Other Forensic Disciplines
Forensic Disciplines and Lubricant Analysis
Distinguished Speaker Series: Candice Bridge, Ph.D. — Lubricants: Revolutionizing Sexual Assault Investigations (8/26/2025)
Scope: Forensic disciplines with a focus on lubricants in sexual assault investigations; historical context, lab methods, data analytics, and real-world implications; also includes related topics in entomology, forensic social work, and explosives for broader forensic science context.
Sexual Assault Investigations: Key Statistics and Lab Practices
Sexual assaults in America (high-level trend data):
2010: Nearly 1 in 5 (18.3%) women and 1 in 71 (1.4%) men have reportedly experienced a sexual assault in their lifetime.1
2014 (BJS): 33.6% of rapes and sexual assaults were reported (noting most violence is underreported).2
2017 (BJS): 1.4 per 1000 people were raped or sexually assaulted; 28.2% increase from 2016.
Note on DNA: Increased condom use may reduce the likelihood of finding DNA evidence in sexual assault cases.1
Sources: [1] Black et al., CDC/NIPC; 2011. [2] Truman & Langton, BJS; 2015. http://www.bjs.gov/content/pub/pdf/cv14.pdf
2012 NIJ Study (Los Angeles County, SAKs):
10,895 SAKs not submitted; 1,948 SAKs randomly selected.
1,320 (68%): DNA was found.
699 (36%): full profiles → CODIS.
347 (18%): positive CODIS hits; 320 (16%) – “Offender” hit.
90 (5%): Already convicted.
147 (8%): Identified Offender.
70 (4%): Unidentified Offender.
13 (0.6%): Undetermined.
27 (1.4%): Case-to-Case Hits (matches a CODIS profile regardless of case).
20 (1%): Identity known.
40% of cases were solved without DNA.
Key question: What other evidence is probative?
Criminal Justice Outcomes (from NIJ study):
No new arrests after the 371 kits were tested.
Charges filed on one new case; two persons convicted.
A suspect was arrested in nearly 40% of cases without DNA.
Implication: DNA testing is not always necessary for a plea or conviction in sexual assault cases.
Open question: whether uploading unadjudicated profiles into CODIS could help solve future crimes.
Practical/strategic implication: Consideration of additional tools beyond DNA to assist in investigations (e.g., lubricant analysis, other trace evidence).
Evidence Submission and Backlogs
Why are SAKs not submitted?
Not labeled a “backlog” case; backlogs are defined as cases waiting testing in the crime lab or in the lab >30 days.
Survey of 2,000+ law enforcement agencies on submission of forensic evidence.
Unsovled case proportions (examples): 18% of unsolved SA cases; 14% of unsolved homicides; 23% of unsolved property crimes.
Reasons agencies do not submit evidence (percentages):
No suspect identified: 44%
Uncertain of usefulness: 30%
Suspect adjudicated without testing: 24%
Case dismissed: 19%
Prosecutor did not request testing: 15%
Note: The slide shows a mix of rationales; these figures underscore variability in submission decisions.
Sexual Assault Kits: Contents and Time Frames
Sexual Assault Kits (SAKs) contents (typical): paper bags/sheets, clothing, bedding, underwear, comb, documentation forms, evidence collection envelopes, collection instructions, blood collection, nail scraper, cotton swabs (blood, saliva, lubricant, vaginal), urine containers, etc.
Evidence collection time frames by sample type:
Vaginal: Up to
Anal: Up to
Oral: Up to
Bite marks / Saliva on skin: Up to
Source: Forensic best practices cited (French, L.; Forensic Magazine, 2017).
Note: While the specifics include an NIJ/Forensic Magazine reference, the time frames illustrate the urgency of prompt evidence collection in sexual assault cases.
Lubricant Analysis: History, Components, and Methods
Context: Lubricant analysis as a tool in sexual assault investigations; main focus on DNA is complemented by lubricant and related trace evidence.
Common lubricant components (examples):
Tocopheryl Acetate
Nonoxynol-9
Methyl-Polydimethylsiloxane (PDMS)
Hydroxy-PDMS
Polyethylene Glycol (PEG)
Octylamine
GC examples (complex): distinguishing lubricants via gas chromatography patterns; PDMS-containing products versus others.
