gravity and air resistance prior to impacting the receiving surface.
For precision and accuracy of stain measurement, consideration must be given to the
size of the stain, directionality and distance from the ROO. Raymond (1997) stated that
for manual measurement those with impact angles between 30° to 40° where the width
of the stain is > 2.0mm and a length < 8.0mm should be selected. However Reynolds
(2008) concluded when using computer assisted measurement techniques, smaller stains
and those with impact angles <20° can be selected as these bloodstains are approaching
the mathematical theory that the width of the bloodstain is equal to diameter of the
blood droplet.
Bloodstains oscillate during flight with oscillations dampening quickly during flight
(Raymond et al. 1996a). Selecting bloodstains in the outer boundary of the impact
pattern means that the corresponding blood droplets have had an increased flight time
allowing the oscillations to dampen but are being influenced by gravity and thus having
an effect on the resulting bloodstain’s shape.
43
2.3.9 The String Line Method The classic String Line Method as described by MacDonell (1993) and James et al.
(2005), is now deemed outdated by some Bloodstain Pattern Analysts (Laturnus 1998;
Raymond et al. 2001; Carter et al. 2006). The String Line Method is considered
outdated due to its tedious nature, potential analyst error involved in the inaccurate stain
measurement, calculation of impact angle and the actual stringing process when placing
strings along the flight paths (Wonder 2001; Raymond et al. 2001; Carter et al. 2006).
In Western Australia, however the String Line Method is still an applied reconstructive
technique used in the determination of the ROO of an impact spatter pattern. The result
generated from this process provides a good visual representation of the ROO for
presentation in a court of law (Bevel and Gardner 2002).
The String Line Method involves string lines attached to the leading edge of the stain
with the string line extended back along the calculated impact angle, which reflects the
flight path of the selected stains (Knock and Davison 2007). By selecting multiple
stains from various areas of the pattern, the combination of these string lines allows the
ROO to be determined in 3D space [approximate location of the victim] (Maloney et al.
2005). Any convergence of string lines to a single point should be treated as suspicious
(Bevel and Gardner 2002) because the random dispersion of blood droplets caused by
the initial impact mean that no two droplets could have originated from exactly the
same source (Raymond et al. 2001, Bevel and Gardner 2002, Reynolds 2008). The
String Line Method assumes that the flight path of a blood droplet is a straight line not
the parabolic arc that takes into account gravity and air resistance (Knock and Davison
2007). Subsequently, the resultant ROO is often artificially high with the actual
location being at or below the determined area (James et al. 2005).
2.3.10 The Trigonometric [Tangent] Method
When a series of bloodstains have a common AOC the application of the Tangent
Method is often used as an alternative to the manual String Line Method to determine
the Region of Origin (Griffin and Anderson 1993). Originally used as a reconstruction
method for straight line bullet trajectories (Griffin and Anderson 1993; Rowe 2007) the
Tangent Method uses the properties of a right angle triangle to establish the ROO. The
tangent of an angle in a right angle triangle can be defined as the ratio of the side
44
opposite an angle in the triangle to the length of the side adjacent to the angle (Equation
2.4).
Tangent can be defined as Equation 2.4
Tan theta [θ] = opposite adjacent
For reconstruction of an impact pattern a straight line is drawn though the long axis of
the stain with the common AOC then determined for a series of stains. Figure 2.17
represents the application of the right angled triangle to the Tangent Method. The
measurement from the leading edge of the selected stain to the common AOC will give
a length for the adjacent side [AC]. The bloodstain [A] is manually or computer
measured to determine the length and width. The angle of impact is determined using
the basic trigonometric equation [arcsine the measured width to length ratio of each
selected stain]. Using the tangent equation the opposite side measurement can be
obtained [BC], or the distance of a convergence point from a wall, for a vertical surface.
The ROO can be obtained by averaging the distance [BC] for a series of stains.
Figure 2.17 A basic right angled triangle and how this can be used by a Bloodstain Pattern
Analyst to determine the area of origin for a impact pattern on a wall (image by
Natasha Rogers).
Adjacent (AC = distance of stain to convergence site)
Opposite (BC = distance of impact from the wall)
Hypotenuse
B
A (bloodstain)
C
θ
(C = Two dimensional convergence site)
(θ = stain angle of impact)
45
The straight line trajectory nature of the Tangent Method means that the parabolic path
taken by a blood droplet due to the influence of gravity and air resistance is neglected.
As for the String Line Method the calculated blood source height will often be above
the actual blood source height (Bevel and Gardner 2002).
2.3.11 The Computer Assisted [BackTrack™] Method
Computer programs such as BackTrack™ are advocated by Bloodstain Pattern Analysts
because they are quick and can eliminate some of the human error experienced during
the ROO determination (Wonder 2001). BackTrack™ was originally developed by Dr
Alfred Carter of Carleton University and uses digital photographs and directional stain
analysis [ellipse fitting angle of impact] to determine the blood source by giving the
analysts three views of the crime scene [top, side and end] (Carter 2001). The first (top)
view, using virtual strings, shows the intersection of the flight paths of selected blood
droplets, the average of which gives the X and Y positions of the blood source at the
time of impact. A subsequent side view allows the analysis to estimate the height of the
blood source at the time of impact or determine the Z coordinate value (Carter et al.
2006). A validation study completed by Carter et al. (2006) found that the average
difference between the known and calculated values for the X, Y and Z coordinate
values was less than 7cm. This study showed BackTrack™ to be more accurate and less
time consuming than manual String Line Method. But, as with the String Line and
Tangent Methods, the BackTrack™ program also only gives the upper height limit of
the blood source because each of the virtual strings pass directly over the blood source
(Carter 2001). Unfortunately human error is not eliminated with the use of
BackTrack™. Inappropriate stain selection and uncertainty when fitting the ellipse for
impact angle calculations by the inexperienced analyst can still occur (Wonder 2001;
Carter et al. 2006).
2.3.12 Section Conclusions
The reconstruction of bloodshed events gives both the analyst and the criminal justice
system an insight into physical processes and activities that led to bloodstain pattern
creation. The type of crime scene encountered, analyst personal preference and access
to specific computer programs determines which technique [Tangent Method, String
Line Method, BackTrack™] will be used for reconstruction. When conducting
experimental research to determine the causal affect of a variable the work must be
46
substantially similar to that encountered in the real world. For this reason Chapters 3
and 4 not only provides a detailed examination of the effect of hematocrit value on
individual stain parameters at a variety of impact angles, but also uses the three most
common reconstruction techniques to determine experimental outcomes for
investigators to understand and apply to casework.
47
3. THE EFFECT OF HEMATOCRIT VALUE ON RESULTANT STAIN PARAMETERS
3.1 Experimental Methods 3.1.1 Introduction
Using a passive drop technique (see Section 3.1.5) 30 bloodstains were produced for
each of the 10 different hematocrit values at 4 different angles of impact (see below).
Each stain was manually measured by the author and digitally photographed. The
digitally captured bloodstain was imported into a specifically designed Microsoft®
Office Excel 2003 workbook and measured utilizing the AutoShape function of the
software to computer fit the theoretical ellipse. Using Equation 2.3, the angle of impact,
for each bloodstain, was calculated using both the manual measurement and Microsoft®
Office Excel 2003 Auto Shapes measurement techniques.
3.1.2 Collection and Handling of Blood
In order to reduce the risk of communicable diseases, such as HIV and hepatitis, the
author’s blood was used for all experiments. All blood was used within 21 days of
collection as suggested by Dailey (2001). Venous blood is preferred for most
haematological examinations and is best withdrawn from an antecubital vein by means
of a disposable plastic syringe (Dacie and Lewis 1991). Blood was drawn from the
author by a qualified medical technologist and placed in vials containing the
anticoagulant, Ethylenediamine tetra acetic acid [EDTA]. The sodium and potassium
salts of EDTA, at a concentration of 1.5mg/ml, are powerful anticoagulants and are used
for routine haematological sampling and therefore deemed adequate for this study.
Although EDTA is excess of 2.0mg/ml of blood has been known to cause a significant
decrease in hematocrit value (Dacie and Lewis 1991), the amount of EDTA in the vials
is below 2mg/ml and the hematocrit values were artificially adjusted and tested using an
automated Coulter machine prior to experimentation. MacDonell (1993) and Raymond
(1997) both suggest that EDTA appears to influence the properties of blood, but without
the addition of an anticoagulant the blood would clot and be useless for experimental
purposes. Also, regardless of the addition of the anticoagulant, if blood is allowed to
stand at room temperature it will undergo certain degenerative changes. To retard any
degenerative changes the blood was stored at 4.0°C in the refrigerator prior to
experimental use (Dacie and Lewis 1991; Raymond 1997).
48
Three different methods of measuring hematocrit value currently exist for haematology
testing, the Macro Method [Wintrobes Method], the Micro Method and the Fully
Automated Method using a Coulter machine. The Coulter machine provides an
accurate and reliable means of determining hematocrit value and is currently used by
PathWest for routine haematology work in Western Australia. The author was allowed
to use this machine and all blood drawn was haematologically tested prior to use.
Due to the varying density of the biological components of blood, the cellular
components will begin to settle to the bottom of any container during storage.
Subsequently, blood vials were agitated prior to experimental use to ensure adequate
mixing of the individual components; in order to replicate human body temperature,
prior to experimental use, the blood was placed in a warm water bath heated to
approximately 37.0°C [average human body temperature].
3.1.3 Adjusting Hematocrit Values Ten EDTA vials, each containing approximately 3ml of fresh venous blood, were drawn
from the author’s antecubital vein. One vial was placed on the automated Coulter
machine and the hematocrit value determined [control]. The remaining vials were
centrifuged for ten minutes resulting in the red blood cells settling to the bottom of each
vial. The plasma component [containing water, salts, glucose, fibrinogen, ABO
antibodies, proteins, lipids, waste products and clotting factors] was drawn off using a
Labnet Biopette micropipette and pooled into one large vial. The remaining component
containing the red blood cells [hematocrit value], hemoglobin and ABO antigens, was
drawn off using the same method and pooled in another large vial. Mixing of the buffy
coat containing white blood cells and platelets did occur, but this was unavoidable due
the small fraction of cells lying above the red blood cells becoming disturbed due to the
pipetting technique.
It was determined to achieve the appropriate replication [30 droplets] within the
experimental design, 3ml [standard vial size] of blood was required for each hematocrit
value. Approximate hematocrit values were made up by pipetting a known volume red
blood cells combined with a known volume of plasma into separate vials (Table 3.1).
Due to the viscosity [stickiness] of the red blood cells all the red blood cells were not
removed from the pipette, thus the actual hematocrit values were slightly different from
49
the predicted hematocrit values. For this reason each vial was further tested by the
Coulter machine to determine the specific hematocrit values. A printout of the
haematology data was then obtained.
Table 3.1 Volumes of plasma and red blood cells pipette into labeled vials with the
corresponding predicted hematocrit values versus the actual hematocrit values
used for experimental purposes.
Vial
Number
Pipette Plasma
Volume (ml)
Pipette Red
Blood Cells
Volume (ml)
Predicted
Hematocrit
(%)
Actual
Hematocrit
(%)
1 Normal N/A N/A 36.9
2 0.45 2.55 15 11.2
3 0.75 2.25 25 22.3
4 1.05 1.95 35 28.5
5 1.20 1.80 40 33.7
6 1.50 1.50 50 39.7
7 1.65 1.35 55 46.2
8 1.95 1.05 65 50.2
9 2.10 0.90 70 61.3
10 2.40 0.60 80 68.9
3.1.4 Angle Board
A tilting table with adjustable angle plate was sourced from a local engineering
company and used to determine different angles for the receiving surface. The tilting
table had a solid steel base with an adjustable top plate that was able to move from 0°
[perpendicular to the base plate] to 90° [parallel to the base plate]. The top plate has a
lip to which the receiving surface could be anchored. The required angle was read off
an indicator on the rear of the stand. A Kent 10T high precision [± 0.1°] adjustable set
square was used to manually calibrate the angle plate with each change of known angle.
A Stabila non magnetic spirit level was used to ensure the base plate was level prior to
experimentation and after angle adjustment. The tilting table with adjustable angle plate
was set to 4 different angles of impact 15°, 30°, 45° and 60°. These correspond with the
angles of impact often encountered by an analyst during examination of an impact
spatter pattern. Willis et al. (2001) and McGuire & Rowe (2004) mathematically
50
demonstrated that stains produced from angles of impact >60.0º are unreliable and
therefore should not be used for reconstructive purposes. For this reason impact angles
above 60.0° were not examined.
3.1.5 Experimental Setup
For each of the 10 chosen hematocrit values, 30 blood droplets were deposited onto the
angled surface [smooth, non porous ceramic tiles] at each of the four known angles.
Each droplet had a volume of 18µL and were released from a Labnet BioPette pipette
[2µL to 20µL with a manufactured accuracy of ±0.8%]. The droplets were released
from the pipette by slowly depressing the plunger, allowing the formed droplet to
overcome the physical and chemical properties of blood and fall under the influence of
gravity from the pipette tip. A new clean pipette tip was used for each blood droplet.
