Physical and Chemical

Characterization of Matchsticks

for Forensic Classification and

Commercial Brand

Determination

Ciaran F. A. Phillip

Matchsticks

Three functional classes

i. Safety – purpose built striking surface

ii. Strike anywhere – no purpose built striking surface

iii. Waterproof – water resistant

Many different commercial brand manufacturers

2

Matchstick Forensics

Stereomicroscopy SEM-EDS X-ray Diffraction

Visually

different?

Y/N

Chemically

different?

Y/N

Structurally

different?

Y/N

Qualitative similarity

assessment on Q vs. K

3

Matchstick Forensics

Stereomicroscopy SEM-EDS X-ray Diffraction

Visually

different?

Y/N

Chemically

different?

Y/N

Structurally

different?

Y/N

Qualitative similarity

assessment on Q vs. K

How can we improve

trace evidence analysis?

4

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

5

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

Investigative leads Strengthens associations

Functional Class and

Commercial Brand of Q

6

Samples

Four brands of safety match

Four brands of waterproof match

Two brands of strike anywhere match

7

Samples

8

Samples

9

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

10

Stereomicroscopy

Safety

Diamond SB

Diamond MB

UCO & Fancy Fish

Waterproof

Coleman & Coghlan

REI

Proforce

Strike Anywhere

Redbird

Diamond SA

11

Stereomicroscopy

12

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

13

FTIR: Safety & Strike Anywhere

Diamond Deluxe Match Books

No polymer binder

1448.02 cm−1: CO32−

950.92 cm−1: PO43−

931.93 cm−1: ClO3−

14

FTIR: Waterproof

Shellac binder

1697.2cm−1: aromatic carbonyl

1454.2cm−1: azo

1375.2cm−1: aromatic C―N

1241.5cm−1: C―O

15

FTIR: Waterproof

Shellac binder

1697.2cm−1: aromatic carbonyl

1454.2cm−1: azo

1375.2cm−1: aromatic C―N

1241.5cm−1: C―O

REI

Alkyd binder

1715.4cm−1: carbonyl

1253.7cm−1: C―H

737.2cm−1: phenyl

16

FTIR: Waterproof

Shellac binder

1697.2cm−1: aromatic carbonyl

1454.2cm−1: azo

1375.2cm−1: aromatic C―N

1241.5cm−1: C―O

REI

Alkyd binder

1715.4cm−1: carbonyl

1253.7cm−1: C―H

737.2cm−1: phenyl

Coghlan &

Coleman

Nitrocellulose binder

1635.5cm−1: NO2

1268.6cm−1: C―N

17

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

18

ICP-MS Data

ICP-MS Metal Element Concentration Profiling Results [ppb·mg−1]

Brand B11 Mg25 Fe57 Zn (all) Zn70

Coleman 59.0843 138.6581 31.7377 3.4087 0.6359

Coleman 30.8953 90.0515 18.1351 2.0764 0.3827

Coleman 37.9094 98.9309 23.1432 2.3294 0.3956

Coleman 32.7572 95.0507 19.4906 2.3343 0.3967

ICP-MS Metal Element Concentration Profiling Results All Values Standardized by Maximum Value in each Variable

Brand B11 Mg25 Fe57 Zn (all) Zn70 Coleman 100.0000 100.0000 66.5726 1.2271 2.0766 Coleman 52.2902 64.9450 38.0400 0.7475 1.2497 Coleman 64.1615 71.3488 48.5449 0.8386 1.2918 Coleman 55.4415 68.5504 40.8833 0.8403 1.2954

ICP-MS Metal Element Concentration Profiling Results All Values Transformed by LN(X+1)

Brand B11 Mg25 Fe57 Zn (all) Zn70 Coleman 4.6151 4.6151 4.2132 0.8007 1.1238 Coleman 3.9758 4.1888 3.6646 0.5582 0.8108 Coleman 4.1769 4.2815 3.9029 0.6090 0.8294 Coleman 4.0332 4.2421 3.7349 0.6100 0.8309

How do we display this?

19

PCA

Linear combinations of all variables for each axis

Variable 1 = ln(11Bstd+1)

Variable 2 = ln(25Mgstd+1)

Variable 3 = ln(57Festd+1)

Variable 4 = ln(Znstd+1)

Variable 5 = ln(70Znstd+1)

Principal component 1 = cV1PC1(Variable 1)+cV2PC1(Variable 2)……

Principal component 2 = cV1PC2(Variable 1)+cV2PC2(Variable 2)……

Principal component 3 = cV1PC3(Variable 1)+cV2PC3(Variable 2)……

Principal component 4 = cV1PC4(Variable 1)+cV2PC4(Variable 2)……

Principal component 5 = cV1PC5(Variable 1)+cV2PC5(Variable 2)……

20

PCA

Account for maximum variation between samples

PC Percent

Variation Cumulative Percent

Variation

1 55.2 55.2

2 36.6 91.8

3 5.7 97.5

4 2.2 99.8

5 0.2 100.0

21

ICP-MS PCA

22

ICP-MS PCA

23

ANOSIM

PCA emphasises differences between sample groups

Are the emphasised differences statistically significant?