Historical progression in lubricant analysis:
1981 (Blackledge & Cabiness, US Army Crime Lab, Europe): lubricant evidence in rape/sodomy cases; tested petrolatum-based products for comparison; IR inconclusive; fluorescence similar; GC distinguished differences; same-manufacturer products could have similar GC patterns.
GC examples included Vaseline and Pro-Line Conditioner.
1994–1996 developments:
Condom components identified in SA cases; FTIR traces of PDMS; microscopy showing cornstarch granules, silica, talc; DCI: PDMS and Nonoxynol-9;
Guidelines for trace component analysis from latex condoms (1994);
Viscosity discrimination of lubricants (1995);
FBI Bulletin on lubricant analysis as a factor in investigations (1996).
2001 onward — key researchers and milestones:
Maynard (Australia, 2001) protocol for analyzing condoms and personal lubricants.
Campbell (NZ, 2007) identification of PDMS oligomers.
2012 (Gross, Germany) use of DART to analyze silicones.
2012 (Musah, NY, USA) DART to characterize SA lubricants & N-9.
2013 (Bradshaw, UK) multidisciplinary analysis of condom-contaminated fingerprints.
2013 (Tonkin, Australia) effect of environment on persistence of lubricants on skin.
Overall take: Lubricants have emerged as an analyzable trace with multiple analytical modalities (IR, FTIR, GC-MS, DART-MS, microscopy) that can contribute to linking a suspect, victim, and scene, especially when common DNA evidence is limited.
Analytic Techniques and Instrumentation in Lubricant Analysis
Gas Chromatography–Mass Spectrometry (GC-MS):
Traditional GC-MS analysis used to distinguish lubricant samples and to identify solvent/solubilizing components.
DART-Mass Spectrometry (DART-MS):
Ambient/direct ionization (Penning source).
High resolution and accurate mass capabilities; comprehensive analysis from 40–2000 m/z; rapid analysis (~5–10 seconds) with little sample prep.
Experimental setup (illustrative): mass spectral data in positive-ion mode; parameters such as heater temp ~350°C; MS range 60–1000 amu; typical voltages for ion optics.
Study design: subset of samples representing main lubricant marketing types; analysis included neat liquids, hexane extracts, and extracts at 100–350°C temperatures.
FTIR (Fourier-transform infrared spectroscopy):
Used to identify functional groups (OH, CH, C=O) in neat and extracted residues; peaks noted at ~3351 cm⁻¹ (OH), ~2916 cm⁻¹ (CH sp³), ~2848 cm⁻¹, ~1638 cm⁻¹ (C=C), etc.
LC/GC with targeted fragments for PDMS: analyses focusing on PDMS oligomeric ranges and characteristic fragments.
Polarized Light Microscopy: used to analyze cornstarch or other granular components in lubricants.
Nomenclature snapshot from spectra:
Positive ion species: e.g., Glycerol [M+H]+ at 93.0554 (calc), measured 93.0552.
[2M+H–H2O] at 167.0906 (calc) vs 167.0919 (meas).
Maltol [M+H]+ at 127.0403 (calc) vs 127.0395 (meas).
Negative ion forms: Glycerol [M–H]− at 91.0405 (calc) vs 91.0395 (meas).
Furoic acid [M–H]−, Aconitic acid [M–H]−, Citric acid [M–H]− with corresponding calculated vs measured masses.
Chemometrics (for spectral data):
Reduces subjectivity in spectral inspection (Q vs K comparisons).
Techniques: Classical least squares? (CA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA).
Data preprocessing: mass-calibrated, spectral-averaged, background-subtracted datasets.
Chemometrics and Data Analysis in Lubricant Profiling
Purpose: Extract patterns and relationships not visible via visual inspection; enhance discrimination among lubricant types.
Methods referenced:
CA (Correspondence Analysis) / PCA (Principal Component Analysis) / LDA (Linear Discriminant Analysis).
Data basis: mass spectral data from DART-MS, including neat and extracted residues; external test sets used for model validation.
Principles illustrated by slides:
Cluster analysis and heat maps visualize mass spectral datasets and reveal differences within and between lubricant marketing types.
PCA reveals grouping by marketing type, with multiple groups showing distinct clusters; some overlap occurs depending on formulation (water-based, silicone, condoms).
