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					    Lecture: Forensic Evidence




        Physical Evidence
Any material either in gross or trace
quantities that can establish through
 scientific examination and analysis
  that a crime has been committed.
            Forensic laboratories

            Items of physical evidence


identification       evaluation    individualization
  Classification of Physical Evidence

• Trace evidence         extremely small items

• Direct evidence        stands on its own to prove an
                          alleged fact

• Prima facie evidence   evidence established by law

• Circumstantial
  evidence               incriminates a person

• Exculpatory evidence   helps to prove that an
                          accused individual is not
                          guilty
 Physical evidence utilization in other areas
           of forensic investigation

• Provides investigative leads for a case
• Ties one crime to a similar crime or connects one
  suspect with another
• Corroborates statements from witnesses to or
  victims of a crime
• The elements of a crime help to determine what
  will be useful as evidence.
• Besides knowing what types of evidence to search
  for, it is necessary to know where evidence is most
  likely to be found.
     Characteristics of evidence
                                                      features that place the item
Class characteristics                                into a specific category

                                                      features that distinguish one
Individual characteristics                           item from another of the same
                                                      type

              E x a m in a t io n a n d a n a l y s is o f p h y s ic a l
                                   e v id e n c e



       H ig h e s t d e g r e e o f s c ie n t ific c e r ta in t y p o s s ib le w ith
                                c u rre n t te c h n o lo g y


        p h y s ic a l                c h e m ic a l                 b io lo g ic a l
    id e n tific a tio n           id e n tific a tio n            id e n tific a tio n
Evidence       Analyte/Characteristic Techniques
Blood          Ethanol
               Drugs of abuse        Headspace analysis GC GC/MS
Fabric         Composition           FT-Raman spectroscopy
               Color                 Visible, diffuse reflectance
                                     spectroscopy
Fibers         Composition           FT-IR microscopy
               Physical properties   Solubility, melting point
Glass          Refractive index      Microscopy
               Magnesium             Atomic absorption
                                     spectrophotometry
Shoes          Miscellaneous         Solid-phase extraction; LC
Powder         Drugs of abuse        FT-IR
Soil           pH                    Potentiometry
               Iron                  UV-Visible Spectr.
Saliva stain   Proteins              Immunological tests
               DNA                   Short tandem repeat
                                     DNA analysis
Hair           Appearance            Microscopy
Evidence            Techniques
Gunshot residue     Atomic absorption spectrophotometry,
                    scanning electron microscopy
Clothing            Visible reflectance, FT-IR microscopy, FT-
                    Raman
Pen inks            UV–vis, LC
Plastic fragments   FT-IR, UV–vis
Tire fragments      Thermal analysis, FT-IR
Food (poisoned)     Liquid- and solid-phase extraction, GC/MS
Fingerprints        Fluorescent visualization
Metals              Atomic absorption spectrophotometry,
                    titrations

Arson samples       GC, GC/MS
One morning in the summer of 1961, hundreds of crazed birds attacked the seaside
town of Capitola, California. The birds "cried like babies" as they dove into streetlamps,
crashed through glass windows, and attacked people on the ground. Most of the birds
were sooty shearwaters, a normally nonaggressive species that feeds on small fish and
comes ashore only to breed. The incident fascinated Alfred Hitchcock, who frequently
vacationed in nearby Santa Cruz. He included newspaper clippings about the Capitola
attack in his studio proposal for The Birds, which appeared in cinemas two years later.

In the winter of 1987, the agent that is now believed to be responsible for the Capitola
incident struck on the opposite shore of the continent. This time, it struck higher on the
food chain. Over a hundred people became extremely ill within hours after dining on
cultured blue mussels in restaurants around Prince Edward Island in Canada. It quickly
became apparent that this was no ordinary outbreak of food poisoning. Vomiting,
cramps, diarrhea, and incapacitating headaches were followed by confusion, loss of
memory, disorientation, and (in severe cases) seizures and coma. A few exhibited
emotional volatility, with uncontrolled crying or aggressiveness. Three elderly victims
died. [Perl].

