OFFICE OF JUSTICE PROGRAMS 

~ BJS N/J OJJ[F OVC SMART 

The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Biometrics from the Isotopic Analysis of 

Amino Acids in Human Hair 

Author(s): Glen P. Jackson 

Document Number: 251376 

Date Received: November 2017 

Award Number: 2013-DN-BX-K007 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
 

 

 

 

 

 

G. P. Jackson, Biometrics from Human Hair 

Executive Summary 

Instrumental methods of analysis can provide elemental, proteomic and isotopic 

information about human hair samples, which, because of their objectivity, scientific foundation, 

and statistical nature, offer many advantages over forensic hair microscopy. The criminal justice 

system could benefit greatly from an objective instrumental method of analysis that could provide 

investigative leads about a suspect from a questioned hair sample. Such investigative leads could 

include a suspect’s age, sex, race, region-of-origin, genetic disorders, and disease state(s) or body 

mass index, among other traits. Here, we used isotope ratio mass spectrometry (IRMS)—which is 

already in use in many government forensic laboratories—to analyze hair samples from a cohort 

of >200 subjects across the US and Jordan. 

We contributed 100 data points to the publication of a new global database of bulk carbon 

and nitrogen isotope ratios in human hair containing more than 3500 analysis results, which can 

be used by future investigators to provide statistical basis for inclusion or exclusion of evidence. 

In separate work, we used LC–IRMS to measure the isotope ratios of 14 amino acids in hair 

proteins independently, and leucine/isoleucine as a co-eluting pair to provide 15 variables for 

classification. Multivariate analysis confirmed that the essential amino acids and non-essential 

amino acids were mostly independent variables in the classification rules, thereby enabling the 

separation of dietary factors of isotope intake from intrinsic or phenotypic factors of isotope 

fractionation. Multivariate analysis revealed at least three potential sources of non-dietary factors 

influencing the carbon isotope ratio values of the amino acids in human hair: body mass index 

(BMI), age and sex. We could also discriminate 20 US subjects from 20 Jordanian subjects with 

75% accuracy (leave-one-out cross validation). These results provide evidence that compoundspecific isotope ratio analysis has the potential to go beyond region-of-origin or geospatial 

movements of individuals—obtainable through bulk isotope measurements—to the provision of 

physical and characteristic traits about the individuals, such as age and BMI. 

By quantifying the amount of amino acids in human hair, we were able to perform 

statistical comparisons between donors with different characteristic traits, including sex, age and 

region of origin. Using leave-one-individual-out cross-validation on more than 80 hair donors, the 

fuzzy rule-building expert system (FuRES) classification rate was 94% for sex, 83% for age group, 

and 61% for the region of origin. In addition, we used the amino acid profiles of scalp hair of 27 

Jordanian subjects (15 diabetes mellitus (DM) type 2 patients and 12 control subjects) to predict 

diabetes from hair analysis. Using a FuRES, we were able to classify the amino acid profiles into 

diabetic and control groups with 100% sensitivity and specificity using leave-one-individual-out 

cross-validation. The areas under the receiver operative characteristics (ROC) curves were 1.0, 

which represents a highly sensitive and specific diabetes test. The associations between the 

abundance of amino acids of human hair and health status may have clinical applications in 

providing diagnostic indicator or predicting other chronic or acute diseases. 

This project has the ability to provide significant advances in the scientific understanding 

of factors that influence the isotopic composition of amino acids in human hair. This basic 

understanding can benefit the criminal justice system by offering a scientific, biochemical basis 

for the comparison of human hairs and for the provision of investigative leads for suspects. 

Although the primary intended benefit of this research project is for the criminal justice system, 

the methods and results of this work has broader impacts that could directly benefit other fields of 

study such as archaeological applications—involving human ancestral habits/traits, ecological 

applications—involving migration and dietary habits, and disease diagnosis and health monitoring. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

iii 
 

 

1.2. 1 

1.2.2 

1.2,3 

2. 1.1 

2. 1.2 

2,2, 1 

2.2.2 

3.1.1 

3.1.2 

3.4. 1 

3.4.2 

4.1.1 

4.1.2 

4.1.3 

6.2. 1 

6.2.2 

G. P. Jackson, Biometrics from Human Hair 

Contents 

1 Introduction ..........................................................................................................1 

1.1 Statement of Problem................................................................................................................... 1 