The ceramic tiles were cleaned with hot soapy water, rinsed and dried between each
experiment. Each blood droplet underwent a vertical fall of 200cm prior to impacting
the receiving surface. Raymond et al. (1996) found that oscillations dampen after 40cm
for passive drops and 100cm for droplets resulting from an impact. The vertical fall
height of 200cm was chosen to avoid any errors and complications associated with
oscillating blood droplets. A total of 1200 stains were deposited [10 hematocrit values,
4 angles of impacts, 30 replicate blood droplets] to form experimental samples.
3.1.6 Photographic Recording Technique
Following stain deposition each tile was horizontally positioned and allowed to air dry
prior to photographing. Photographing the resultant bloodstains was undertaken using a
Nikon D100 digital camera with a 60mm macro lens. The aperture settings ranged
between f18 to f22, ISO 200 with the speed adjusted according to the amount of
available natural light. The camera was mounted on a secure tripod at an optical axis of
90° to minimise distortion via “camera shake” and allow for timed exposure lengths.
After being photographed each bloodstain image was stored as a high resolution
electronic JPEG file. Prior to photographing a graduated 1mm scale with stain
identification number was placed adjacent to each bloodstain.
51
3.1.7 Manual Measurements of Bloodstains
Once each stain had been photographed it was manually measured by the author using a
pair of electronic callipers [device scaled to 0.1mm] and OptiVisor optical headset [10x
magnification]. The callipers were visually matched to the widest part of the outside
edge of the stain and the stain measurement is read off the electronic display. The
callipers were placed along the long axis of the stain from the tip of the leading edge
until the centre point of the visually determined bloodstain width line. This distance
was read off the electronic display and doubled to gain the total length of the stain
(Chafe 2003). Both the length and width measurements were entered onto a Microsoft®
Office Excel 2003 spreadsheet.
3.1.8 Computer Assisted Bloodstain Measurement Using Microsoft® Office Excel 2003 Auto Shapes
Each bloodstain image was imported into a Microsoft® Office Excel 2003 workbook.
The workbook was specially designed for measuring bloodstains using the Auto Shapes
function (Reynolds and Raymond 2008). The workbook contained 31 worksheets, one
for each imported image and a master sheet containing the automatically linked data for
each of the 30 individual worksheets. Each image was labelled according to its
deposition number, Stain 1 through to Stain 30 for workbook number 1, Stain 31 to 60
for workbook number 2 etc.
Using the picture toolbar each image was adjusted according to individual requirements
and typically involved cropping, resizing, adjusting the contrast and brightness. The
image aspect ratio was locked during all image adjustment. Once the required
adjustment had been achieved each image was saved to the worksheet.
Using the macro automatic functions sets of parallel grid lines and a long axis line were
appropriately positioned on the stain by the author and used to determine the widest part
of the stain and thus the leading edge. An ellipse was placed in the centre of the
positioned grid lines and symmetrically elongated to fit the leading edge. The length
and width of the stain were directly obtained from the ellipse parameters and manually
placed into the appropriate cells. The calculation of the impact angle was automatically
performed using macro functions incorporated in the worksheet. A scale bar was
inserted into the image and lined up with the graduated scale placed adjacent to the stain
52
prior to photographing. The dimensions of the original stain were then obtained and
placed into the worksheet. All this information was automatically transferred to the
master worksheet. Information from each master sheet was then subsequently
combined in a separate Microsoft® Office Excel 2003 workbook for statistical analysis.
3.1.9 Statistical Analysis
For all known impact angles, hematocrit values and stain measurement techniques, the
stain width, stain length, impact angle and difference between known and calculated
impact angle [positive or negative overestimation] was determined. For stains
measured manually statistical analyses compared the width of the stain at known impact
angle of 15º using a one-way Analysis of Variance [ANOVA] and Tukey’s testing [α =
0.05]. The dependent variable was width, the independent variable was hematocrit
value, with each known impact angle being considered separately. Comparisons of
length and calculated impact angle were also made. Bloodstains measured using
Microsoft® Office Excel 2003 Auto Shapes were compared using the same statistical
procedures.
A two-way ANOVA compared the difference between the known and calculated angle
of impact as the dependent variable for the differing hematocrit values and the manual /
Microsoft® Office Excel 2003 Auto Shapes measurement technique. Any interaction
between these independent variables was considered [α = 0.05].
For the manual measurement technique, the response to various hematocrit values for
the four impact angles [α = 0.05] was compared using a two-way ANOVA. Bloodstains
measured using Microsoft® Office Excel 2003 Auto Shapes were compared using the
same statistical procedures.
Two-way ANOVA testing compared the differences between the known and calculated
impact angles for the manual and Microsoft® Office Excel 2003 Auto Shapes
measurement technique, at the four impact angles [significance level = 0.05].
53
3.2 Results
Results obtained for manual and Microsoft® Office Excel 2003 Auto Shapes stain
measurement techniques are shown in Tables 3.2, 3.3 and 3.4. For all known impact
angles it is apparent that there is close agreement between the known and calculated
impact angles irrespective of the hematocrit value.
3.2.1 Impact Angle
At a known impact angle of 15° for both the manual and Microsoft® Office Excel 2003
Auto Shapes measurement techniques, hematocrit value significantly affected the
calculated impact angle [F = 6.85; df = 9, 290; P <0.001, F = 23.33; df = 9, 290; P
<0.001].
The control hematocrit value [36.9%] gave an average calculated impact angle of
15.32° ± 0.49° for manually measured stains and 14.67° ± 0.65° for Microsoft® Office
Excel 2003 Auto Shapes measured stains (Table 3.2 and Figure 3.1). For manually
measured stains the highest average calculated impact angle was achieved for the
control hematocrit value of 36.9% [15.32° ± 0.49°]. However, for Microsoft® Office
Excel 2003 Auto Shapes measured stains the highest average calculated impact angle
was achieved at the highest hematocrit value of 68.9% [16.31° ± 0.88°]. It is of interest
to note that although the average calculated impact angle was deemed to be significant
for both measurement techniques the actual range of averages for manually measured
stains was 1.09° [14.23° ± 0.67° to 15.32° ± 0.49°] and 2.07° for the Microsoft® Office
Excel 2003 Auto Shapes technique [14.24° ± 0.94° to 16.31° ± 0.75°].
At a known impact angle of 30°, hematocrit value had no affect on the calculated
impact angle for manually measured stains [F = 1.51; df = 9, 290; P =0.143]. The
average calculated impact angles ranged from 29.70° ± 1.39° at the lowest hematocrit
value of 11.2% to 30.82° ± 1.27° at a hematocrit value of 50.2%.
At a known impact angle of 30°, hematocrit value significantly affected the calculated
impact angle for Microsoft® Office Excel 2003 Auto Shapes measured stains [F = 16.29;
df = 9,290; P <0.001]. Calculated impact angles were similar for hematocrit values of
28.5%, 11.2%, 39.7%, 33.7%, 22.3%, 36.9% and 61.3%, but different when compared
to the similar calculated impact angles achieved at hematocrit values of 46.2%, 68.9%
54
and 50.2%. Although the calculated impact angle was significantly different the
average calculated impact angles ranged 2.25° from 29.06° ± 0.89° at a hematocrit
value of 28.5% to 31.31° ± 0.90° at a hematocrit value of 50.2%.
For both the manual and Microsoft® Office Excel 2003 Auto Shapes, at a known impact
angle of 45°, hematocrit value significantly affected the calculated impact angle [F =
87.62; df = 9, 290; P <0.001, F = 2.99; df = 9, 290; P <0.01. Although the calculated
impact angle was deemed to be significant for both measurement techniques the actual
range of averages was for manually measured stains 4.77° [40.88° ± 1.86° to 45.65° ±
2.22°] and 1.45° for the Microsoft® Office Excel 2003 Auto Shapes technique [43.76° ±
2.00° to 45.21° ± 1.54°].
For both manual and Microsoft® Office Excel 2003 Auto Shapes, at a known impact
angle of 60°, the hematocrit value significantly affected the calculated impact angle [F =
5.36; df = 9, 290; P <0.001, F = 6.90; df = 9, 290; P <0.001]. The lowest average
calculated impact angle for manually measured stains was at a hematocrit value of
28.5% [55.80° ± 3.30°]. This compared to the highest calculated impact angle at
hematocrit value of 39.7% [60.48° ± 5.19°] for manually measured stains. When
measured using Microsoft® Office Excel 2003 Auto Shapes, the lowest calculated
impact angle occurred at a hematocrit value of 39.7% [58.02° ± 2.35°] whilst the
highest occurred at a hematocrit value of 68.9% [61.44° ± 2.22°].
55
Table 3.2 Shows Manual and Microsoft® Office Excel Auto Shapes (BOLDED) mean calculated impact angle, standard deviation and minimum-maximum range
for bloodstains with different hematocrit values that have fallen on ceramic tiles offset from a vertical at known angle values (n = 30 stains).
Hematocrit Known Angle 11.2 22.3 28.5 33.7 36.9 39.7 46.2 50.2 61.3 68.9
15.0 Manual
Calc Angle 14.23 14.87 14.66 14.70 15.32 14.85 15.26 15.19 14.86 15.12 Std Dev 0.67 0.83 0.78 0.55 0.49 0.71 0.57 0.65 0.75 0.88
Calc Range 12.31-15.66 13.17-17.00 12.73-16.53 13.89-16.19 14.61-16.26 13.69-16.03 14.11-16.83 13.99-16.80 13.39-16.62 13.34-16.85
15.0 Excel
Calc Angle 14.24 14.67 14.60 14.81 14.67 14.29 15.06 15.47 16.11 16.31 Std Dev 0.94 0.63 0.67 0.67 0.65 0.66 0.99 0.99 1.06 0.75
Calc Range 12.29-16.00 13.52-16.03 13.43-15.64 13.31-16.22 13.35-15.95 12.88-15.51 13.27-17.12 13.90-17.89 13.77-18.12 14.61-17.79
30.0 Manual
Calc Angle 29.70 30.06 29.93 29.88 29.92 30.16 30.06 30.82 30.12 30.18 Std Dev 1.39 1.07 1.50 1.36 1.34 1.00 1.25 1.27 1.48 1.49
Calc Range 26.63-33.84 19.42-23.64 25.95-32.48 27.38-32.80 27.21-32.27 28.21-31.77 27.16-32.46 28.54-33.41 26.92-33.12 27.63-34.30
30.0 Excel
Calc Angle 29.08 29.56 29.06 29.55 29.72 29.37 29.89 31.31 29.76 30.59 Std Dev 1.05 0.88 0.89 0.92 1.08 0.72 0.74 0.90 1.17 0.94
Calc Range 27.00-31.54 27.98-31.28 27.51-30.98 27.98-31.76 27.56-31.64 28.27-31.15 28.69-31.32 29.73-32.99 28.02-33.43 29.05-32.33
45.0 Manual
Calc Angle 41.04 42.75 42.62 42.70 44.17 42.47 45.65 40.88 44.11 43.27 Std Dev 2.74 1.51 2.98 3.82 2.96 1.22 2.22 1.86 2.09 2.32
Calc Range 35.80-51.21 39.60-45.47 38.47-51.54 38.10-51.90 39.31-49.61 38.18-48.79 41.14-50.86 37.06-45.39 41.09-50.97 39.98-48.92
45.0 Excel
Calc Angle 43.76 45.18 45.21 44.88 44.62 44.58 44.10 45.09 44.57 45.20 Std Dev 2.00 1.57 1.54 1.40 1.44 1.25 1.57 1.85 1.48 1.41
Calc Range 40.64-47.97 41.56-47.73 42.86-48.54 42.06-48.36 42.05-46.69 41.99-46.80 41.22-47.27 40.47-49.78 42.38-48.30 43.07-49.40
60.0 Manual
Calc Angle 59.80 57.30 55.80 58.89 59.01 60.48 59.98 59.02 56.81 57.78 Std Dev 3.77 3.23 3.30 3.80 3.48 5.19 2.98 3.50 2.71 3.25
Calc Range 52.60-67.14 52.45-66.35 49.35-64.06 52.40-66.70 50.58-64.57 52.58-58.58 53.65-65.63 54.63-65.95 52.92-64.63 50.97-64.45
60.0 Excel
Calc Angle 59.53 58.28 58.07 58.96 58.35 58.02 58.59 58.40 60.23 61.44 Std Dev 2.61 2.97 2.71 1.83 2.13 2.35 1.53 1.86 2.45 2.22
Calc Range 54.03-65.25 54.52-69.04 53.63-66.25 55.70-62.82 53.96-63.15 54.84-63.98 55.34-61.12 54.88-62.45 56.22-65.35 56.55-64.90
56
Comparative Impact Angle Analysis
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
65.0
70.0
11.2 22.3 28.5 33.7 36.9 39.7 46.2 50.2 61.3 68.9
Hematocrit
Cal
cula
ted
Ang
le o
f Im
pact
15° Manual30° Manual45° Manual60° Manual15° Excel30° Excel45° Excel60° Excel
Figure 3.1 Comparative impact angle for bloodstains created at known impact angles with different hematocrit values, manually measured and measured using
Microsoft® Office Excel Auto Shapes [Error bars represent ±1 Std Dev].
57
3.2.2 Stain Width
For manual and Microsoft® Office Excel 2003 Auto Shapes measurement techniques at
15°, the hematocrit value significantly affected the width of the resultant stain [F =
156.80; df = 9, 290; P <0.001, F = 177.71; df = 9, 290; P <0.001].