𝑅 =𝑟 𝐵 − 𝑟 𝑊𝑛(𝑛 − 1)/4

Test Statistic R

−1 ≤ R ≤ 1

R→1 Differences between

groups become more significant

24

ANOSIM

ANOSIM R values

W vs. S 0.163

W vs. SA 0.59

S vs. SA 0.171

25

Objective

Single multivariate forensic signature

Stereomicroscopy FTIR ICP-MS

26

Data Pretreatment

Matchstick Multivariate Data Set Incorporating Physical and Chemical Data

Brand B 11

Mg 25

Fe 57

Zn (all)

Zn 70

Binder P/A

NC Binder

Alkyd Binder

Shellac Binder

Head Structure

Paper Splint

REI 0.1 4.0 0.0 1.4 0.2 1 0 1 0 2 0 REI 0.1 3.9 0.0 1.3 0.1 1 0 1 0 2 0 REI 0.1 4.6 0.0 1.3 0.2 1 0 1 0 2 0 REI 0.0 4.2 0.0 1.2 0.1 1 0 1 0 2 0

Matchstick Multivariate Data Set Incorporating Physical and Chemical Data

All Variable Values Standardized by Maximum Value in each Variable

Brand B 11

Mg 25

Fe 57

Zn (all)

Zn 70

Binder P/A

NC Binder

Alkyd Binder

Shellac Binder

Head Structure

Paper Splint

REI 0.1 2.9 0.0 0.5 0.5 100 0 100 0 100 0 REI 0.1 2.8 0.0 0.5 0.5 100 0 100 0 100 0 REI 0.1 3.3 0.0 0.5 0.5 100 0 100 0 100 0 REI 0.1 3.1 0.0 0.4 0.5 100 0 100 0 100 0

Matchstick Multivariate Data Set Incorporating Physical and Chemical Data

All Variable Values Transformed by LN(X+1)

Brand B 11

Mg 25

Fe 57

Zn (all)

Zn 70

Binder P/A

NC Binder

Alkyd Binder

Shellac Binder

Head Structure

Paper Splint

REI 0.1 1.3 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0 REI 0.1 1.3 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0 REI 0.1 1.5 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0 REI 0.1 1.4 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0

27

Gower dissimilarity measure

Dissimilarity Analysis

|𝑦1𝑗 − 𝑦2𝑗 |

(𝑚𝑎𝑥𝑗 −𝑚𝑖𝑛𝑗 )

𝑝

𝑗=1

Assesses both discrete and

continuous variable

contributions to similarity

between samples

𝑤12𝑗 𝑠12𝑗𝑝𝑗=1

𝑤12𝑗𝑝𝑗=1

28

NMDS

Displays sample relationships in multivariate space

Based on calculated dissimilarities

MDS for metric dissimilarity measure

NMDS for non-metric dissimilarity measure

A B

C

D(A, B) ≥ 0

D(A, B) = 0 ⟷ A=B

D(A, B) = D(B, A)

D(B, C) ≥ [D(A, B) + D(C, A)]

29

Forensic Signature

ANOSIM R values

W vs. S 0.609

W vs. SA 0.981

S vs. SA 0.431

30

Forensic Signature

ANOSIM R values

W vs. S 0.609

W vs. SA 0.981

S vs. SA 0.431

ANOSIM R values

R=1 for all

Commercial Brand

pairwise comparisons

31

Forensic Signature

𝑅 =𝑟 𝐵 − 𝑟 𝑊𝑛(𝑛 − 1)/4

As n increases the robustness of R also increases

32

Summary

Combination of analyses excellent for classification and

brand determination

Multivariate statistical analysis confirms hypothesis

Very visual and intuitive final result

33

Discussion

How do we improve Trace Evidence Analysis?

Matchsticks are a good model

Branching out: fibers, paint, glass

Sufficient sample sizes

Automated statistical methods

34

Acknowledgements

Mr. Chad Schennum – Forensic Scientist1

Mr. Tomson Huynh – FTIR Assistant2

Mr. Spencer Nwogoku – Laboratory Assistant2

Dr. Joseph Turner – Director of Instrumentation2

Mr. Thomas Pugh – Introducing Presenter2

1VA Department of Forensic Science

2Virginia Commonwealth University

35

Questions/Comments 36