1.2 Literature Citations and Review................................................................................................. 1 

Bulk IRMS of Human Hair...................................................................................................... 2 

Compound-Specific Isotope Analysis (CSIA)......................................................................... 3 

Human Identification ............................................................................................................... 3 

1.3 Rationale for Research................................................................................................................. 4 

2 Methods ................................................................................................................4 

Recruitment and Selection of Human Subjects....................................................................... 4 

Sample Preparation.................................................................................................................. 4 

2.2 Reagents and Methods ................................................................................................................. 5 

EA-IRMS................................................................................................................................. 5 

LC-IRMS................................................................................................................................. 6 

3 Results ..................................................................................................................7 

3.1 Bulk IRMS: Contribution to a new global database of hair and nail isotope ratios for 

carbon and nitrogen ...............................................................................................................................7 

NIJ-Funded Contribution to Global database .......................................................................... 7 

Spatial distribution of isotope ratios...................................................................................... 11 

3.2 Biometric Traits from the bulk isotope ratio analysis of human hair (Jordanian hair)...... 12 

3.3 Biometric Traits from the compound-specific isotope ratio analysis of human hair 

(Jordanian hair).................................................................................................................................... 14 

3.4 Compound-specific isotope ratio analysis of amino acids in human hair (US hair)............. 25 

Predicting Type II Diabetes from the amino acids in human hair......................................... 29 

Amino acid composition of human scalp hair as a biometric classifier and investigative lead 

30 

4 Conclusions ........................................................................................................31 

Conclusions of global database of bulk hair isotope data...................................................... 31 

Conclusions for CSIA results on Jordanian hair.................................................................... 32 

Conclusions for CSIA results on Jordanian hair.................................................................... 32 

4.2 Implications for policy and practice ......................................................................................... 32 

4.3 Implications for future research ............................................................................................... 32 

5 References ..........................................................................................................33 

6 Dissemination of research findings ....................................................................39 

6.1 Publications................................................................................................................................. 39 

6.2 Presentations...............................................................................................................................42 

Non-conference venues ......................................................................................................... 42 

Conferences ........................................................................................................................... 43 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

iv 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

1 

G. P. Jackson, Biometrics from Human Hair 

Introduction 

1.1 Statement of Problem 

There are a limited number of approaches that currently exist for the forensic examination 

of questioned hair samples and their comparison to known samples. The most common method, 

microscopy, received harsh criticism in the 2009 NAS Report on Strengthening Forensic Science 

in the US, mostly because of its subjectivity but also because there are no widely accepted statistics 

regarding the frequency with which particular characteristics of hair are distributed among the 

population. Nuclear or mitochondrial DNA analysis can provide a high degree of confidence in 

matching questioned hairs with known samples, but forensic DNA analysis is only useful in the 

presence of comparison samples. The criminal justice system could benefit enormously from an 

objective, instrumental method of analysis of human hair that could provide investigative leads 

such as the age, sex, race, dietary habits, region-of-origin, genetic disorders, disease state(s) or 

body mass index of the donor, in the absence of a comparison sample. Such forensic hair analysis 

would be of even more value if the technique in question were already widely validated and used 

in scientific disciplines outside of forensic science, and were commercially available. Such a 

technique does exist and it is compound specific isotope analysis (CSIA). Until now, CSIA has 

not been applied to the determination of biometrics from human hair in a forensic context. 

The purpose of our study is to investigate the potential of compound specific isotope analysis 

of amino acids in human hair for providing a novel forensic tool for biometric information about 

donors. Such a capability could ultimately provide an objective and statistical approach for 

investigative leads. Our goal is to analyze the carbon isotope ratios of more than 14 amino acids 

in the hair samples from approximately 150 human subjects across the US. We will then perform 

multivariate statistical analyses including canonical discriminant analysis (a form of principal 

component analysis) to optimize the classification of individuals into arbitrary classification 

groups. The grouping factors will be determined from biometric information collected from 

detailed questionnaires that each volunteer subject will complete. The ultimate objective is to be 

able to link the amino acid-specific isotope ratios (aka biomarkers) to relevant biochemical 

processes in the body that are intrinsic to each group and therefore provide a scientific, biochemical 

basis for discriminating between groups. Examples of the ability to classify groups according to 

amino acid isotope ratios already exist in clinical studies and include the ability to identify patients 