The control hematocrit value [36.9%] gave average width measurements of 6.82mm ±
0.20mm and 6.70mm ± 0.24mm for manual and Microsoft® Office Excel 2003 Auto
Shapes measurement techniques, respectively (Table 3.3 and Figure 3.2). For the
manual measurement technique, average stain width decreased by 34% from 7.26mm ±
0.73mm at a hematocrit value of 11.2% to 4.76mm ± 0.44mm at a hematocrit value of
68.9%. This compared to a decrease by 34% from 7.23mm ± 0.74mm at a hematocrit
value 11.2%, to 4.77mm ± 0.43mm at a hematocrit value 68.9% for stains measured
using Microsoft® Office Excel 2003 Auto Shapes.
The widths of bloodstains produced at a known impact angle of 30°, for manual and
Microsoft® Office Excel 2003 Auto Shapes measurement techniques, were affected by
hematocrit value [F = 245.83; df = 9, 290; P <0.001, F = 240.58; df = 9, 290; P
<0.001].
Manual and Microsoft® Office Excel 2003 Auto Shapes measurement techniques
showed width decreases of 34%, for blood with hematocrit values ranging from 11.2%
to 68.9% [average width values fell from 10.50mm ± 0.87mm to 6.96 ± 0.41mm for
manually measured stains and 10.52 ± 0.89mm to 6.99 ± 0.40mm for Microsoft® Office
Excel 2003 Auto Shapes measured stains]. It is interesting to note that average widths
of bloodstains produced by blood within the hematocrit value range of 28.5% to 39.7%,
and measured by both methods, were similar [manually measured 28.5% hematocrit
value = 9.60mm ± 0.49mm; 36.9% hematocrit value = 9.42mm ± 0.56mm; 39.7%
hematocrit value = 9.38mm ± 0.28mm, Microsoft® Office Excel 2003 Auto Shapes
measured 28.5% hematocrit value= 9.64mm ± 0.49mm; 36.9% hematocrit value =
9.52mm ± 0.59mm; 39.7% hematocrit value = 9.42mm ± 0.30mm].
Hematocrit value was shown to affect the width of bloodstains produced at a known
impact angle of 45°, for both manual and Microsoft® Office Excel 2003 Auto Shapes
58
measurement techniques [F = 87.62; df = 9, 290; P <0.001, F = 80.42; df = 9, 290; P
<0.001].
The control hematocrit value [36.9%] gave average widths of 11.33mm ± 0.89mm and
11.12mm ± 0.97mm for manual and Microsoft® Office Excel 2003 Auto Shapes
measurement techniques, respectively. As the hematocrit value increased from 11.2% to
68.9% there was a significant decrease in the width of the resultant bloodstains for both
manual and Microsoft® Office Excel 2003 Auto Shapes measurement techniques. For
manually measured stains a 28% decrease in average width was observed for stains with
a hematocrit value of 11.2% compared with those stains with a hematocrit value of
68.9% [13.01mm ± 0.82mm and 9.37mm ± 0.62mm]. For stains measured using
Microsoft® Office Excel 2003 Auto Shapes a decrease in width by 28% was observed
for stains with a hematocrit value of 11.2% [12.96mm ± 0.85mm] compared with those
stains with a hematocrit value of 68.9% [9.38mm ± 0.70mm].
The width of bloodstains produced at a known impact angle of 60° were affected by
different hematocrit values for both manual and Microsoft® Office Excel 2003 Auto
Shapes measurement techniques [F = 174.78; df = 9, 290; P <0.001, F = 180.11; df =
9, 290; P <0.001].
Average width measurements of stains with a hematocrit value of 11.2% for manual and
Microsoft® Office Excel 2003 Auto Shapes measured stains were 14.59mm ± 0.90mm
and 14.74mm ± 0.85mm respectively. The widths gradually decreased until the average
minimum width was obtained at a hematocrit value of 61.3% for both manually and
Microsoft® Office Excel 2003 Auto Shapes measured stains [9.68 ± 0.41mm and
9.75mm ± 0.46mm]. Similar average width measurements were obtained for hematocrit
values of 36.9%, 39.7% and 46.2% for manually measured stains [11.97mm ± 0.78mm;
11.86mm ± 0.58mm; 11.47mm ± 0.35mm respectively]. Whilst for Microsoft® Office
Excel 2003 Auto Shapes measured stains similar average width measurements were
obtained for hematocrit values of 22.3%, 28.5% and 33.7% [13.12mm ± 0.71mm;
12.80mm ± 0.75mm; 12.66mm ± 0.75mm respectively].
59
Table 3.3 Shows Manual and Microsoft® Office Excel Auto Shapes (BOLDED) mean width (mm), standard deviation and minimum-maximum range for
bloodstains with different hematocrit values that have fallen on ceramic tiles offset from a vertical at known angle values (n = 30 stains).
Hematocrit Known Angle 11.2 22.3 28.5 33.7 36.9 39.7 46.2 50.2 61.3 68.9
15.0 Manual
Width 7.26 7.24 6.64 6.21 6.82 6.38 5.88 5.29 4.71 4.76 Std Dev 0.73 0.23 0.36 0.46 0.20 0.36 0.41 0.43 0.30 0.40 Range 5.43-8.41 6.50-7.62 5.69-7.35 4.66-6.74 6.31-7.14 5.48-6.97 5.09-6.68 4.20-5.89 4.13-5.33 3.37-5.68
15.0 Excel
Width 7.23 7.22 6.61 6.18 6.70 6.31 5.80 5.30 4.74 4.77 Std Dev 0.74 0.26 0.34 0.44 0.24 0.41 0.46 0.44 0.28 0.43 Range 5.13-8.24 6.53-7.86 5.87-7.30 4.81-6.92 6.15-7.22 5.26-7.14 4.94-6.69 4.06-5.84 4.26-5.44 3.22-5.44
30.0 Manual
Width 10.50 10.95 9.60 9.95 9.42 9.38 8.79 7.77 7.13 6.96 Std Dev 0.87 0.45 0.49 0.21 0.56 0.28 0.52 0.33 0.31 0.41 Range 8.92-11.94 9.66-11.44 8.57-10.43 9.53-10.34 7.64-10.09 8.68-9.97 7.13-9.35 6.94-8.28 6.30-7.67 6.25-7.70
30.0 Excel
Width 10.52 10.98 9.64 9.92 9.52 9.42 8.86 7.75 7.14 6.99 Std Dev 0.89 0.43 0.49 0.24 0.59 0.30 0.50 0.35 0.35 0.40 Range 9.23-11.67 9.67-11.49 8.62-10.74 9.36-10.44 7.90-10.31 8.71-9.96 7.33-9.48 6.84-8.27 6.38-7.81 6.17-7.65
45.0 Manual
Width 13.01 12.39 11.74 10.89 11.33 10.91 10.21 9.68 8.72 9.37 Std Dev 0.82 0.95 1.06 1.04 0.89 0.61 0.68 0.44 0.56 0.62 Range 11.19-13.91 9.98-13.47 9.19-12.89 9.03-13.00 9.30-12.30 9.42-11.77 8.41-11.05 8.38-10.55 7.42-9.85 8.26-11.06
45.0 Excel
Width 12.96 12.18 11.73 10.74 11.12 10.91 10.20 9.72 8.65 9.38 Std Dev 0.85 0.93 1.08 0.96 0.97 0.63 0.71 0.44 0.56 0.70 Range 11.17-13.82 9.82-13.53 9.01-12.97 8.79-12.18 8.48-12.21 9.56-11.76 8.57-11.13 8.51-10.86 7.34-9.69 8.27-10.94
60.0 Manual
Width 14.59 13.22 12.69 12.68 11.97 11.86 11.47 10.73 9.68 10.06 Std Dev 0.90 0.73 0.78 0.55 0.78 0.58 0.35 0.45 0.41 0.36 Range 11.73-15.49 12.25-14.42 10.50-13.68 11.32-13.55 10.12-13.05 9.80-12.77 10.47-11.89 9.20-11.37 8.92-10.60 9.09-10.80
60.0 Excel
Width 14.74 13.12 12.80 12.66 12.02 11.79 11.44 10.83 9.75 10.17 Std Dev 0.85 0.71 0.75 0.52 0.78 0.53 0.35 0.49 0.46 0.43 Range 11.94-15.52 11.95-14.27 10.39-13.67 11.31-13.37 10.09-13.23 9.82-12.41 10.45-11.92 9.41-11.69 9.00-10.85 8.99-10.98
60
Comparative Width Analysis
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
11.2 22.3 28.5 33.7 36.9 39.7 46.2 50.2 61.3 68.9
Hematocrit
Wid
th (m
m)
15° Manual30° Manual45° Manual60° Manual15° Excel30° Excel45° Excel60° Excel
Figure 3.2 Comparative width analysis for bloodstains created at known impact angles with different hematocrit values, manually measured and measured using
Microsoft® Office Excel Auto Shapes [Error bars represent ±1 Std Dev].
61
3.2.3 Stain Length
With a known impact angle of 15° the length of bloodstains were affected by different
hematocrit values for both manual and Microsoft® Office Excel 2003 Auto Shapes
measurement techniques [F = 177.71; df = 9, 290; P <0.001, F = 180.19; df = 9, 290; P
<0.001].
When measured manually a 38% decrease in average stain length was observed when
the blood hematocrit value was increased from 11.2% to 68.9% [29.54mm ± 2.66mm to
18.31mm ± 1.81mm]. This compared to a decrease of 42% in average stain length
observed when the blood hematocrit value was increased from 11.2% to 68.9%
[29.39mm ± 2.25mm to 17.00mm ± 1.58mm] and measured using Microsoft® Office
Excel 2003 Auto Shapes.
Hematocrit value does affect the length of bloodstains produced at a known impact
angle of 30°, for both manual and Microsoft® Office Excel 2003 Auto Shapes
measurement techniques [F = 204.36; df = 9, 290; P <0.001, F = 233.88; df = 9, 290; P
<0.001].
The control, at the hematocrit value of 36.9%, gave average lengths of 18.92mm ±
1.23mm and 19.23mm ± 1.35mm for manual and Microsoft® Office Excel 2003 Auto
Shapes measurement techniques respectively. As hematocrit value increased from
11.2% to 68.9% there was a significant decrease in the length of the resultant
bloodstains. A 35% decrease in average length was observed when bloodstains were
measured manually [hematocrit value 11.2% = 21.20mm ± 1.64mm and 68.9% =
13.87mm ± 1.00mm]. A similar decrease (37%) was observed when bloodstains were
measured using Microsoft® Office Excel 2003 Auto Shapes [hematocrit value 11.2% =
21.66mm ± 1.89mm and 68.9% = 13.73mm ± 0.73mm].
The length of bloodstains produced at a known impact angle of 45° were affected by
different hematocrit value for both manual and Microsoft® Office Excel 2003 Auto
Shapes measurement techniques [F = 9.12; df = 9, 290; P <0.001, F = 80.42; df = 9,
290; P <0.001].
62
Average length measurements at a hematocrit value of 11.2% for manual and
Microsoft® Office Excel 2003 Auto Shapes measured stains were 26.02mm ± 1.64mm
and 18.75mm ± 1.23mm respectively. The lengths gradually decreased until the
average minimum length was obtained at hematocrit value of 61.3% for manually and
Microsoft® Office Excel 2003 Auto Shapes measured stains [17.43 ± 1.12mm and
12.35mm ± 0.95mm]. Similar average length measurements were obtained for
hematocrit values of 36.9%, 39.7% and 33.7% for manually measured stains [22.66mm
± 1.78mm; 21.81mm ± 1.22mm; 21.78mm ± 2.08mm]. Whilst for Microsoft® Office
Excel 2003 Auto Shapes measured stains similar average length measurements were
obtained for 46.2%, 39.7% and 36.9% [14.68mm ± 1.12mm; 15.55mm ± 0.96mm;
15.84mm ± 1.36mm].
At a known impact angle of 60° hematocrit value does affect the length of bloodstains
measured using the both manual and Microsoft® Office Excel 2003 Auto Shapes
measurement techniques [F = 107.67; df = 9, 290; P <0.001, F = 163.54; df = 9, 290; P
<0.001].
As hematocrit value increased the length of the resultant stains decreased. For the
manual measurement technique at a hematocrit value of 11.2% the average stain length
was 16.94mm ± 1.17mm this compared to 11.93mm ± 0.58mm at a hematocrit value
68.9%. For the Microsoft® Office Excel 2003 Auto Shapes measurement technique
average stain length at a hematocrit value of 11.2% was 17.13mm ± 1.11mm this
decrease by 32% to 11.59mm ± 0.61mm at a hematocrit value 68.9%.
63
Table 3.4 Shows Manual and Microsoft® Office Excel Auto Shapes (BOLDED) mean length (mm), standard deviation and minimum-maximum range for
bloodstains with different hematocrit values that have fallen on ceramic tiles offset from a vertical at known angle values (n = 30 stains).
