Evaluating Sources of Variability in Forensic 

Fiber Trace Evidence Examination

Julie L. Bitter, Amanda L. Forster, Jack L. Glover, Sydney C. Brooks, & Ha Kim Ngan Doan

Material Measurement Laboratory, Material Measurement Science Division

National Institute of Standards and Technology, Gaithersburg, MD

Julie.Bitter@nist.gov 301-975-5384
Goals and Objectives

To improve confidence in and reliability of analyses for fiber evidence.

Globally, millions of metric tons of fibers 

are produced every year

Methods of Analysis

 Microscopy

 Stereo

 Polarized Light

 Comparison

 Fluorescence

 Microspectrophotometry 

 Fourier Transform Infrared Spectroscopy

 Scanning Electron Microscopy with Energy Dispersive X-ray

 Pyrolysis Gas Chromatography – Mass Spectrometry

Understand potential sources of variability and make 

recommendations to reduce measurement uncertainty:

 Recommend ways to shift from qualitative observations to 

quantitative measurements.

Identify needs and requirements by:

 Examining techniques, instrumentation, and 

methodology used by forensic science community

Performance Chemicals from BASF
1. Pre-Compression Measurement of Fiber Characteristics

 Embed fibers in polymer matrix

 Hand-section fibers with razor blade

 Mount thin sections and carbon coat

 Collect images using SEM

 Process images with computer algorithm

 Obtain parameters of interest

 Examine variability within & among fibers

2. Post-Compression Analyses of Fiber Thin Films

 Mount fibers in diamond cell

 Compress at different torques

 Measure film with optical profilometry

 Obtain measurements of film thickness

 Examine variability across compressed areas

 Measure FTIR spectrum at same torques

 Determine if patterns emerge

Microtrace Forensic Fiber Reference Collection – https://www.microtracellc.com/service/forensic-fiber-reference-collection/

Experimental Plan: A 2-Pronged Effort

2. Post-Compression Analyses of Fiber Thin Films

 Mount fibers in diamond cell

 Compress at different torques

 Measure film with optical profilometry

 Obtain measurements of film thickness

 Examine variability across compressed areas

 Measure FTIR spectrum at same torques

 Determine if patterns emerge
Pre-Compression: Analysis of Cross-Sectional Images (HFW of images = 144μm)

029_Acrylic 060_Modal

152_Polyester

077_Nylon

126_Olefin
Pre-Compression: Analysis of Cross-Sectional Images

Image analysis via the water shed algorithm:

1. The micrograph can be visually divided into 2 

sections, fiber and background, where the 

bright border acts as a topographic edge. A 

thresholded image is created as a guess for 

the algorithm to use. 
Image analysis via the water shed algorithm:

1. The micrograph can be visually divided into 2 

sections, fiber and background, where the 

bright border acts as a topographic edge. A 

thresholded image is created as a guess for 

the algorithm to use. 

2. The image is then filled in, which falls either 

inside the border (fiber) or outside 

(background), until it fills up to the edge.

Pre-Compression: Analysis of Cross-Sectional Images
Image analysis via the water shed algorithm:

1. The micrograph can be visually divided into 2 

sections, fiber and background, where the 

bright border acts as a topographic edge. A 

thresholded image is created as a guess for 

the algorithm to use. 

2. The image is then filled in, which falls either 

inside the border (fiber) or outside 

(background), until it reaches the edge.

3. The algorithm determines based on this filling 

as to what it labels as the fiber (green) or the 

background (red).

Pre-Compression: Analysis of Cross-Sectional Images
area_px: 183858

perimeter_um: 263.538152585

solidity: 0.983303027062

area_um: 3665.65784352

centroid: (554, 526)

equiv_diam_um: 68.3173515576

Image analysis via the water shed algorithm:

1. The micrograph can be visually divided into 2 

sections, fiber and background, where the 

bright border acts as a topographic edge. A 

thresholded image is created as a guess for 

the algorithm to use. 

2. The image is then filled in, which falls either 

inside the border (fiber) or outside 

(background), until it reaches the edge.

3. The algorithm determines based on this filling 

as to what it labels as the fiber (green) or the 

background (red).

4. Parameters of interest, such as the equivalent 

diameter are computed

Pre-Compression: Analysis of Cross-Sectional Images
Pre-Compression: Analysis of Cross-Sectional Images (HFW of images = 144μm)

029_Acrylic 060_Modal

152_Polyester

077_Nylon

126_Olefin
Pre-Compression: Analysis of Cross-Sectional Images Equivalent Diameter (

m)

10

15

20

25

30

35

40

45

50

Expected Diameter from Calucations

15 Acrylic 18 Acrylic 21 Acrylic 23 Acrylic

015_Acrylic 018_Acrylic

021_Acrylic 023_Acrylic

18μm 35μm

50μm 65μm 11.8μm 18.0μm 29.0μm 44.0μm
Pre-Compression: Centroid to Radii Distance Measurements