A tragic symptom of poisoning was the destruction of short term memory in about one
quarter of the survivors. They could remember nothing that happened after the
poisoning. Some were unable to recognize their surroundings or relatives. They could
learn no new facts or skills. The most severely affected lost memories several years old.
For twelve of the victims, the loss of short term memory was permanent.
Figure 1. General strategy for isolation of the toxin responsible for
amnesic shellfish poisoning. Based on a diagram by M. Quilliam and J. L.
C. Wright (Analytical Chemistry, 61, 1054 (1989)).
A band very close to the band for glutamic acid was observed in the electrophoresis of the toxic XAD-2
fraction, but not in the control fraction. It stained a distinctly different color from the glutamic acid. When
the material in the band was collected and injected onto the HPLC column, it took exactly the same
amount of time to move through the column as the toxic component found by the HPLC analysis. It also
produced exactly the same amount of toxicity as the HPLC fraction had.
Mass spectrometry was used to determine the compound's molecular weight (312 g/mol) and molecular
formula (C15H22NO6). Spectroscopic analysis revealed the presence of conjugated double bonds and
features characteristic of an amino acid. By matching the spectra with those from STN International's
Registry system, the compound was unambiguously identified as domoic acid, an triprotic amino acid:




             Domoic acid in acidic solution.                  Glutamic acid in acidic solution.
 Domoic acid is a molecular Trojan Horse. Nerve cells mistakenly
recognize domoic acid as glutamic acid- a fatal error. Domoic acid's
structure is obviously similar to glutamic acid. But its five-sided ring
makes it less flexible than glutamate, which causes it to bind very
tightly to glutamate receptors. As a result, the excitatory effect of
domoate is 30 to 100 times more powerful than that of glutamate
[Perl].
                  Individualization




            Forensic laboratories

            Items of physical evidence


identification       evaluation    individualization
       Characteristics of evidence

• Class characteristics   features that place the
                           item into a specific
                           category


• Individual              features that distinguish
  characteristics          one item from another
                           of the same type
                  Types of physical evidence

• BODY FLUIDS
 Conventional serology:
    presence of blood in stains
    species identification and
     ABO grouping
    is not adequately
     informative to positive         SEM: erythrocytes & lymphocytes
     identify a person
 DNA analysis can associate
  victim and/or suspect with each
  other or with the crime scene
• BLOODSTAIN PATTERNS
    additional information
                  Types of physical evidence


• BODY TISSUES                    toxicological analysis
   organ samples collected at       volatile compounds (ethanol,
                                      methanol, isopropanol)
    autopsy, including blood,
                                     heavy metals (arsenic)
    urine and stomach contents       nonvolatile organic compounds
                                      (drugs of abuse, pharmaceuticals)
                                     miscellaneous (strychnine,
                                      cyanide)

• DRUGS & CONTROLLED              trace drug presence, identity,
  SUBSTANCES
                                   and quantity
   plant materials, powders,
    tablets, capsules


                                   Black tar heroine wrapped in cellophane
                   Types of physical evidence

• DOCUMENTS
   examination

 typed, handwritten and printed
  materials for evidence of forgery
 indented writings, obliterated or
  altered writings, used carbon
  paper, burned or charred paper

   paper and ink analysis
                                         Obliterated writing examination
   handwriting comparison to
    determine authenticity
                Types of physical evidence

• HAIRS
   hairs analysis can       human/animal
    determine                race
                             body area
                             cosmetic treatments
                             method of removal (crushed,
                              cut, burned, forcibly removed,
                              fallen out naturally)
  morphological features    can associate a hair to a
                              person
  DNA analysis              positive identification
  toxicological             presence of drugs and poisons
   examination
 FIBERS                     type
                             color, composition construction
                     Types of physical evidence



Two matching hairs identified with the
comparison microscope




Flax fibers viewed with
polarized light
                   Types of physical evidence


• FINGERPRINTS
 the strongest possible
  evidence of a person’s
  identity

                                                Fingerprint Matching


• FIRE DEBRIS &
  EXPLOSIVES RESIDUE
  EXAMINATIONS
 identification of accelerants
  and explosive residues
                                  Unburned accelerator liquid on a soot covered carpet
                    Types of physical evidence

• FIREARMS & AMMUNITION
 individual microscopic marks
    identification, source,
     operability of firearms.
 detection and characterization           Photomicrograph: test
  of gunpowder residues                   bullet - questioned bullet

 muzzle-to-garments distance
  estimation
• GLASS FRAGMENTS
   Cause of breakage
    Direction of breakage force
    Physical fitting                      Glass fracture produced

    Glass fragment comparisons            by a high-speed projectile
                   Types of physical evidence


• PAINT & PAINT PRODUCTS
 analysis and comparison of
  paint transferred from the
  surface of an object to another
  during the commission of a
  crime:
 Suspect vehicle impacting a
  victim vehicle; a pedestrian or a
  stationary object
 Tool impacting stationary object
 Paint databases can help
  identify the year, make and/or      Paint Layers on Wood Surface
  color of a motor vehicle from a
  chip of paint left at the scene.
                   Types of physical evidence

• TOOLMARK IDENTIFICATION
 microscopic side-by-side
  comparison
 attempts to link a particular tool
  with a particular mark to the
  exclusion of any other tool
                                       Spacing between teeth in gripping -major
                                            role in toolmark examinations
• ROPE & CORDAGE
 composition, construction, color          manufacturer
  and diameter
                  Types of physical evidence


• SOILS & MINERALS
 comparison between two or
  more soils to determine if
  they share a common origin
 color, texture, composition
  comparison                    Layers of soil exposed at a grave site. Each
                                           layer must be sampled

• WOOD
 place the suspect at the
  crime scene
 side or end matching,
  fracture matching and
  species identification.