Hematocrit Known Angle 11.2 22.3 28.5 33.7 36.9 39.7 46.2 50.2 61.3 68.9
15.0 Manual
Length 29.54 28.24 26.27 24.50 25.84 24.89 22.37 20.19 18.39 18.31 Std Dev 2.66 1.07 1.40 1.92 1.01 1.11 1.84 1.49 1.05 1.81
Calc Range 22.78-33.82 25.28-30.70 23.48-28.20 18.38-27.08 23.66-27.70 21.98-27.22 18.98-25.52 15.40-21.90 16.62-20.34 12.82-21.46
15.0 Excel
Length 29.39 28.55 26.25 24.22 26.49 25.61 22.40 19.97 17.16 17.00 Std Dev 2.25 1.22 1.58 1.94 1.31 1.94 2.28 2.13 1.52 1.58
Calc Range 23.70-33.30 25.92-31.96 22.78-28.69 19.58-27.43 24.30-30.50 20.11-28.91 19.08-26.47 13.22-23.32 14.65-19.77 11.57-20.15
30.0 Manual
Length 21.20 21.88 19.27 20.00 18.92 18.68 17.57 15.20 14.25 13.87 Std Dev 1.64 0.97 1.12 0.80 1.23 0.63 1.21 0.93 0.94 1.00
Calc Range 18.52-24.00 19.42-23.64 17.14-21.80 18.22-21.22 15.46-20.52 17.08-19.88 14.52-19.28 12.78-17.10 12.52-16.12 11.92-16.02
30.0 Excel
Length 21.66 22.28 19.86 20.14 19.23 19.21 17.78 14.91 14.41 13.73 Std Dev 1.89 1.11 1.10 0.73 1.35 0.59 1.08 0.74 0.89 0.73
Calc Range 17.89-24.43 19.50-24.09 17.74-21.67 18.04-21.20 15.79-21.26 17.63-20.09 14.65-19.43 13.34-16.61 11.92-15.72 12.50-15.46
45.0 Manual
Length 26.02 24.78 23.49 21.78 22.66 21.81 20.41 19.36 17.43 18.75 Std Dev 1.64 1.91 2.12 2.08 1.78 1.22 1.36 0.87 1.12 1.23
Calc Range 22.38-27.82 19.96-26.94 18.38-25.78 18.06-26.00 18.60-24.60 18.84-23.54 16.82-22.10 16.76-21.10 14.84-19.70 16.52-22.12
45.0 Excel
Length 18.76 17.18 16.54 15.23 15.84 15.55 14.68 13.75 12.35 13.23 Std Dev 1.24 1.15 1.53 1.44 1.36 0.96 1.12 0.76 0.95 1.02
Calc Range 16.44-20.79 14.81-19.36 12.45-18.69 12.44-17.53 12.41-17.69 13.63-17.19 12.35-16.63 11.72-15.66 9.97-14.27 11.49-15.59
60.0 Manual
Length 16.94 15.76 15.39 14.86 14.01 13.73 13.28 12.55 11.59 11.93 Std Dev 1.17 1.24 1.09 0.78 1.06 1.06 0.61 0.66 0.61 0.58
Calc Range 13.52-18.68 13.46-17.78 13.14-16.78 13.34-16.10 11.86-15.90 11.10-16.08 12.14-14.54 11.20-13.50 10.58-12.82 10.72-12.94
60.0 Excel
Length 17.13 15.46 15.11 14.79 14.13 13.93 13.41 12.72 11.24 11.59 Std Dev 1.11 1.02 0.92 0.58 0.88 0.75 0.42 0.51 0.59 0.61
Calc Range 13.60-18.45 12.79-17.17 12.90-16.52 13.48-15.70 12.28-15.77 11.29-15.00 12.26-14.06 11.50-13.49 10.22-12.55 10.19-12.41
64
Comparative Length Analysis
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
11.20 22.30 28.50 33.70 36.90 39.70 46.20 50.20 61.30 68.90
Hematocrit
Len
gth
(mm
)
15° Manual30° Manual45° Manual60° Manual15° Excel30° Excel45° Excel60° Excel
Figure 3.3 Comparative length analysis for bloodstains created at known impact angles with different hematocrit values, manually measured and measured using
Microsoft® Office Excel Auto Shapes [Error bars represent ±1 Std Dev].
65
3.2.4 Manual versus Microsoft® Office Excel 2003 Auto Shapes measurement techniques / hematocrit value comparison
The difference between the known and calculated angle of impact for manual and
Microsoft® Office Excel 2003 Auto Shapes stain measurement techniques were affected
by differing blood hematocrit values [F = 33.59; df = 1,2380; P <0.001; F = 7.45; df =
9,2380; P <0.001].
The difference between known and calculated impact angle demonstrated an interaction
between stain measurement technique and hematocrit value [F = 7.87; df = 9, 2380; P
<0.001]. It is of interest to note the greatest difference achieved between the known
and calculated angle of impact was for manually measured stains at 28.5% hematocrit [-
1.75° ± 2.89°] this compared to the least amount of difference that occurred at 50.2%
hematocrit for Microsoft® Office Excel 2003 Auto Shapes stains [0.07° ± 1.80°].
3.2.5 Manual versus Microsoft® Office Excel 2003 Auto Shapes measurement techniques / impact angle comparison
The difference between the known and calculated impact angle was significantly higher
at known angles of 60° and 45° when compared to known angles 15° and 30° [F =
59.16; df = 3, 2392; P <0.001]. At 15° the average difference between the known and
calculated impact angle was -0.03° ± 0.92° this compared to -1.26° ± 3.23° at a known
impact angle of 60°.
For the comparative analysis of stain measurement there is a difference between manual
and Microsoft® Office Excel 2003 Auto Shapes stain measurement techniques [F =
35.31; df = 1, 2392; P <0.001]. The difference between the known and calculated
angle of impact was -0.89° ± 2.67° and -0.37° ± 1.79° for manual and Microsoft®
Office Excel 2003 Auto Shapes respectively. Stain measurement using Microsoft®
Office Excel 2003 Auto Shapes was significantly enhanced from the manual at known
impact angles of both 45° [-2.03° ± 2.91° to -0.28° ± 1.61°]; and 60° [-1.51° ± 3.81° to
-1.01° ± 2.50°].
66
3.2.6 Manual measurement technique – impact angle and hematocrit value comparison
The difference between the known and calculated angle of impact for the manual
measurement technique were affected by differing blood hematocrit value and known
impact angle [F = 6.35; df = 9, 1160; P <0.001; F = 60.03; df = 3, 1160; P <0.001].
For manual measurement the difference between the known and calculated impact angle
was similar at known angles of 60° and 45° [-1.51° ± 3.81° and -2.03° ± 2.91°
respectively], but significantly different when compared to known angles 30° and 15°
[0.08° ± 1.33° and -0.09° ± 0.76° respectively]. The greatest difference between known
and calculated impact angle occurred at hematocrit value 28.5% [-1.75° ± 2.89°] with
the least difference occurring at hematocrit value 46.2% [0.24° ± 1.97°].
The difference between known and calculated impact angle demonstrated an interaction
effect between hematocrit value and the known impact angle [F = 6.22; df = 27, 1160;
P <0.001]. At a known impact angle of 45° and 50.2% hematocrit the difference
between the known and calculated angle of impact was -4.12° ± 1.86° this compared to
0.82° ± 1.27° at a known impact angle of 30° and 50.2% hematocrit. Using the manual
measurement technique 90% of the differences were negative [underestimation of
impact angle] and 10% of differences were positive [overestimation of impact angle].
3.2.7 Microsoft® Office Excel 2003 Auto Shapes measurement technique – impact angle and hematocrit value comparison
For Microsoft® Office Excel 2003 Auto Shapes measured stains the overall difference
between the known and calculated impact angle were affected by both hematocrit value
and known impact angle [F = 25.75; df = 3, 1160; P <0.001; F = 16.72; df = 9, 1160; P
<0.001]. The difference was significantly higher at a known angle of 60° [-1.01° ±
2.50°] and when compared to known angles 15°, 30° and 45° [0.02° ± 1.06°, -0.21° ±
1.13° and -0.28° ± 1.61° respectively].
The difference between the known and calculated impact angle showed an interaction
effect between hematocrit value and the known impact angle [F = 5.02; df = 27, 1160;
P <0.001]. The greatest difference between the known and calculated impact angle
occurred at the highest hematocrit value of 68.9% at 60° [1.44° ± 2.22°]. Using the
67
Microsoft® Office Excel 2003 Auto Shapes measurement technique 70% of differences
were negative [underestimation of impact angle].
3.2.8 General observations
Figure 3.4 shows a series of bloodstains produced using three different hematocrit
values [11.2%, 36.9% and 68.9%] at four known impact angles [15°, 30°, 45° and 60°].
It was observed that bloodstains produced at a lower hematocrit value have a translucent
mottled light red colour, whilst those with higher hematocrit values have more a
uniformed dark red colour [related to the increase in red blood cells].
68
Figure 3.4 Impact angle, hematocrit value and stain shape.
15 Degree 30 Degree 45 Degree 60 Degree
15 Degree 30 Degree 45 Degree 60 Degree
11.2% Hematocrit 36.9% Hematocrit 68.9% Hematocrit
69
3.3 Discussion In actual casework the hematocrit value, and therefore blood viscosity, is unknown. No
testing procedure is currently available that allows for the determination of hematocrit
value or viscosity from a bloodstain. Although the data presented shows that the
hematocrit value appears to significantly effect the calculated impact angle statistically,
all the calculated impact angles were within limits of 5° to 7° as described by Bevel and
Gardner (2002) [Difference range –4.20° to 0.82° for manually measured stains; –1.98
to 1.44 for Microsoft® Office Excel 2003 Auto Shapes measured stains].
For BPA two components of blood are important; plasma and hematocrit value
(Wonder 2001), with the latter having a direct influence on blood viscosity (Raymond
1997; Johnson 1999; Bevel and Gardner 2002; Paut and Bissonette 2002). As
hematocrit values decrease the plasma component of blood increases. This has a
dilution effect, resulting in disproportional decrease in blood viscosity. The viscosity of
blood is important for both blood droplets during flight and upon impact with a
receiving surface. During flight, viscosity helps to dampen oscillations, thus enabling
the droplet to establish a position of equilibrium after the distortion at the propagation
act. Upon impact with a receiving surface, viscosity affects the resultant bloodstain
shape (Raymond 1997). A blood droplet upon impact with a receiving surface
experiences two opposing forces as it makes contact with, and spreads across, the
surface. These forces include those that promote spreading; inertia [density, impact
velocity and volume] and those that resist spread and promoting fluid equilibrium
[surface tension and viscosity] (Hulse-Smith and Illes 2007).
Laber (1985) suggested that the height of origin for a blood droplet could not be
determined from stain diameter if the volume of the droplet was unknown. Laber
(1985) stated that a change in stain diameter is considered a function of a change in
droplet volume and/or impact velocity. This study (Tables 3.3, 3.4 and Figures 3.2, 3.3)
indicated that bloodstain length and width is also a function of blood hematocrit value.
These results indicate that the height of origin for a blood droplet can not be determined
unless droplet volume, impact velocity and the hematocrit value of the resulting
bloodstain is known. Klabunde (2005) determined there is an increase in blood
viscosity with increase in hematocrit value. An increase in viscosity provides a
resistance to spread as the blood droplet contacts and disperses across the impacting
70
surface. It is suggested that the forces that resist spreading increase with an increase in
hematocrit value whilst those that promote spreading potentially remain unchanged.
This study supports Laber (1985) and Willis et al. (2001) who stated that there are
inherent dangers when trying to estimate the distance of fall from the size parameters of
the blood droplet.
The author’s normal blood hematocrit value was 36.9%. For bloodstains created using
an increase from the author’s normal hematocrit value, 50% of experimentally
calculated impact angles were greater than the known impact angle. This is in contrast
to previous studies that have reported an overestimation of ellipse length, leading to an
underestimation of impact angle for manually measuring stains (Laturnus 1994;
Raymond 1997). For bloodstains created using hematocrit values equal to or less than
the authors normal value, only 10% of experimentally calculated impact angles were
greater than the known impact angle. Reynolds (2008) found an overestimation of
impact angle occurred for small bloodstains [≤ 3.0mm long] caused by droplets
impacting a surface obliquely at a known impact angle of 15°. In this study the
overestimation of calculated impact angle was not related to the size of the bloodstains
[all stains ≤9.97mm long], the known impact angle [15° to 60° range], or the stain
measurement technique [manual and Microsoft® Office Excel 2003 Auto Shapes].
As the hematocrit value decreases, the physical appearance of the stain alters. Those
bloodstains produced at a hematocrit value of 11.2% have a translucent mottled
appearance. The visual appearance of the stains at hematocrit value of <36.9% made it
difficult for the analyst to accurately judge the length of the stain or fit the computer
ellipse. The migration of the cells to the terminal edge of the bloodstain may lead to the
overestimation of ellipse length and thus an underestimation of impact angle. No
studies prior to this have reported a significant difference in the experimental and
theoretical expectations based on the visual appearance of the bloodstain. Further
investigation would need to be undertaken using blind trials and multiple bloodstain
analysts.
