Lecture: Forensic Evidence Physical Evidence Any material either in gross or trace

1. 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.

3. Trace evidence Direct evidence Prima facie evidence Circumstantial evidence Exculpatory evidence extremely small items stands on its own to prove an alleged fact evidence established by law incriminates a person helps to prove that an accused individual is not guilty Classification of Physical Evidence

4. 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.

5. Characteristics of evidence • features that place the item into a specific category • features that distinguish one item from another of the same type • Class characteristics • Individual characteristics

6. Evidence Analyte/CharacteristicTechniquesBlood Ethanol Drugs of abuse Headspace analysis GC GC/MSFabric Composition FT-Raman spectroscopy Color Visible, diffuse reflectance spectroscopyFibers Composition FT-IR microscopy Physical properties Solubility, melting pointGlass Refractive index Microscopy Magnesium Atomic absorption spectrophotometryShoes Miscellaneous Solid-phase extraction; LCPowder Drugs of abuse FT-IRSoil pH Potentiometry Iron UV-Visible Spectr. Saliva stain Proteins Immunological tests DNA Short tandem repeat DNA analysisHair Appearance Microscopy

7. Evidence TechniquesGunshot residue Atomic absorption spectrophotometry, scanning electron microscopyClothing Visible reflectance, FT-IR microscopy, FT- RamanPen inks UV–vis, LCPlastic fragments FT-IR, UV–visTire fragments Thermal analysis, FT-IRFood (poisoned) Liquid- and solid-phase extraction, GC/MSFingerprints Fluorescent visualizationMetals Atomic absorption spectrophotometry, titrationsArson samples GC, GC/MS

9. 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.

10. 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)).

13. 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: Glutamic acid in acidic solution. Domoic acid in acidic solution.

14. 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].

15. Individualization

16. Class characteristics Individual characteristics features that place the item into a specific category features that distinguish one item from another of the same type Characteristics of evidence

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

18. BODY TISSUES organ samples collected at autopsy, including blood, urine and stomach contents DRUGS & CONTROLLED SUBSTANCES plant materials, powders, tablets, capsules toxicological analysis volatile compounds (ethanol, methanol, isopropanol) heavy metals (arsenic) nonvolatile organic compounds (drugs of abuse, pharmaceuticals) miscellaneous (strychnine, cyanide) trace drug presence, identity, and quantity Black tar heroine wrapped in cellophane Types of physical evidence

19. 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 handwriting comparison to determine authenticity Types of physical evidence Obliterated writing examination

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

21. Types of physical evidence Two matching hairs identified with the comparison microscope Flax fibers viewed withpolarized light

22. FINGERPRINTS the strongest possible evidence of a person’s identity FIRE DEBRIS & EXPLOSIVES RESIDUE EXAMINATIONS identification of accelerants and explosive residues Types of physical evidence Fingerprint Matching Unburned accelerator liquid on a soot covered carpet

23. FIREARMS & AMMUNITION individual microscopic marks identification, source, operability of firearms. detection and characterization of gunpowder residues muzzle-to-garments distance estimation GLASS FRAGMENTS Cause of breakage Direction of breakage force Physical fitting Glass fragment comparisons Types of physical evidence Photomicrograph: test bullet - questioned bullet Glass fracture produced by a high-speed projectile

24. 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 color of a motor vehicle from a chip of paint left at the scene. Types of physical evidence Paint Layers on Wood Surface

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

26. SOILS & MINERALS comparison between two or more soils to determine if they share a common origin color, texture, composition comparison WOOD place the suspect at the crime scene side or end matching, fracture matching and species identification. Types of physical evidence Layers of soil exposed at a grave site. Each layer must be sampled Cross-section - Xylem

27. OILS/GREASE & COSMETIC PRODUCTS SHOEPRINTS & TIRE TREAD IMPRESSIONS have value for forensic comparisons. can provide positive identification of the suspect’s shoes or tires from the suspect’s vehicle. possess unique composition for comparison Types of physical evidence Shoeprint collected using agelatin lifter.

28. 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.

29. Class characteristics Individual characteristics features that place the item into a specific category features that distinguish one item from another of the same type Lecture: Forensic Evidence and ProbabilityCharacteristics of evidence

30. 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.

31. 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?

32. 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%

33. 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.

34. 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%

35. prior probabilityP(C) . P(~C) The probability that the suspect is or is not guilty prior to presenting this evidence conditional probabilityP(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

36. 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?

37. 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?

38. 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?