Example interpretation:
Groupings separated into multiple clusters (e.g., Group 1: glycerol; Group 2: glycerol + lidocaine; Group 3: ethoxydiglycol, triethanolamine, benzocaine; etc.).
External test and real samples yield high classification accuracy, supporting the ability to distinguish lubricants by composition.
Classification outcomes (illustrative numbers):
Silicone-based lubricant model: 97.80% correct classification on the validated dataset; 2 of 62 misclassifications.
Real samples testing: 92.22% correct classification; 7 of 90 misclassifications; 2 samples could be undetermined (e.g., water).
External test set validation (silicone, water, condom): high accuracy with cross-validation; e.g., 93.0557 mass/score threshold; several class thresholds used to separate marketing types.
Practical significance: Multivariate chemometric approaches enable objective categorization of lubricants, aiding investigative leads when traditional DNA evidence is limited.
Cluster Analysis, Heat Maps, and PCA in Lubricant Profiling
Heat maps: graphical representations of mass spectral datasets; reveal patterns and structure; show both inter-type and intra-type variation among lubricants.
PCA (Principal Component Analysis):
Reduces dimensionality to reveal grouping of lubricant samples.
Example groupings in slides show many brands (Adam & Eve, Astroglide, Jo H2O, KY Jelly, Wet, etc.) forming clusters across several PCs.
Demonstrates separation by marketing type and composition (e.g., water-based vs silicone vs condom-associated products; presence of PDMS, anesthetics, flavor/fragrance components).
LDA (Linear Discriminant Analysis):
Used in conjunction with PCA-derived PCs and cluster groupings to classify samples.
External validation and confusion matrices illustrate predictive performance across 12 lubricant groups.
Example: classification results show high accuracy for silicone, water, and condom groups, with some misclassifications in more similar formulations.
Main takeaway: Multivariate statistics (PCA, CA, LDA) applied to mass-spectral data enable robust discrimination of lubricant types and components, improving investigative leads in sexual assault cases.
Forensic Entomology: Principles and Applications
What is forensic entomology?
The study/use of insects and other arthropods that feed on decaying remains to aid legal investigations.
Subfields: medicolegal (criminal) and urban (criminal and civil) entomology; also relevant to stored products and environmental effects.
Primary objective: Estimate the elapsed time since death (ETSD), also called the post-mortem interval (PMI).
Additional applications:
Secondary movement of a corpse (or other evidence).
Post-mortem tampering.
Linking perpetrators to crime scenes via insect evidence.
At the crime scene:
Collection methods include Berlese funnels, leaf litter sampling, soil sampling, and handling of specimens.
Guidelines emphasize careful collection around the body and in the surrounding environment; proper preservatives and transport.
Practical notes:
Insects aid PMI estimation, which can be pivotal in homicide investigations and corroborating timelines.
Forensic Social Work: Roles and Functions
Definition: Forensic social work is the practice of social work that intersects with the law.
Settings: Community-based child and family services, healthcare, education, child welfare, mental health, substance abuse, social services, juvenile justice, civil and criminal justice systems.
Functions of forensic social workers:
Providing consultation, education, or training to criminal/juvenile justice systems, law makers, law enforcement personnel, attorneys, paralegals, and the public.
Diagnosis, treatment, and recommendations for criminal/juvenile justice populations; assessing mental status, child interests, incapacities, or ability to testify; serving as an expert witness.
Screening, evaluating, or treating law enforcement and other criminal justice personnel; policy and program development; mediation and arbitration; teaching, training, supervising; behavioral science research.
Roles/areas of practice:
Child abuse and welfare; mediation (divorce/separation); expert witness (competency determinations and abuse); mitigation; victims of crime investigations and advocacy; work with defendants and offenders; juvenile justice; refugees and immigration.
Explosives: Definitions, Types, History, Collection, and Analysis
Core definitions:
Deflagration: Decomposition at a rate much slower than the sonic velocity; propagation by thermal conduction/radiation; slower than the speed of sound; no atmospheric oxygen required.
Detonation: A chemical reaction with high temperature and pressure gradient (shock wave) causing instantaneous initiation; detonation velocities range roughly from 1,500 to 9,000 m/s.