The United States Supreme Court ruling of Daubert (1993) and Kumho Tyre Company
(1999) have suggested that expert scientific evidence should only be accepted by the
court if a number of criteria can be fulfilled, some of these include; if the hypotheses
within the method can be tested, the method is scientifically reliable with reproducible
71
results and the potential and known error rates are established. Unfortunately, any type
of measurement involves error. Previous studies have indicated that the manual
measurement of bloodstains is relatively inaccurate (Laturnus 1994; Bevel and Gardner
2002). Willis et al. (2001) presented a series of mathematical equations to determine
the error associated with distance of fall and impact angle of bloodstains. They
determined that an impact angle ≤60° can be accurately determined but for anything
>60° the variance increases rapidly as 90° is approached. Raymond (1997) stated that
inaccurate results are inevitable when the impact angle is such that small changes in the
stain length/width ratio make significance difference to the calculated impact angle.
This study showed that bloodstains with known impact angles between 45° and 60°
were measured to within ± 4.20° using both manual and Microsoft® Office Excel 2003
Auto Shapes measurement techniques. This compared to ± 1.31° for bloodstains
measured with known impact angles of 15° and 30°. The results presented for the
passive drop experimentation show that accuracy of the calculated impact angles are all
within the acceptable limits [5° to 7°] as proposed by Bevel and Gardner (2002). The
levels of standard deviation calculated from the data supported the findings by Willis et
al. (2001) with the standard deviations increasing as the known impact angle increased
to 60°. Thus this study demonstrates that measurement precision improved as the
impact angle became more acute.
In the context of an investigation, if the above results are applied to a bloodstain of
angle 15.0°, 100cm from source, the error splay becomes approximately ±2.47cm [Tan
13.69°*100 = 24.36cm, Tan 16.31°*100 = 29.26cm]. For a bloodstain at 60°, 100cm
from the source, the error splay becomes ±33.66cm [Tan 55.80°*100 = 147.14cm, Tan
64.20°*100 = 206.86cm].
Bloodstain measurements must be accurate and reliable. Raymond (1997) concluded
that repeat stain measurement should be carried out for 30° impact angles to improve
accuracy. This study showed that the accuracy and precision of the calculated impact
angle is dependent on the droplet-surface impact angle, with the standard deviation
increasing as the impact angle increased. Subsequently, the data obtained supports the
proposition that to obtain the required accuracy for bloodstain measurement purposes
the impact angle should not be determined by single measurement value. Bloodstain
72
Pattern Analysts should make repeated measurements on the same stain using the same
measurement procedures.
Previous studies have indicated that the width of the stain can be measured accurately,
but the manual measurement of the length is relatively inaccurate (Balthazard et al.
1939; Laturnus 1994; Raymond 1997; Janes 2001; Willis et al. 2001). It has been
recognised that it is common for an analyst to overestimate ellipse length causing an
underestimation of the calculated impact angle (Laturnus 1994; Raymond 1997). This
study showed that for all width measurements standard deviation values were < 1.08,
whilst the standard deviation for length measurements ranged from 0.42 to 2.66. This
supports previous research conducted by Laturnus (1994) and Raymond (1997) who
demonstrated a greater standard deviation for length, as opposed to width,
measurements. However, Laturnus (1994) and Raymond (1997) stated that a major
factor in the analyst’s ability to calculate an accurate angle of impact is the elliptical
shape of the stain, not their ability to judge the stain length. Raymond (1997) suggested
that to accurately determine the impact angle, elliptical stains <30° should be chosen.
To overcome the inaccuracies of manual measurement, the use of digital photography
with computer based ‘ellipse fitting’ measurement methods have been introduced, with
BackTrack™ Images becoming the accepted industry standard (Reynolds 2008). The
use of computer based methods to measure bloodstains to improve accuracy was
validated by Carter (2001). However, the computer program used, BackTrack™
Images, is not readily available to all analysts. In response to the availability issue,
Reynolds (2008) validated the utility of Microsoft® Office Excel 2003 Auto Shapes to
measure bloodstains. Reynolds and Raymond (2008) concluded that Microsoft® Office
Excel 2003 Auto Shapes is an accurate and precise measurement tool for bloodstains,
with this newly developed technique having the potential to replace traditional manual
measurement methods. Although this method doesn’t completely overcome the
subjective overestimation of ellipse length, relative to width, the ellipse visualisation
and symmetrical elongation of the computer generated ellipse minimises
overestimation. The results provide support for the use of Microsoft® Office Excel
2003 Auto Shapes as a replacement for manual measurement, producing calculated
impact angles closer to the known impact angle than the manual measurement
technique. For bloodstains measured using Microsoft® Office Excel 2003 Auto Shapes
the average difference between the known and calculated impact angle was -0.37°.
73
Manually measured bloodstains yielded a difference between the known and calculated
impact angle of -0.89°. These results suggest that the overestimation of ellipse length,
which is commonly seen when using the manual measurement technique, has been
significantly reduced by the use of Microsoft® Office Excel 2003 Auto Shapes.
The results of impact angle calculations at known impact angles of 45° and 60° are of
interest. The results show that for 45° and 60° using Microsoft® Office Excel 2003
Auto Shapes significantly reduces the difference between the known and experimentally
calculated impact angle when compared to the manual measurement technique. Stain
measurement using Microsoft® Office Excel 2003 Auto Shapes was significantly
enhanced from the manual measurement at known impact angles of both 45° [-2.03° to
-0.28°] and 60° [-1.51° to -1.01°]. Subsequently, from a theoretical perspective, a
bloodstain analyst may be able to use stains created at angles >30° to determine a blood
source Region of Origin if the stains were measured using Microsoft® Office Excel
2003 Auto Shapes. Further study would be needed to investigate and validate this
theory.
3.4 Conclusions Blood hematocrit value has been shown to affect the length and width of bloodstains.
Whilst the results from this research show that the affect of hematocrit value on impact
angle calculations is statistically significant. The significance between figures appears
to be due to the mathematical resolving power of the applied statistical test rather than a
divergence from the calculation model used to establish angles of blood droplet impact.
Examination of the known impact angle and experimentally calculated impact angle
from this study demonstrates that the error associated with impact angle calculations for
bloodstains created using a blood hematocrit range of 11.2% to 68.9% falls well within
variation stated in the literature (Laturnus 1994; Bevel and Gardner 2002; James et al.
2005).
74
The present study clearly demonstrates the use of Microsoft® Office Excel 2003 Auto
Shapes to be a reliable method to measure bloodstains. The use of Microsoft® Office
Excel 2003 Auto Shapes allows traceability of electronic data, with the ability of other
analysts or the courts to review and reproduce results. Although the measurement
accuracy was shown to be dependent on surface impact angle, measurements obtained
from impact angles 45° to 60° were still within the industry derived acceptable
variation. The use of Microsoft® Office Excel 2003 Auto Shapes may allow impact
angles of increasing obtuseness to be considered for reconstructive purposes should
limited stains be available for selection.
75
4. THE EFFECT OF HEMATOCRIT VALUES ON IMPACT SPATTER PATTERNS – IMPLICATIONS FOR RECONSTRUCTION
4.1 Experimental Methods
4.1.1 Introduction
This thesis is a two part study examining the effect of blood hematocrit value on
resultant bloodstains and bloodstain patterns. Chapter 3 examined the impact angle
calculations associated with bloodstains created by blood of differing hematocrit values
falling vertically onto inclined surfaces at known impact angles. Chapter 4 examines
the effect of differing blood hematocrit values on generated impact spatter bloodstains
and their relationship on the ability to determine a 2D AOC and 3D ROO for the blood
source.
4.1.2 Collection and Handling of Blood
Approximately 180ml of venous blood was drawn from the author’s antecubital vein by
a qualified medical technologist and placed into 10ml vials containing the anticoagulant
EDTA. After adjusting the hematocrit values [Section 4.1.3] the blood was refrigerated
at 4°C prior to experimentation. The blood was placed in a water, bath heated to 37°C
[body temperature], and inverted to mix the plasma and cellular components prior to
experimentation. All blood was used within 21 days of collection to prevent
degenerative changes (Dailey 2001).
4.1.3 Adjusting Hematocrit Values
The hematocrit value was determined using automated Coulter machine for three 10ml
vials of collected blood. These three vials were used as the control for experimental
purposes. The remaining vials containing the author’s blood were centrifuged for 10
minutes to separate the cellular and plasma components. The cellular and plasma
components were drawn off and separated using a Labnet Biopette micropipette. The
approximate hematocrit values were made up by pipetting a known volume of red blood
cells combined with a known volume of plasma into one large container. Each
container, containing a different hematocrit value, was remixed and tested using the
76
Coulter machine to determine the actual hematocrit value. Once the actual hematocrit
value was determined 10ml was pipetted into three clean vials and labelled with the
actual hematocrit value obtained and the replicate number [1 to 3] (Table 4.1). A
printout of the haematology data was obtained for each actual hematocrit value.
Table 4.1 Predicted hematocrit value versus the actual hematocrit value used for
experimental purposes for plasma and red blood cells.
Container
Number
Number of
replicates
Pipette
Plasma
Volume
(ml)
Pipette Red
Blood Cells
Volume
(ml)
Predicted
Hematocrit
(%)
Actual
Hematocrit
(%)
1 3 25.5 4.5 15 16.7
2 3 21 9 30 30.5
3 (STD) 3 N/A N/A N/A 39.8
4 3 12 18 60 52.9
5 3 7.5 22.5 75 64.8
4.1.4 Experimental Setup
All experiments were carried out at the Western Australia Police Forensic Division in a
specifically designed room for bloodstain pattern trials [width 360cm, length 560cm and
height 267cm]. The room consists of four walls, three that are made of plaster board
and painted with high gloss paint, and one that has glass panels attached to the solid
wall. Only the three painted walls were used for this experiment to limit any error and
uncertainty with different receiving surfaces. For each of the five hematocrit values,
three impact spatter patterns were created.
The bloodstain patterns were created by striking a blood pool of approximately 10ml
volume, which was placed on a clean flat surface, with the blunt end of a claw hammer.
For each pattern the blood source was located at known X, Y, and Z positions of 40cm
from the spatter bearing wall, 180cm from the intersection of the spatter bearing wall
and the right edge and 100cm above the floor. The X, Y, and Z positions were known
to the author prior to reconstruction.
77
From each impact spatter pattern, bloodstains resulting from fast upward moving blood
droplets were selected for measurement and reconstructive purposes. The number of
stains selected was directly dependent on the quality of the stains produced, with the
number of stains selected ranging from seven to 12 for each pattern. Attempts were
made to choose half the stains located to the left of the blood source and half to the right
but this was not always possible due to variations in bloodstains quality.
4.1.5 Stain Measurement
The width and length of each bloodstain was manually measured by the author using a
pair of electronic callipers and OptiVisor optical headset; see Figure 4.1 (refer Section
2.3.7). The angle of impact was then calculated using the relationship between the
width and length of the bloodstain and the angle it strikes a surface (see Equation 2.3).
Each stain was individually photographed using a Nikon D100 digital camera with a
60mm macro lens. The aperture settings were adjusted according to the amount of
available light but the ISO speed remained constant at 200. Due to the location of the
stains and the reflective nature of the receiving surface, the camera was hand held and
the images taken with no flash. The stains were photographed at close range, along
with a graduated 1mm scale, bloodstain identification number, and a vertical plumb line
[for gamma angle measurement]. The bloodstain images were then downloaded and
stored as a high resolution electronic JPEG file.
78
Figure 4.1 Author manually measuring the width and length of a bloodstain using electronic
calipers and OptiVisor headset (image by Brett McCance).
The image produced for each stain was measured using Microsoft® Office Excel 2003
Auto Shapes (refer Section 2.3.7). For each bloodstain pattern a workbook was
compiled containing each individual stain image in a worksheet and master worksheet
for all pattern data. The calculated angle of impact was determined and subsequently
applied to both the Tangent Method and String Line Method.
4.1.6 The Trigonometric [Tangent] Method
For each bloodstain selected; a straight line, using a metal ruler, was drawn through the
major axis of the stain opposite the direction of travel. The area at which all the lines
for a particular pattern intersected on the wall was determined to be the AOC. Using a
hand held Leica Distometer [manufacture accuracy ± 1.5mm] the 2D AOC and the Y
and Z coordinates values for the pattern were determined. The X coordinate value for
each stain was then calculated (refer Figure 2.17). The combined average of each
79
bloodstains X coordinate value provided the overall X coordinate value for the pattern
(Table 4.2).
4.1.7 The String Line Method
For each bloodstain measured using Microsoft® Office Excel 2003 Auto Shapes, a laser
protractor was adjusted to the calculated angle of impact and placed along the major
axis of the bloodstain [pointing towards the AOC]. A red laser beam was emitted by the
protractor onto the floor where the string was to be attached to give the straight line
trajectory of the blood droplet. A string line was then attached from the leading edge of
the stain to each marked position on the floor. The ROO [X, Y and Z coordinate
values] was visually determined by the author from the area where the multiple string
lines intersected (Figure 4.2).
Figure 4.2 Stringing of a impact spatter pattern showing the estimated Region of Origin in
yellow (image by Natasha Rogers).
80
4.1.8 The BackTrack™ Method
The same digital photographs used for both the Tangent Method and String Line
Method were imported into BackTrack™ Images (Figure 4.3). The program was then
used to calculate the glancing [γ] angle and the impact [ά] angle for each stain. This
data was then transferred to BackTrack™ Win to determine the position of the virtual
strings and subsequently the 3D ROO for the blood source.