                                           Cross-section - Xylem
                    Types of physical evidence


• OILS/GREASE &                      possess unique composition
  COSMETIC PRODUCTS                   for comparison


• SHOEPRINTS & TIRE                                     Shoeprint
  TREAD IMPRESSIONS                                     collected
                                                        using a
 have value for forensic                               gelatin lifter.
  comparisons.
 can provide positive
  identification of the suspect’s
  shoes or tires from the
  suspect’s vehicle.
      Processing physical evidence



• discovering, recognizing and examining it;
• collecting, recording and identifying it;
• packaging, conveying and storing it;
• exhibiting it in court;
• disposing of it when the case is closed.
     Lecture: Forensic Evidence and
               Probability

        Characteristics of evidence


• Class characteristics   features that place the
                           item into a specific
                           category


• Individual              features that distinguish
  characteristics          one item from another
                           of the same type
 The arithmetic mean is the "standard"
 average, often simply called the "mean"


The standard deviation (SD) quantifies variability.
If the data follow a bell-shaped Gaussian
distribution, then 68% of the values lie within one
SD of the mean (on either side) and 95% of the
values lie within two SD of the mean. The SD is
expressed in the same units as your data.
1% of women at age forty who participate in routine screening have breast
cancer. 80% of women with breast cancer will get positive mammographies. 9.6%
of women without breast cancer will also get positive mammographies. A woman
in this age group had a positive mammography in a routine screening. What is the
probability that she actually has breast cancer?
1% of women at age forty who participate in routine screening have breast
cancer. 80% of women with breast cancer will get positive mammographies. 9.6%
of women without breast cancer will also get positive mammographies. A woman
in this age group had a positive mammography in a routine screening. What is the
probability that she actually has breast cancer?

                            STATISTICAL SOLUTION
To put it another way, before the mammography screening, the 10,000 women can
be divided into two groups:
•Group 1: 100 women with breast cancer.
•Group 2: 9,900 women without breast cancer.

 After the mammography, one gets:
* 80 women with breast cancer, and a positive mammography.
  i.e. 80% of 100
* 950 women without breast cancer, and a positive mammography.
  i.e. 9.6% of 9900

The probability that a patient with a positive mammogram has breast cancer is:
# (breast cancer + positive mammorgraphy) / #(positive mammorgraphy )
= 80/(80+950) = 7.8%
1% of women at age forty who participate in routine screening have breast
cancer. 80% of women with breast cancer will get positive mammographies. 9.6%
of women without breast cancer will also get positive mammographies. A woman
in this age group had a positive mammography in a routine screening. What is the
probability that she actually has breast cancer?

                             BAYESIAN SOLUTION

The original proportion of patients with breast cancer is known as the prior
probability:
 P(C) = 1% and P(~C) = 99%
The chance of a patient having a positive mammography given that she has cancer,
and the chance that of a patient having a positive mammography given that she does
not have cancer, are known as the two conditional probabilities. Collectively
information is often termed the liklehood ratio:
P(M | C) = 80% i.e probability of +ve mammogram given that she has cancer
P(M | ~C) = 9.6% i.e probability of +ve mammogram given that she does not
                                         have cancer
The final answer - the estimated probability that a patient has breast cancer given
that we know she has a positive result on her mammography - is known as the
revised probability or the posterior probability.
1% of women at age forty who participate in routine screening have breast
cancer. 80% of women with breast cancer will get positive mammographies. 9.6%
of women without breast cancer will also get positive mammographies. A woman
in this age group had a positive mammography in a routine screening. What is the
probability that she actually has breast cancer?


prior probability x conditional probability = posterior probability

P(C) . P(M | C) = P(C | M)
P(~C) P(M | ~C) P(~C | M)