Angle (rad)

0 1 2 3 4 5 6

Distance from Centroid (

m)

0

5

10

15

20

23 Acrylic

21 Acrylic

18 Acrylic

15 Acrylic
Angle (rad)

0 1 2 3 4 5 6

Distance from Centroid (

m)

10

15

20

25

30

35

40

45

50

Pre-Compression: Centroid to Radii Distance Measurements
1. Pre-Compression Measurement of Fiber Characteristics

 Embed fibers in polymer matrix

 Hand-section fibers with razor blade

 Mount thin sections and carbon coat

 Collect images using SEM

 Process images with computer algorithm

 Obtain parameters of interest

 Examine variability within & among fibers

2. Post-Compression Analyses of Fiber Thin Films

 Mount fibers in diamond cell

 Compress at different torques

 Measure film with optical profilometry

 Obtain measurements of film thickness

 Examine variability across compressed areas

 Measure FTIR spectrum at same torques

 Determine if patterns emerge

Microtrace Forensic Fiber Reference Collection – https://www.microtracellc.com/service/forensic-fiber-reference-collection/

Experimental Plan: A 2-Pronged Effort
Diamond Compression Cell

Post-Compression: Understanding Effect of Torque

3D Printed Adapter

Adapter used with Torque Wrench Torques measured ranged from 4 to 7 in-lb
Post-Compression: Profilometry of Fiber Films

A profilometer measures the profile of a surface to quantify roughness, or in our case film thickness 

 Measuring the difference of light path between a test surface and a reference surface

 At equal distances from the beam splitter, interference fringes are formed

 Fringes are generated by optical path differences due to height variances on the test surface

Zygo, “Optical Profiler Basics” – http://www.zygo.com/?/met/profilers/opticalprofilersabout.htm
021Acrylic_7B

0 100 200 300 400 500 600

0

100

200

300

400

20 

30 

40 

50 

60 

70 

80 

90 

Post-Compression: Profilometry of Fiber Films

Is the thickness of the fiber film uniform? Does it have variations? What about fiber shape?

20 

30 

40 

50 

60 

70 

80 

90 
021Acrylic_7B

X

0 50 100 150 200 250 300 350 400

Y

150

200

250

300

350

35 

40 

45 

50 

55 

60 

Post-Compression: Profilometry of Fiber Films

35 

40 

45 

50 

55 

60 

Is the thickness of the fiber film uniform? Does it have variations? What about fiber shape?
021Acrylic - Averages

Wavenumber (cm-1)

4000 3500 3000 2500 2000 1500 1000

Absorbance

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Tq4 

Tq5 

Tq6 

Tq7 

Post-Compression: FTIR of Fiber Films 

023Acrylic - Averages

Wavenumber (cm-1)

4000 3500 3000 2500 2000 1500 1000

Absorbance

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Tq4 

Tq5 

Tq6 

Tq7 

41.1μm average diameter 31.4μm average diameter
Post-Compression: FTIR of Fiber Films 

018Acrylic - Averages

Wavenumber (cm-1)

4000 3500 3000 2500 2000 1500 1000

Absorbance

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Tq4 

Tq5 

Tq6 

Tq7 

015Acrylic - Averages

Wavenumber (cm-1)

4000 3500 3000 2500 2000 1500 1000

Absorbance

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Tq4 

Tq5 

Tq6 

Tq7 

22.6μm average diameter 13.1μm average diameter
Where are we now?

Understand potential sources of variability and make 

recommendations to reduce measurement uncertainty:

 Recommend ways to shift from qualitative observations to 

quantitative measurements.

Identify needs and requirements by:

 Examining techniques, instrumentation, and 

methodology used by forensic science community

 Examining the use of 

computer algorithms to aid in 

image analysis

 Measuring fiber film thickness 

with optical profilometry

 

4000 3500 3000 2500 2000 1500 1000

0.0

0.1

0.2

0.3

0.4

0.5

0.6 Tq4 

Tq5 

Tq6 

Tq7 

Current Research State:

 Developed a 3D printed 

adapter for standardizing 

compression using a torque 

wrench

 Exploring the effects of fiber 

film thickness on FTIR 

analyses to make 

recommendation

To improve confidence in and reliability of analyses for fiber evidence.
Future Work and Directions

1. Continue small scale size study

2. Explore the effect of shape on fibers of the same type (acrylic, nylon, etc.)

3. Examine how fiber processing techniques may affect compression and analysis

cabling

twisting

heat setting

dyeing

color fastening

stain protectant
Contact:

Julie Bitter – julie.bitter@nist.gov, 301-975-5384

Amanda Forster – amanda.forster@nist.gov, 301-975-5632

Jack Glover – jack.glover@nist.gov, 301-975-8821

Thanks to:

Eric Steel & Susan Ballou for funding this work

Our undergraduate interns, Sydney Brooks & Ngan Doan

Edwin Chan for use of the profilometer

Thank you for your Attention!