Types of explosives:
Low explosives: Detonation velocity < 1,000 m/s; examples include black powder (KNO3, charcoal, sulfur); smokeless powders (nitrocellulose; single/double/triple base formulations).
High explosives: Primary (high sensitivity, primers) and secondary (insensitive) explosives; categories include nitroaromatics, nitrate esters, nitramines.
History of explosives: Key milestones
Black powder: Origin in China (10th century); Berthold Schwartz improved BP; mining/blasting era; safety fuses (William Bickford) in 1830s.
Nitroglycerin (NG): Discovered by Ascanio Sobrero (1846); Alfred Nobel developed NG-based dynamite (1867) using diatomaceous earth; detonation vs. blasting applications; nitro headaches and fumes concerns.
Ammonium nitrate: Used for fertilizers, later blasting agents; ANFO developed (1955); shift toward safer handling.
Point-of-use synthesis: Inert or safe compounds mixed at the site to generate explosive compounds; binary systems.
Advantages/Disadvantages of explosives handling:
Advantages: On-site preparation, controlled sizing, reduced storage danger, appropriate detonation when mixed; detonation at very low temperatures.
Disadvantages: Cost, time to mix on-site; handling hazards.
Collection and processing of residue after an explosion:
Debris collection; device reconstruction important; swabs wetted with acetone or water; microscopic examination; aqueous washes for inorganic oxidizers; organic washes for organics; sample filtration and analysis.
Field and laboratory detection methods:
Field: Ion mobility spectrometers, mobile GC-MS (sniffer capabilities), X-ray detection.
Laboratory: GC-MS, GC-Thermal Energy Analysis (TEA), HPLC-MS, IMS.
Notable analytic data:
Vapor pressures and deflagration points for select explosives (examples):
EDGN: vapor pressure at 100°C = 200 Torr; deflagration temperature $T_d$ = 2582°C (illustrative).
NG: vapor pressure at 100°C = 0.39 Torr; $T_d$ = 78.6°C.
TNT: vapor pressure at 100°C = 0.069 Torr; $T_d$ = 360°C.
PETN: vapor pressure at 100°C = 0.0008 Torr; $T_d$ = 26.9°C.
RDX: vapor pressure at 100°C = 0.00016 Torr; $T_d$ = 0.31°C.
GC-MS (NICI) approaches used for explosive analysis.
Historical/modern case examples (terrorist uses of explosives): Beirut 1983 Embassy bombing; 1983 Marine Barracks bombing; Pan Am 103 (Lockerbie) 1988; World Trade Center 1993; Oklahoma City 1995; Jerusalem 1996 Hamas bus attack; Khobar Towers 1996; US Embassy bombings in East Africa 1998; USS Cole 2000; 9/11 attacks 2001; London Tube 2005; Boston Marathon 2013.
Research, Standards, and Forensic Science Landscape
Research in forensic science—definition in slides: systematic investigations and studies to establish new knowledge, create theories/methods, improve existing theories, develop a field, bridge knowledge gaps, apply methods to real problems, etc.
National Institute of Standards and Technology (NIST) and OSAC: organizational structure for forensic science research and standards; institutional landscape includes national labs, universities, industry partners (Pfizer, Johns Hopkins, Google, Tesla, etc.).
OSAC structure and needs:
Subcommittees include Biology, Chemistry (Seized Drugs & Toxicology), Forensic Interest, Forensic Toxicology, Digital Evidence, Firearms & Toolmarks, Footwear & Tire, Forensic Anthropology, Forensic Nursing, Forensic Odontology, Imaging, and more.
Example note: Digital Evidence Subcommittee reported no identified needs at that time.
Research areas and priorities highlighted for technological development:
Development of DNA extraction methods, probabilistic genotyping, SNP panels, software solutions for data analysis, alternative matrices, cannabinoids, trace materials, and more.
Emphasis on data analytics in toxicology, environmental effects on persistence of traces, and advanced analytical technologies for trace evidence.
NIH/Forensic Science Infrastructure:
NIH and national labs (NIST, Los Alamos, Savannah River, Ames) contribute to the broader research ecosystem supporting forensics.