Figure 4.3 An example of a individual stain, with scale, plumb line and major axis line
photographed for use in both BackTrack™ Images and Microsoft® Office Excel
2003 Auto Shapes (image by Natasha Rogers).
81
4.1.9 Statistical Analysis
For all generated data, the X, Y and Z coordinate values were calculated. The known X,
Y and Z coordinate values were then deducted from the calculated X, Y and Z
coordinate values in order to derive the direction of difference [positive or negative].
The statistical analysis of the data compared the difference between the known and
calculated coordinate values using a two-way Analysis of Variance [ANOVA] and
Tukey’s test [α = 0.05]. The dependent variable was the difference in coordinate value
[positive or negative], with the blood hematocrit value and reconstructive method
[Tangent, String Line and BackTrack™ Images] the two independent variables. The X,
Y and Z coordinates values were considered using separate two-way ANOVA’s. Any
interaction between the two variables was considered [α = 0.05].
A two-way ANOVA and Tukey’s test [α = 0.05] was used to compare the difference
between the known and calculated X coordinate value for the three different stain
measurement techniques [Microsoft® Office Excel 2003 Auto Shapes, Manual and
BackTrack™ Images] applied to the Tangent Method and blood hematocrit value. The
dependent variable was the difference in X coordinate value, and blood hematocrit
value and stain measurement technique [Microsoft® Office Excel 2003 Auto Shapes,
Manual and BackTrack™ Images] the independent variables. Any interaction between
the two variables was considered [α = 0.05].
For impact spatter patterns, statistical analysis compared the difference for all
coordinates values [X, Y and Z] using a one-way ANOVA and Tukey’s test [α = 0.05].
The dependent variable was the difference between the known coordinate value and
calculated coordinate value and the X, Y or Z coordinate value as the independent
variable. A one-way ANOVA and Tukey’s test [α = 0.05] was used to statistically
analyse the difference for all coordinates values [X, Y and Z] [dependent variable] and
the reconstructive method [Tangent, String Line and BackTrack™ Images]
[independent variable]. The final statistical analysis compared the difference for all
coordinates values [X, Y and Z] and blood hematocrit value using a one-way ANOVA.
The dependent variable was the difference between coordinate values with the blood
hematocrit value as the independent variable.
82
4.2 Results
The ROO results obtained for the 15 impact spatter patterns are shown in Tables 4.2 and
4.3. From results shown in Table 4.2 and illustrated in Figures 4.4 - 4.7 it is
immediately apparent that irrespective of reconstructive method applied, blood
hematocrit value has no significant influence on the ability of a Bloodstain Pattern
Analyst to reliably estimate the X, Y and Z blood source ROO.
4.2.1 Comparison of the X Coordinate
No significant difference was noted between the known and calculated X coordinate
values at the different blood hematocrit values [F = 1.55; df = 4, 30; P = 0.213]. The
average difference for the X coordinate values at the lowest hematocrit value of 16.7%
was X = 4.22cm ± 2.86cm [3 pattern average], this was comparative to a difference of X
= 3.56cm ± 2.92cm for the impact spatter patterns created with the highest hematocrit
value of 64.8%. Even though lowest average difference for the X coordinate value was
obtained for patterns created with a 30.5% hematocrit value: X = 1.11cm ± 1.96cm this
was still similar to patterns created with all other hematocrit values. The interaction
between the three different reconstructive techniques and hematocrit value at the X
coordinate value was not significant [F = 0.47; df = 8, 30; P = 0.866].
The ability to determine the X coordinate value was not significantly affected by the
three reconstructive methods [Microsoft® Office Excel 2003 Auto Shapes combined
with Tangent, Microsoft® Office Excel 2003 Auto Shapes combined with the String
Line and BackTrack™ Images] [F = 2.28; df = 2, 30; P = 0.120]. Using the Tangent
Method, the calculated average difference for the X coordinate value was 2.40cm ±
2.59cm. This was similar to the String Line Method average difference of 4.13cm ±
3.11cm and the BackTrack™ Images Method average difference of 2.07cm ± 2.60cm
(Figure 4.4).
83
Table 4.2 Shows Microsoft® Office Excel Auto Shapes, Manual (BRACKETS) and
BackTrack™ (BOLDED) measurement data for bloodstains to determine Region
of Origin using Tangent, String Line and BackTrack™ Methods for Impact
Spatter Patterns 1 to 15.
Pattern Number
(Hematocrit)
Stains
(n)
Coordinate Known
Value (cm)
Tangent Method String Line
Method BackTrack
Method
Cal (cm)
Diff (cm)
Cal (cm)
Diff (cm)
Cal (cm)
Diff (cm)
Pattern 1 (16.7%) 10
X 40 ±2 41 (44) 38 +1 (+4) -2 41 +1 40 0 Y 180 ±2 183 +3 185 +5 169 -11 Z 100 110 +10 105 +5 104 +4
Pattern 2 (16.7%) 7
X 40 ±2 47 (55) 45 +7 (+15) +5 47 +7 46 +6 Y 180 ±2 176 -4 182 +2 179 -1 Z 100 110 +10 108 +8 108 +8
Pattern 3 (16.7%) 9
X 40 ±2 45 (56) 45 +5 (+16) +5
47 +7 44 +4 Y 180 ±2 180 0 183 +3 179 -1 Z 100 110 +10 108 +8 113 +13
Pattern 4 (30.5%) 10
X 40 ±2 42 (42) 41 +2 (+2) +1 45 +5 39 -1 Y 180 ±2 182 +2 190 +10 176 -4 Z 100 106 +6 100 0 114 +14
Pattern 5 (30.5%) 10
X 40 ±2 39 (40) 39 -1 (0) -1 40 0 40 0 Y 180 ±2 174 -6 185 +5 175 -5 Z 100 117 +17 105 +5 112 +12
Pattern 6 (30.5%) 10
X 40 ±2 41 (43) 40 +1 (+3) 0 43 +3 41 +1 Y 180 ±2 182 +2 185 +5 179 -1 Z 100 109 +9 103 +3 108 +8
Pattern 7 (39.8%) 12
X 40 ±2 39 (42) 37 -1 (+2) -3 40 0 37 -3 Y 180 ±2 195 +15 190 +10 178 -2 Z 100 113 +13 115 +15 114 +14
Pattern 8 (39.8%) 12
X 40 ±2 45 (46) 44 +5 (+6) +4 50 +10 42 +2 Y 180 ±2 180 0 190 +10 180 0 Z 100 118 +18 120 +20 121 +21
Pattern 9 (39.8%) 12
X 40 ±2 42 (43) 40 +2 (+3) 0 45 +5 42 +2 Y 180 ±2 180 0 180 0 184 +4 Z 100 121 +21 110 +10 115 +15
Pattern 10 (52.9%) 10
X 40 ±2 44 (47) 43 +4 (+7) +3 45 +5 41 +1 Y 180 ±2 182 +2 183 +3 174 -6 Z 100 102 +2 105 +5 108 +8
Pattern 11 (52.9%) 10
X 40 ±2 45 (47) 43 +5 (+7) +3 44 +4 44 +4 Y 180 ±2 176 -4 182 +2 177 -3 Z 100 110 +10 111 +11 107 +7
Pattern 12 (52.9%) 10
X 40 ±2 41 (43) 41 +1 (+3) +1 40 0 43 +3 Y 180 ±2 179 -1 180 0 179 -1 Z 100 113 +13 100 0 105 +5
Pattern 13 (64.8%) 10
X 40 ±2 39 (37) 39 -1 (-3) -1 45 +5 41 +1 Y 180 ±2 183 +3 192 +12 178 -2 Z 100 115 +15 103 +3 107 +7
Pattern 14 (64.8%) 10
X 40 ±2 41 (42) 42 +1 (+2) +2 42 +2 45 +5 Y 180 ±2 178 -2 190 +10 178 -2 Z 100 113 +13 112 +12 105 +5
Pattern 15 (64.8%) 10
X 40 ±2 45 (41) 46 +5 (+1) +6 48 +8 46 +6 Y 180 ±2 182 +2 190 +10 178 -2 Z 100 117 +17 112 +12 114 +14
84
Impact Patterns: Difference Between the Known and Calculated X Coordinate Using the Tangent, String Line and BackTrack Methods
-4
-2
0
2
4
6
8
10
12
1(16.7)
2(16.7)
3(16.7)
4(30.5)
5(30.5)
6(30.5)
7(39.8)
8(39.8)
9(39.8)
10(52.9)
11(52.9)
12(52.9)
13(64.8)
14(64.8)
15(64.8)
Pattern Number and Hematocrit
Diff
eren
ce (c
m)
Tangent X
Stringline X
Backtrack™ X
Figure 4.4 Difference between the known and calculated X coordinate value using the Tangent, String Line and BackTrack™ Methods to determine the Region of
Origin for 15 impact spatter patterns with differing hematocrit values.
85
Impact Patterns: Difference Between the Known and Calculated Y Coordinate Using the Tangent, String Line and BackTrack Methods
-14-12-10-8-6-4-202468
1012141618
1(16.7)
2(16.7)
3(16.7)
4(30.5)
5(30.5)
6(30.5)
7(39.8)
8(39.8)
9(39.8)
10(52.9)
11(52.9)
12(52.9)
13(64.8)
14(64.8)
15(64.8)
Pattern Number and Hematocrit
Diff
eren
ce (c
m) Tangent Y
Stringline Y
Backtrack™ Y
Figure 4.5 Difference between the known and calculated Y coordinate value using the Tangent, String Line and BackTrack™ Methods to determine the Region of
Origin for 15 impact spatter patterns with differing hematocrit values.
86
Impact Patterns: Difference Between the Known and Calculated Z Coordinate Using the Tangent String Line and BackTrack Methods
0
4
8
12
16
20
24
1(16.7)
2(16.7)
3(16.7)
4(30.5)
5(30.5)
6(30.5)
7(39.8)
8(39.8)
9(39.8)
10(52.9)
11(52.9)
12(52.9)
13(64.8)
14(64.8)
15(64.8)
Pattern Number and Hematocrit
Diff
eren
ce (c
m)
Tangent Z
Stringline Z
Backtrack™ Z
Figure 4.6 Difference between the known and calculated Z coordinate value using the Tangent, String Line and BackTrack™ Methods to determine the Region of
Origin for 15 impact spatter patterns with differing hematocrit values.
87
Impact Patterns: Difference Between Known and Calculated X Coordinate Using The Tangent Method
-4
-2
0
2
4
6
8
10
12
14
16
18
1 (16.7) 2 (16.7) 3 (16.7) 4 (30.5) 5 (30.5) 6 (30.5) 7 (39.8) 8 (39.8) 9 (39.8) 10(52.9)
11(52.9)
12(52.9)
13(64.8)
14(64.8)
15(64.8)
Pattern Number and Hematocrit
Diff
eren
ce (c
m)
Microsoft® Office Excel ManualBacktrack™
Figure 4.7 Difference between the known and calculated X coordinate value using the Tangent Method for bloodstains measured using Microsoft® Office Excel
Auto Shapes, Manual and BackTrack™ for 15 impact spatter patterns with differing hematocrit values.
88
4.2.2 Comparison of the Y Coordinate
The difference between the known and calculated Y coordinate was significant between
the blood hematocrit values [F = 2.93; df = 4, 30; P = 0.037]. The average difference
ranged from Y = 4.11cm ± 6.05cm at a hematocrit value of 39.8% to Y = -0.89cm ±
3.02cm for a hematocrit value of 52.9%. Out of the five different hematocrit and X, Y,
Z coordinate values, only two [16.7% and 52.9%] gave a similar negative difference;
both occurred with the Y coordinate value: Y = -0.44cm ± 4.80cm for 16.7%
hematocrit value and Y = -0.89cm ± 3.02cm for 52.9% hematocrit value. The
difference in Y coordinate value was similar for a hematocrit values of 64.8% [Y =
3.22cm ± 5.91cm], 39.8% [Y = 4.11cm ± 6.05cm] and 30.5% [Y = 0.89cm ± 5.35cm].
The interaction between the three different reconstructive techniques and hematocrit
value at the Y coordinate value was not significant [F = 0.72; df = 8, 30; P = 0.670].
The determination of the Y coordinate value was affected by the reconstructive method
[Microsoft® Office Excel 2003 Auto Shapes combined with Tangent, Microsoft® Office
Excel 2003 Auto Shapes combined with the String Line and BackTrack™ Images] [F =
17.28; df = 2, 30; P < 0.001]. When measured by Microsoft® Office Excel 2003 Auto
Shapes and the Tangent Method applied, the calculated average difference for the Y
coordinate value was 0.80cm ± 4.80cm; this result is similar to when BackTrack™
Images was used to determine the Y coordinate value [Y = -2.47cm ± 3.29cm].
However, both of these differed significantly when the Y coordinate value was obtained
using the Microsoft® Office Excel 2003 Auto Shapes combined with the String Line
Method: Y = 5.80cm ± 4.14cm.