0.01 . 0.8 = 0.008 = 80
0.99 0.096 0.095     950

the estimated odds that a patient has breast cancer given that we know she has a
positive result on her mammography are 80 to 950

the estimated probability that a patient has breast cancer given that we know she
has a positive result on her mammography is 80 / (80+950) = 7.8%
 prior probability P(C) .
                  P(~C)
The probability that the suspect is or is not guilty prior to presenting this
evidence

 conditional probability P(M | C)
                        P(M | ~C)
Also called the Likelihood Ratio (LR) and represents the probability that this
evidence would be present if the suspect is or is not guilty

posterior probability P(C | M)
                      P(~C | M)

The probability that the suspect is or is not guilty given the evidence
presented
                   Bayesian Probability
•   Problem#1
    A suspect is seen fleeing the crime. The suspect is positively identified as being at least six feet tall and was
    wearing a nurse’s uniform. Exactly 5% of the male population is at least 6 feet tall, while 0.5% of the
    female population is at least 6 feet tall, and 98% of all nurses are female. What are the odds that the suspect
    is a male.

•   Problem#2
    1 million people in America have HIV/AIDS. HIV tests correctly identify a HIV infected person with a
    positive result 97.7% of the time. HIV tests correctly identify a non-HIV infected person with a negative
    result 92.6% of the time. If an American gets a positive HIV test result what are the odds that they are
    infected with HIV? (Assume an american population of 260 million)

•   Problem#3
    Suppose that a barrel contains many small plastic eggs. Some eggs are painted red and some are painted
    blue. 40% of the eggs in the bin contain pearls, and 60% contain nothing. 30% of eggs containing pearls
    are painted blue, and 10% of eggs containing nothing are painted blue. What is the probability that a blue
    egg contains a pearl?

•   Problem#4
    There are 100 people in a room, 20 women and 80 men. 80% of women are blonde, while 30% of the men
    are blonde. The suspect has blonde hair and is definitely one of the people in the room. What are the odds
    that the suspect is a female.

•   Problem#5
    The investigator on the case informs you that the odds that the suspect committed the crime are 2 to 1.
    Your DNA fingerprint analysis of the suspect’s blood gives a 1 in a million probability that it is a random
    match to the blood found at the crime scene. You also know that your lab has a 1 in a 1000 chance of a
    false positive. What are the odds that the blood found at the crime scene came from your suspect?
Defender’s Fallacy :
        P(S | M) = P(M | ~S) x sample population


Prosecutor’s Fallacy :
       P(S | M) = 1 - P(M | ~S)


     A crime has been committed, and a blood sample has been found at the
     crime scene. The blood is typed as A- , a blood type found in 5% of the
     population A suspect is identified, who also happens to have the A-
     blood type. In addition a DNA profile of the suspect gives the odds of a
     random match of his blood to the blood found at the crime scene of
     105 to 1.

     What are the odds that this suspect was present at the crime scene?
     What is the probability that this suspect was present at the crime
     scene?
     If the odds of a false positive for the DNA profile are one in a
     thousand, what are the odds that this suspect was present at the crime
     scene? What is the probability that this suspect was present at the
     crime scene?
                   Bayesian Probability
•   Problem#1
    A suspect is seen fleeing the crime. The suspect is positively identified as being at least six feet tall and was
    wearing a nurse’s uniform. Exactly 5% of the male population is at least 6 feet tall, while 0.5% of the
    female population is at least 6 feet tall, and 98% of all nurses are female. What are the odds that the suspect
    is a male.

•   Problem#2
    1 million people in America have HIV/AIDS. HIV tests correctly identify a HIV infected person with a
    positive result 97.7% of the time. HIV tests correctly identify a non-HIV infected person with a negative
    result 92.6% of the time. If an American gets a positive HIV test result what are the odds that they are
    infected with HIV? (Assume an american population of 260 million)

•   Problem#3
    Suppose that a barrel contains many small plastic eggs. Some eggs are painted red and some are painted
    blue. 40% of the eggs in the bin contain pearls, and 60% contain nothing. 30% of eggs containing pearls
    are painted blue, and 10% of eggs containing nothing are painted blue. What is the probability that a blue
    egg contains a pearl?

•   Problem#4
    There are 100 people in a room, 20 women and 80 men. 80% of women are blonde, while 30% of the men
    are blonde. The suspect has blonde hair and is definitely one of the people in the room. What are the odds
    that the suspect is a female.

•   Problem#5
    The investigator on the case informs you that the odds that the suspect committed the crime are 2 to 1.
    Your DNA fingerprint analysis of the suspect’s blood gives a 1 in a million probability that it is a random
    match to the blood found at the crime scene. You also know that your lab has a 1 in a 1000 chance of a
    false positive. What are the odds that the blood found at the crime scene came from your suspect?