Innocence Project and wrongful convictions (context from slides):
Statistics cited on wrongful convictions and misapplication of forensic science; racial disparities in exonerations; eyewitness misidentification (cross-racial issues); issues of false confessions and coercive interrogations; police/prosecutorial misconduct; inadequate defense.
Emphasis on evaluating forensic science claims and improving standards to reduce wrongful convictions.
Forensic Science in Public Discourse and Education
Slide content includes a visualization of the Innocence Project statistics:
64% of Innocence Project clients exonerated or freed are Black and/or Latinx.
63% of cases involved eyewitness misidentification (including cross-racial misidentification).
53% of cases involved misapplication of forensic science.
Takeaway: Public-facing forensic science must be contextualized with data on errors and biases; ongoing education and standardization are essential to reduce wrongful outcomes.
Mathematical and Statistical Notes (key figures and formulas)
Example masses from DART-MS data (illustrative):
Positive ion glycerol: calc mass ; measured .
Positive ion adduct:
Negative ion glycerol: calc mass
Negative ion furoic acid:
Classification accuracy examples (silicone-based lubricant model):
Overall accuracy: ext{Accuracy} = 0.9780 ext{ (97.80%)}; ext{ misclassifications} = 2/62.
Real samples: ext{Accuracy} = 0.9222 ext{ (92.22%)}; ext{ misclassifications} = 7/90; ext{ undetermined} = 2.
External test set: accuracies show high discrimination among Silicone, Water-based, and Condom groups.
PCA/LDA results (illustrative):
LDA used with the first 9 PCs from PCA; external validation with 30% test split; observed high separation among lubricant groups with overall accuracy around the mid-to-high 90s percentile for key categories.
Pearson correlation (example):
Used to compare low-temperature vs high-temperature DART-MS data; e.g., correlation coefficients indicate better separation at lower temperatures for certain flavors/fragrances.
Note: All these numerical values are as reported in the slides and serve to illustrate the magnitude of discrimination achievable with chemometric approaches.
Summary: Practical Implications and Ethical Considerations
Lubricant analysis expands the forensic toolkit beyond DNA, offering complementary evidence that can link evidence, scenes, and individuals where DNA is absent or inconclusive.
The integration of GC-MS, DART-MS, FTIR, and chemometrics enables objective classification of lubricants and related components, reducing subjectivity in interpretation.
Ethically, forensic science must address risks of misclassification, bias, and over-interpretation; routine reporting should clearly state limitations and confidence levels, especially in cases with high societal impact (sexual assault, wrongful convictions).
The interdisciplinary scope (entomology, social work, explosives) highlights the breadth of forensic science and the importance of cross-disciplinary collaboration for robust investigations and public safety.
Key References and Data Points (select)
Sexual assault prevalence and reporting trends: 2010, 2014, 2017 data; condom use can affect DNA yields.
NIJ 2012 Los Angeles County SAK study: sample sizes and CODIS outcomes; 40% of cases solved without DNA.
Lubricant analysis milestones: IR, GC, GC-MS, FTIR, DART-MS, microscopy, and PDMS-focused work.
DART-MS methodology: positive/negative ion modes; rapid acquisition; minimal prep; broad m/z range.
Chemometrics in forensic analysis: PCA, CA, and LDA usage; mass-calibrated, background-subtracted datasets.
Explosives: history from black powder to ANFO; field and lab detection methods; vapor pressure/deflagration data; NICI-GC-MS.
Forensic science ecosystem: OSAC structure; multidisciplinary research; relevance of accurate interpretation to reduce wrongful convictions.
Public discourse: Innocence Project statistics on misidentification, misapplication, and wrongful convictions.
Appendix: Quick Glossary of Terms
CODIS: Combined DNA Index System.
SAK: Sexual Assault Kit.
PDMS: Poly(dimethylsiloxane), a silicone compound common in lubricants.
DART-MS: Direct Analysis in Real Time Mass Spectrometry.
GC-MS: Gas Chromatography–Mass Spectrometry.
FTIR: Fourier-Transform Infrared Spectroscopy.
PCA: Principal Component Analysis.
LDA: Linear Discriminant Analysis.
CA: Correspondence Analysis.
PMI: Post-Mortem Interval.
ETSD: Estimated Time of Death.
NICI: Negative-Ion Chemical Ionization (a mode used in GC-MS).