When the bloodstains were measured manually, positive Y coordinate values were
obtained for seven of the 15 [47%] impact patterns. For the remaining eight patterns, 3
[20%] recorded an equal Y coordinate value [Patterns 3, 8 and 9], whilst five [33%]
recorded a negative Y coordinate value [Patterns 2, 5, 11, 12 and 14]. The difference
between the actual and calculated Y coordinate ranged from -6cm [Pattern 5] to 15cm
[Pattern 7]. The specific differences between the known and calculated Y coordinate
values using the Tangent Method [combined with Microsoft® Office Excel 2003 Auto
Shapes for stain measurement], Stringline Method [combined with Microsoft® Office
Excel 2003 Auto Shapes for stain measurement], and BackTrack™ Images are shown in
Table 4.2 and illustrated in Figure 4.5.
89
4.2.3 Comparison of the Z Coordinate
The difference between the known and calculated Z coordinate was significantly
different between blood hematocrit values [F = 7.81; df = 4, 30; P < 0.001] and
reconstructive method [Microsoft® Office Excel 2003 Auto Shapes combined with
Tangent, Microsoft® Office Excel 2003 Auto Shapes combined with the String Line and
BackTrack™ Images] [F = 4.60; df = 2, 30; P <0.05]. The average difference for the Z
coordinate was significantly lower for String Line Method [Microsoft® Office Excel
2003 Auto Shapes used for stain measurement] Z = 7.80cm ± 5.62cm when compared to
the Tangent Method Z = 12.27cm ± 4.92cm [Microsoft® Office Excel 2003 Auto Shapes
used for stain measurement]. The difference for the Z coordinate value obtained from
BackTrack™ Images Method [Y = 10.33cm ± 4.79cm] was similar to the String Line
Method [Microsoft® Office Excel 2003 Auto Shapes used for stain measurement] it was
also similar to the Tangent Method [Microsoft® Office Excel 2003 Auto Shapes used for
stain measurement].
The average difference for a hematocrit value of 52.9% was Z = 6.78cm ± 4.24cm. This
was similar to hematocrit values of 30.5% [Z = 8.22cm ± 5.43cm], 16.7% [Z = 8.44cm
± 2.74cm], and 68.8% [Z = 10.89cm ± 4.78cm]. At a blood hematocrit value of 39.8%
[Z = 16.33cm ± 3.87cm] the Z coordinate value was significantly higher than values
obtained for hematocrit values of 52.9%, 30.5% and 16.7%, but similar to hematocrit
value of 68.8% (Figure 4.6). There was no significant interaction between
reconstructive techniques and hematocrit value at the Z coordinate value [F = 0.77; df =
8, 30; P = 0.630].
Although the average difference for the Z coordinate value was as much as 16.33cm ±
3.87cm for patterns created with a blood hematocrit value of 39.8% [three methods and
three pattern average], all the average differences between the known and calculated Z
coordinate values were positive regardless of hematocrit value. For hematocrit values
of 16.7%, 30.5%, 52.9% and 64.8% the Z coordinate value were Z = 8.44cm ± 2.74cm,
Z = 8.22cm ± 5.43cm, Z = 6.78cm ± 4.27cm and Z = 10.89cm ± 4.78cm respectively.
The constant overestimation of the Z coordinate value corresponds to the theoretical
model of using straight lines to replicate blood droplet flight paths.
90
It is of interest to note that although hematocrit value did not significantly affect the
difference between known and calculated coordinate values for X coordinate value, the
greatest difference for the both Y and Z coordinate values were obtained from the
authors control blood hematocrit value of 39.8%: Y = 4.11cm ± 6.05cm and Z =
16.33cm ± 3.87cm (Table 4.2).
4.2.4 Comparison of the Stain Measurement Technique – Tangent Method
Blood hematocrit value did have an affect on the difference between the X coordinate
values when the three different stain measurement techniques [Microsoft® Office Excel
2003 Auto Shapes, Manual and BackTrack™ Images] were applied to the Tangent
Method [F = 4.68; df = 4, 30; P < 0.01]. The greatest average difference for the X
coordinate value was 6.22cm ± 5.89cm for a hematocrit value of 16.7%; compared to
the significantly lower difference for hematocrit values of 39.8%, 64.8% and 30.5%
[2.00cm ± 2.92cm, 1.33cm ± 2.87cm and 0.78cm ± 1.39cm respectively]. There was no
significant interaction between the three different stain measurement techniques
[Microsoft® Office Excel 2003 Auto Shapes, Manual and BackTrack™ Images] and
hematocrit value [F = 1.50; df = 8, 30; P = 0.197].
The three different stain measurement techniques [Microsoft® Office Excel 2003 Auto
Shapes, Manual and BackTrack™ Images] when applied to the Tangent Method did
significantly affect the difference between the known and calculated X coordinate value
[F = 3.80; df = 2, 30; P = 0.034]. The difference obtained for the manual measurement
technique was significantly higher than the BackTrack™ Images measurement
technique, 4.53cm ± 5.15cm and 1.53cm ± 2.75cm respectively. The manual
measurement technique and Microsoft® Office Excel 2003 Auto Shapes measurement
technique were similar, 4.53cm ± 5.15cm and 2.40cm ± 2.59cm respectively.
In 13 of the 15 impact spatter patterns where the bloodstains were measured manually
[87%], positive X coordinate values were obtained this compared with 12 [80%] and
nine [60%] for bloodstains measured using Microsoft® Office Excel 2003 Auto Shapes
and BackTrack™ Images respectively. The specific differences between the known and
calculated X coordinate value using the three different measurement techniques,
combined with the Tangent Method are shown in Table 4.2 and illustrated in Figure 4.7.
91
4.2.5 Comparison of Blood Hematocrit Value
Blood hematocrit value did affect the difference between known and calculated
coordinate values [F = 2.99; df = 4, 130; P = 0.021]. There was a positive average
difference for all of the hematocrit values ranging from 2.96cm ± 4.42cm for a
hematocrit value 52.9% to 7.63cm ± 7.76cm for hematocrit value of 39.8%. The
difference between the known and calculated coordinate values was similar for
hematocrit values of 52.9% [9.96cm ± 4.42cm], 30.5% [3.41cm ± 5.58cm], 16.7%
[4.07cm ± 5.06cm] and 64.8% [5.89cm ± 5.78cm]. However the difference between
coordinate values with hematocrit values of 30.5%, 16.7%, 64.8%, and 39.8% [7.63cm
± 7.76cm] were also similar.
4.2.6 Comparison of the Coordinate Value
The Z coordinate was significantly different when compared with the X and Y
coordinate values [F = 45.76; df = 2, 132; P <0.001]. The average difference between
the known and calculated X value was 2.87cm ± 2.87cm and the Y value 1.38 ±
5.30cm. There was a significant increase from difference for both the X, and Y value to
the Z value of 10.13cm ± 5.34cm.
4.2.7 Comparison of Reconstructive Technique
Overall the reconstructive technique [Microsoft® Office Excel 2003 Auto Shapes
combined with Tangent, Microsoft® Office Excel 2003 Auto Shapes combined with the
String Line and BackTrack™ Images] had no affect on the average difference between
the known and calculated coordinate values [X, Y and Z] [F = 2.28; df = 2, 132; P =
0.106]. The average differences were 5.16cm ± 6.59cm for Microsoft® Office Excel
2003 Auto Shapes combined with Tangent, 5.91cm ± 4.57cm for Microsoft® Office
Excel 2003 Auto Shapes combined with String Line Method and 3.31cm ± 6.45cm for
BackTrack™ Images Method. Although the differences up to 21cm [Pattern 8
BackTrack™ Images Z coordinate value and Pattern 9 Tangent Method Z coordinate
value] were observed between the known and calculated coordinate values, Region of
Origin similarity is evident between the Tangent, String Line and BackTrack™ Images
Methods.
92
Average manual, Microsoft® Office Excel 2003 Auto Shapes and BackTrack™ Images
measurement data for these bloodstains is shown in Table 4.3. Close measurement
agreement is evident between the manual, Microsoft® Office Excel 2003 Auto Shapes
and BackTrack™ Images measurement methods. When manually measured the average
calculated ellipse width [n = 152] was 1.20mm [range 0.94mm to 1.52mm], average
calculated ellipse length [n =152] was 3.69 [range 2.87mm to 4.80mm] and the average
calculated impact angle over the 15 patterns was 19.80° [range 15.91° to 24.16°]. When
measured using Microsoft® Office Excel 2003 Auto Shapes the average calculated
ellipse width [n=152] was 1.21mm [range 0.94mm to 1.54mm], average calculated
ellipse length 3.81 [range 2.96mm to 5.00mm] and the average calculated impact angle
for the 15 patterns was 18.70° [range 15.82° to 21.27°]. When measured using
BackTrack™ Images the average calculated ellipse width [n=152] was 1.18mm [range
0.93mm to 1.51mm], average calculated ellipse length [n=152] was 3.78 [range 2.90mm
to 4.98mm] and the average calculated angle of impact for the 15 pattern was 18.26°
[range 15.43° to 21.73°].
Figures 4.8 to 4.12 show Impact Spatter Pattern 2 created with a blood source
hematocrit value of 16.7%. Figures 4.13 to 4.20 show Impact Spatter Pattern 15 created
with a blood source hematocrit value of 64.8%. In order to give an indication of a
spatial context for the various experimental treatments conducted, comparative X, Y
and Z coordinate measurement results for the actual blood source and the
experimentally derived values using the Tangent Method [combined with the
Microsoft® Office Excel 2003 Auto Shapes measurement technique], String Line
Method [combined with the Microsoft® Office Excel 2003 Auto Shapes measurement
technique] and BackTrack™ Images Method are indicated.
93
Table 4.3 Shows Manual, Microsoft® Office Excel Auto Shapes (ITALICS), BackTrack™ (BOLDED) measurement data for bloodstains for Impact Spatter Patterns
1 to 15.
Stains Bloodstain Width Bloodstain Length Impact Angle Calculations (n) Range (mm) Average (mm) Range (mm) Average (mm) Range Average
Pattern 1 (16.7) 10
0.84 – 1.68 1.10 2.56 – 6.38 3.42 15.27 – 23.48 19.23 0.82 – 1.74 1.08 2.56 – 6.04 3.55 13.10 – 21.30 18.00 0.83 – 1.69 1.05 2.47 – 6.31 3.64 13.80 – 19.70 16.96
Pattern 2 (16.7) 7
1.22 – 2.15 1.62 3.32 – 4.58 3.68 21.56 – 31.77 26.07 1.24 – 2.17 1.62 3.22 – 5.43 4.11 20.50 – 25.50 23.11 1.24 – 2.25 1.61 3.36 – 5.43 4.28 19.60 – 24.40 22.01
Pattern 3 (16.7) 9
1.14 – 1.62 1.35 2.84 – 3.74 3.32 21.64 – 32.44 24.27 1.09 – 1.58 1.33 2.98 – 4.53 3.84 17.70 – 26.10 20.47 1.04 – 1.58 1.32 3.03 – 4.65 3.82 16.70 – 25.60 20.49
Pattern 4 (30.5) 10
0.82 – 1.57 1.26 3.34 – 5.86 4.23 13.11 – 23.50 17.54 0.90 – 1.65 1.27 3.23 – 5.71 4.14 15.30 – 20.10 17.77 0.80 – 1.60 1.23 3.30 – 5.50 4.13 14.20 – 21.20 17.44
Pattern 5 (30.5) 10
0.95 – 1.40 1.17 2.94 – 4.72 3.79 14.34 – 21.97 18.30 1.04 – 1.42 1.19 3.24 – 5.00 3.93 15.10 – 19.90 17.75 1.06 – 1.39 1.18 3.05 – 4.88 3.89 14.90 – 20.60 17.92
Pattern 6 (30.5) 10
0.97 – 1.20 1.12 2.90 – 4.90 3.62 14.06 – 23.58 18.47 1.00 – 1.29 1.15 2.99 – 4.39 3.77 15.60 – 21.30 17.92 0.97 – 1.24 1.10 2.96 – 4.35 3.66 16.00 – 22.30 17.62
Pattern 7 (39.8) 12
0.73 – 1.34 0.89 2.34 – 3.44 2.77 15.68 – 22.93 18.73 0.67 – 1.29 0.85 2.27 – 4.16 2.85 15.60 – 18.30 17.34 0.60 – 1.30 0.82 2.20 – 4.50 2.89 14.30 – 18.60 16.68
Pattern 8 (39.8) 12
0.97 – 1.45 1.22 2.86 – 5.08 3.65 14.87 – 26.36 19.94 0.99 – 1.43 1.22 2.84 – 5.40 3.68 14.80 – 23.90 19.67 0.96 – 1.37 1.17 2.90 – 5.17 3.60 14.50 – 26.50 19.36
Pattern 9 (39.8) 12
0.87 – 1.48 1.17 3.00 – 4.74 3.78 15.47 – 21.05 18.10 0.89 – 1.42 1.18 2.78 – 4.64 3.89 15.90 – 21.80 17.84 0.85 – 1.47 1.16 2.71 – 4.89 3.94 14.90 – 21.40 17.23
Pattern 10 (52.9) 10
0.94 – 1.22 1.08 2.40 – 4.64 3.43 15.24 – 23.06 18.70 0.90 – 1.29 1.05 2.85 – 4.34 3.52 15.00 – 18.90 17.31 0.87 – 1.23 1.02 2.66 – 4.55 3.48 13.30 – 19.90 17.27
94
Stains Bloodstain Width Bloodstain Length Impact Angle Calculations
(n) Range (mm) Average (mm) Range (mm) Average (mm) Range Average
Pattern 11 (52.9) 10
0.92 – 1.49 1.17 2.62 – 3.94 3.39 16.13 – 26.28 20.44 0.95 – 1.52 1.19 2.77 – 4.28 3.51 16.20 – 24.40 19.92 0.90 – 1.50 1.15 3.00 – 4.23 3.49 16.20 – 22.10 19.02
Pattern 12 (52.9) 10
1.03 – 1.84 1.33 2.84 – 5.50 4.04 16.78 – 22.10 19.27 0.96 – 1.82 1.28 2.93 – 5.72 4.06 15.90 – 20.30 18.39 0.94 – 1.74 1.25 2.95 – 5.61 3.96 16.40 – 20.10 18.44
Pattern 13 (64.8) 10
0.81 – 1.28 1.04 2.96 – 4.18 3.71 14.30 – 21.78 16.36 0.83 – 1.21 1.02 2.74 – 4.37 3.56 14.70 – 19.50 16.87 0.83 – 1.17 1.00 2.58 – 3.98 3.41 15.60 – 18.70 17.08
Pattern 14 (64.8) 10
0.97 – 1.58 1.32 3.12 – 5.36 4.43 15.35 – 21.83 17.51 1.04 – 1.83 1.38 3.60 – 6.06 4.72 15.80 – 22.10 17.14 0.99 – 1.54 1.34 3.37 – 5.28 4.50 15.40 -21.00 17.34
Pattern 15 (64.8) 10
0.92 – 1.45 1.18 3.00 – 4.96 4.04 14.86 – 20.23 17.11 1.07 – 1.52 1.29 3.35 – 4.89 4.06 16.10 – 22.40 18.76 1.02 – 1.54 1.28 2.96 – 5.34 3.99 15.60 – 23.90 18.98
0.94 – 1.52 mm 1.20 mm 2.87 – 4.80 mm 3.69 mm 15.91° – 24.16° 19.80° 0.96 – 1.54 mm 1.21 mm 2.96 – 5.00 mm 3.81 mm 15.82° – 21.27° 18.70° 0.93 – 1.51 mm 1.18 mm 2.90 – 4.98 mm 3.78 mm 15.43° – 21.73° 18.26°
95
Figure 4.8 Impact Spatter Pattern 2 (16.7 Hematocrit) – actual blood source Z value indicated.