with liver cirrhosis from the general population. The main discriminating amino acid in this 

previous case was the nitrogen isotope ratio of phenylalanine in the subjects’ hair.[1] An added 

benefit of our proposed research is that our analysis methods and data interpretation methods could 

be applicable to the characterization and comparison of other forms of trace evidence, such as any 

proteinaceous fibers (e.g. silk and wool), or to areas outside of forensic science—such as clinical 

studies and non-invasive long-term health monitoring. Many government agencies around the 

world already have isotope ratio mass spectrometers and would be able to employ our methods, if 

successful. This basic forensic science study has the potential to benefit other areas of forensic 

science and other scientific disciplines. 

1.2 Literature Citations and Review 

Hair is an ideal medium for forensic and clinical analyses and stable isotope ratio analysis 

for several reasons.[2-4] First, hair fibers are a very common but underutilized form of trace 

evidence.[5, 6] Second, in contrast to most other body tissues, hair can be noninvasively sampled 

(by cutting) or minimally invasively sampled (by plucking), making it possible to perform sample 

collection outside of a clinical setting. Third, the protein in hair (keratin) is so stable that it is 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

1 
 

 

 

 

 

 

 

 

 

 

 

 

1.2.1 

G. P. Jackson, Biometrics from Human Hair 

essentially inert after it is synthesized.[7, 8] Fourth, unlike DNA profiles, which are static,[9] each 

hair shaft contains a chronological record of dietary and metabolic activities of the donor, thus 

providing insight into the biochemical processes in the body as a function of time/distance. 

Although this time/distance variability can complicate interpretation, this same variability also 

enables the ability to track the geospatial movements of individuals.[10-14] This investigative lead 

capability has enabled stable isotope analysis to solve several cases involving previously 

unidentified individuals.[3] 

A typical hair shaft mainly consists of the persistent protein structure keratin, which can 

preserve the isotopic information of component amino acids for thousands of years.

[8] It is this 

intrinsic stability that has enabled archaeologists and paleoarcheologists to determine dietary 

trends and changes in our human and prehistoric ancestors.

[15-17]

 Hair from almost any source can 

be used for diet reconstruction. Hair has been shown to be isotopically representative of the body’s 

amino acid pool, and its isotopic composition is found to reflect those of the dietary protein sources

in addition to metabolic and environment influences.[10, 17-23] 

Bulk IRMS of Human Hair 

As early as 1939, Schoenheimer and Rittenberg reported using nitrogen isotope ratios to 

study protein metabolism.[24] They provided the theory of a dynamic equilibrium in proteins and 

lipids that proved using stable isotope ratios as tracers in nutrition and biomedicine.[24, 25] However, 

since this seminal work, only a few isotopic studies at natural stable isotope abundance have 

examined metabolic states and diseases in humans. Fuller et al. were the first to demonstrate that 

the δ15N values in bulk human hair protein can be used to track changes in nitrogen balance during 

pregnancy and morning sickness.[26, 27] There was no significant change in the δ13C results, but all 

the subjects showed a significant changed in the δ15N by approximately 0.5–1‰ during periods of 

weight loss and/or restricted weight gain associated with morning sickness. Stable isotopes have 

been used as a diagnostic technique for tracking eating disorders, disease states, and nitrogen 

balance in archaeological, medical studies, and to understand metabolic pathways in humans. 

In the past 30 years, IRMS has been used quite extensively used in areas such as forensic 

science,[3, 12, 28, 29] food analysis,[30, 31] drugs and pharmaceuticals,[32] geology,[33] biology,[34] 

environmental chemistry,[35] athletics and doping.[36] Most studies have focused on bulk (not 

compound specific) isotope analysis. Recently, multi-collector inductively coupled plasma mass 

spectrometry (MC-ICP-MS) has become a potential alternative method to IRMS for rapid carbon 

and sulfur isotope ratio determinations, but the high background levels for carbon pose a potential 

problem for widespread adoption at the present time.