Figure 4.9 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of experimentally derived Z coordinate values with actual blood source Z coordinate value.
Z Actual 100cm
Z Actual 100cm Z Tangent 110cm
Z Stringline 108cm Z BackTrack™ 108cm
96
Figure 4.10 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of experimentally
derived X coordinate values with actual blood source X coordinate value.
Figure 4.11 Impact Spatter Pattern 2 (16.7 Hematocrit) – Comparison of experimentally
derived Y coordinate values with actual blood source Y coordinate value.
X Actual 40cm X Tangent 47cm
X Stringline 47cm X BackTrack™ 46cm
Y Actual 180cm Y Tangent 176cm
Y Stringline 182cm Y BackTrack™ 179cm
97
Figure 4.12 Impact Spatter Pattern 2 (16.7 Hematocrit) – wooden blood source support positioned as to indicate the actual blood source location top of blood.
98
Figure 4.13 Impact Spatter Pattern 15 (64.8 Hematocrit) – actual blood source Z coordinate
value indicated.
Figure 4.14 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of experimentally
derived Z coordinate values with actual blood source Z coordinate value.
Z Actual 100cm
Z Actual 100cm Z Tangent 117cm
Z Stringline 112cm Z BackTrack™ 114cm
99
Figure 4.15 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of experimentally derived X coordinate values with actual blood source X coordinate value.
Figure 4.16 Impact Spatter Pattern 15 (64.8 Hematocrit) – wooden blood source support positioned as to indicate the actual blood source location top of block.
X Actual 40cm X Tangent 45cm
X Stringline 48cm X BackTrack™ 46cm
100
Figure 4.17 Impact Spatter Pattern 15 (64.8 Hematocrit) – Comparison of experimentally derived Y coordinate values with actual blood source Y coordinate value.
Figure 4.18 Top view 2D representation of Impact Spatter Pattern 15 (64.8 Hematocrit) using BackTrack™. The red cross indicates the X and Y coordinates.
Y Actual 180cm Y Tangent 182cm
Y Stringline 190cm Y BackTrack™ 178cm
101
Figure 4.19 Side view 2D representation of Impact Spatter Pattern 15 (64.8 Hematocrit) using BackTrack™. The red cross indicates the X and Z coordinates.
Figure 4.20 End view 2D representation of Impact Spatter Pattern 15 (64.8 Hematocrit) using BackTrack™. The red cross indicates the Y and Z coordinates.
102
4.3 Discussion
The results demonstrate a significant difference between the determination of X, Y and
Z coordinate values for the varying blood hematocrit values. However, the calculated
coordinate values are all within the acceptable industry limits as described by Carter et
al. (2006). The reported statistical differences appears to be a function of the resolving
power of the applied mathematics. Subsequently, hematocrit value of blood does not
influence the analysts’ ability to reliably determine the ROO for a blood source within
3D space. Further, the impact spatter patterns support the results obtained for the
passive drop experiments (see Section 3.2). Although hematocrit value was
demonstrated to affect the width and length of the resultant bloodstain, a proportional
relationship appears to exist such that hematocrit value does not affect the width to
length ratio and thus the reliability of the calculated impact angle (see Section 3).
The formation of an impact spatter pattern is influenced by a number of factors
including; velocity of impacting force, directionality of applied force, dimensions of the
wound providing the blood source, surface texture and physical obstructions (James et
al. 2005). It is of interest to note it was difficult to produce bloodstain impact patterns
with a blood hematocrit value of 16.7%. After the application of the impact force to the
blood source with a 16.7% hematocrit value it was evident that the bloodstains were not
deposited as high in the vertical direction on the receiving surface. Also, the individual
bloodstains were generally larger in diameter than bloodstains of impact patterns
produced with a higher blood hematocrit value. The observed difference in bloodstain
size for the impact patterns is supported by studies undertaken in Chapter Four which
showed that hematocrit value decreased, both stain width and length increased. These
results suggest that for impact spatter patterns the distribution and representative size of
bloodstains are also a function of the hematocrit value and not just the levels of applied
force.
For each of the 15 impact patterns, irrespective of the reconstructive technique used, the
calculated Z coordinate value was equal to or greater than the known Z coordinate
value. The calculated Z coordinate value [height of the blood source from the ground]
provides an upper limit for the blood source. All bloodstain reconstructive techniques
assume that the blood droplet travels in a straight line trajectory but it has been
determined that a blood droplet actually follows a parabolic flight path due to the
103
influence of gravity and air resistance (Carter 2001; Carter et al. 2005; James et al. 2005
Carter et al. 2006). The blood droplet’s actual flight path cannot be calculated because
the size and speed of the blood droplets is unknown. Therefore, by using of straight line
trigonometry to replicate droplet flight paths the calculated Z coordinate value will
always be higher than the actual blood source value (Carter 2001; Carter et al. 2006).
For the impact spatter patterns a maximum difference between the known coordinate
value and experimentally calculated coordinate value occurred on the Z coordinate
[21cm] (Table 4.2). The average difference between the known and calculated X
coordinate value is 2.87cm, 1.38cm for the Y coordinate value, and 10.13cm for the Z
value. These X, Y, and Z values are less than the radius of a human head, a comparison
used by Carter et al. (2006). The results obtained for this experiment support Carter et
al. (2006) who stated that when estimating the intersection of strings a range of 10cm to
20cm is typical and even with this range it is still adequate to allow the interpretation of
events that may have led to the pattern deposition [the position of the victim; laying,
sitting or standing]. Carter et al. (2006) had similar results when using BackTrack™
alone to determine ROO of a blood source.
For impact spatter patterns found at an actual crime scene the size of the blood source
[wound] will have a direct impact on the analysts ability to determine the location of the
blood source. A blood droplet could originate from any point within the wound plus
any area on which blood has been deposited (James et al. 2005). For this experiment
the static blood source was stated with known X and Y coordinates and a ±2cm level of
accuracy. With the known values having a ±2cm level of accuracy it can therefore be
assumed that the calculated X and Y coordinates will also have ±2cm level of variation
without taking into account any other potential sources of reconstructive error.
When the Tangent Method [combined with Microsoft® Office Excel 2003 Auto Shapes]
was used to determine the Region of Origin of the blood source, equal or positive X
coordinate values were obtained in 10 out of the 15 impact patterns [67%]. For the
String Line Method [combined with Microsoft® Office Excel 2003 Auto Shapes] 15 out
of 15 [100%] equal or positive X coordinate values were calculated. When the
BackTrack™ Images Method was used to determine the Region of Origin, equal or
positive X coordinate values were obtained for two out of 15 impact patterns [13%]. A
positive X coordinate value indicates that the calculated blood source will be further
104
away from blood bearing surface than the actual X value (Reynolds 2008). The
overestimation of the location of the blood source indicates an overestimation in
calculated impact angles for the selected bloodstains [underestimation of ellipse length,
relative to width]. However, an underestimation of the blood source location indicates
an underestimation in calculated impact angles. James et al. (2005) suggest that a 10%
difference in the X coordinate can be explained by a systematic underestimation of
ellipse length with the remaining difference being attributed to either random error or
inappropriate stain selection.
When applying the Tangent Method, the measurement of bloodstains using computer
programs [Microsoft® Office Excel 2003 Auto Shapes and BackTrack™ Images] is
more accurate than measuring the stains manually. However, for reconstructive
purposes using either the Tangent Method or String Line Method is as accurate as using
the computer program, BackTrack™ Images. These results support Carter et al. (2006)
who determined that even though it was expected that the computer program
BackTrack™ Images would be more accurate than the manual String Line Method, the
results obtained were actually not significant. Therefore the uncertainties in the
reconstructed path of a blood droplet can obviously be minimised through the use of the
best measurement technique available [Microsoft® Office Excel 2003 Auto Shapes or
BackTrack™ Images] with a Bloodstain Pattern Analyst having the ability to choose the
reconstructive technique that suits a particular bloodshed scene, based on both the
physical factors of the scene and the analyst’s ability to access the computer programs
without compromising accuracy.
105
4.4 Conclusions The findings of the present thesis provide an insight into the effect of one specific
biological property of blood [hematocrit percent of blood] on the reconstruction of
bloodshed events.
The main objectives of this study were:
(i) To determine the effect of hematocrit value on the angle of impact
calculation theory using single drop experimentation (Chapter 3).
(ii) To examine any error associated with the calculation of angle of impact for
bloodstains generated with different blood hematocrit values using both
manual and computer assisted measurement techniques (Chapter3).
(iii) To determine if the ability to predict the ‘Region of Origin’ of the blood
source is influenced by hematocrit value. This will be conducted using
three different industry accepted reconstruction methods: the String Line
Method [combined with Microsoft® Office Excel 2003 Auto Shapes], the
Tangent Method [combined with Microsoft® Office Excel 2003 Auto
Shapes] and computer assisted Directional Analysis Method [BackTrackTM
Images] (Chapter 4).
Subsequently, the main conclusion of this thesis is:
(i) Donor blood hematocrit values have no effect on the reliable determination
of the Region of Origin of the blood source.
Additional thesis conclusions are:
(ii) The computer fitting of a theoretical ellipse for bloodstain measurement
purposes is more accurate than manual measurement.
(iii) All three reconstructive techniques [The Tangent, The String Line and
BackTrack™ Images] are comparable and reliable blood source Region of
Origin determination methods.
106
5. Future Directions
The Use of the Three Dimensional Scanner for Bloodstain Impact Pattern
Reconstruction: The Western Australia Police Forensic Division does not currently use
BackTrack™ Images for the virtual reconstruction analysis of bloodshed events.
Although results presented in this thesis suggest that the String Line Method is reliable
when compared to BackTrack™ Images, a question has arisen from this study; are there
any other computer assisted methods available for 3D reconstruction of bloodshed
events? With the introduction of 3D scanners for the forensic investigation and
examination of crime scenes it has been noted that bloodstains and bloodstain patterns
can be observed on the computer generated image. Can the 3D scanner be used to
determine the 3D Region of Origin and is it a valid reconstructive technique when
compared against the current industry accepted methods.
Selection of Bloodstains for Reconstructive Purposes: When using Microsoft® Office
Excel 2003 Auto Shapes to fit an ellipse to a bloodstain and calculate the impact angle,
it was noted that accurate results were obtained for bloodstains created at angles <30°.
As such, bloodstains <30° may be used for 3D Region of Origin determination. Further
studies are required relating to the accuracy and precision of impact angles <30° to
determine the 3D Region of Origin.
Determining the Error Range for each Reconstructive Technique: This thesis
mentioned that industry accepted error rates occur for each reconstructive technique. In
order to reliably present the Region of Origin results in a court of law research into the
source of these errors is required. Is the error a combination of blood sheeting
[movement in the vertical direction of the blood volume before detachment of the blood
droplet], measurement error, bloodstain selection, and/or impact velocity?
107
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