[37, 38] 

The most common approach for introducing samples into an isotope ratio mass 

spectrometer is via an elemental analyzer (EA). In an EA system, samples are typically combusted 

at around 1050 oC for bulk δ13C, δ15N and δ34S measurements or pyrolized at around 1400 oC for 

the determination of bulk δ2

H and δ18O values. With these two approaches, one can obtain values 

for a maximum of five variables (elements) with which to compare samples, assuming these five 

elements are all present in high enough abundance in the sample. Such bulk isotope ratios have 

been used in multiple forensic cases involving provenancing humans and tracking geographic 

movements.[11, 39-41] Of more interest, and even greater impact, is the ability to look beyond bulk 

analysis to the compound-specific isotope ratios. Assuming each compound is independently 

variable (not necessarily valid), there is a major statistical advantage for measuring as many 

variables/compounds as possible, as is the case with compound-specific isotope ratio analysis, 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

2 
 

 

 

 

 

 

 

 

 

 

 

 

1.2.2 

1.2.3 

G. P. Jackson, Biometrics from Human Hair 

Compound-Specific Isotope Analysis (CSIA) 

In addition to the more common GC methods of CSIA, liquid chromatography (LC)-IRMS 

has been undergoing many recent developments for studying isotope ratios in a variety of 

biological molecules. Unlike GC-IRMS, LC-IRMS typically does not require derivatization and 

best realizes the belief that “the best derivatization for isotopic analysis is no derivatization at 

all”.[34] In 2004, Krummen et al. reported the wet oxidation interface which was ultimately 

commercialized by Thermo (LC IsoLink®, Thermo Electron, Bremen, Germany).[42] Today, there 

are two commercial wet oxidation interfaces enabling the coupling of HPLC with IRMS, the 

Isolink® (Thermo Scientific, Bremen, Germany) and the Liquiface® (Isoprime Ltd., Cheadle 

Hulme, UK).[42, 43] The wet chemical oxidation interface converts organic molecules in aqueous 

solution to CO2 gas using oxidizing reagents such as sodium persulfate (Na2S2O8) and a catalyst 

such as phosphoric acid and silver nitrate.[34] A membrane exchanger separates CO2 gases from 

the other gases (water vapor, oxygen, argon, etc.) that originate from the liquid phase. The CO2 is 

then transferred through a gas permeable membrane[44] into a counterflow of helium. Finally, the 

helium stream enriched in CO2 is partially dried in an online gas drying semi permeable membrane 

(Nafion®) and is then introduced, via an open split, into the IRMS ion source. 

Several groups have reported the precision, accuracy and linearity of LC-IRMS in various 

applications.[45-51] LC-IRMS has provided a novel tool used in wide range of fields including 

chemical archaeology, biochemistry, food adulteration, medicine and forensics.[45-51] The analysis 

of underivatized amino acids using LC-IRMS has been of interest in many areas such as animal 

physiology, diet, metabolism and palaeodietary studies.[21, 52-56] In 2005, Godin and coworkers 

were the first to analyze 15 underivatized amino acids.[57] A strong cation-exchange column was 

used in series with a reversed-phase column, creating a mixed-mode chromatographic (MMC) 

approach, which was applied to the analysis amino acids by McCullagh et al. in 2006.[58] So began 

the development of other chromatographic methods providing increased resolution between the 

amino acids.[34, 51, 59, 60] More recently, Schierbeek, McCullagh and Godin and their coworkers 

have published new methodological approaches for amino acid 13C analysis, [60-62] and Smith and 

co-workers have published a method using a mixed-phase column enabling baseline resolution 

between all amino acids (except leucine and isoleucine).[51] This recent developments in 

chromatography have advanced LC-IRMS to be a strong competitor with GC/C/IRMS both for 

the number of amino acids baseline resolved and for the precision and accuracy of measured δ13C 

values.[43, 63, 64] HPLC run times of several hours can be forgiven because there is no derivatization 

necessary, unlike GC-IRMS. 

Human Identification

As alluded to earlier, the use of stable isotopes as a tool in human identification is an 

important application of IRMS that is rapidly gaining momentum, acceptance and credibility 

within the forensic community.[65] In cases where DNA is not available, isotope ratio analysis can 

provide an alternative perspective. Analysis of hair and nails is often preferable to the analysis of 

bone and teeth because hair and nail samples can be obtained in a non-invasive manner and because 

the IRMS can be applied as easily to the living as to the dead.[39] Ehleringer et al. investigated the 

links between bulk stable isotope ratios of O and H in hair of US residents and geographic 

provenance.[13] Fraser et al. reported using profiling methods for hair and nails using bulk δ13C, 

δ15N, δ2H and δ18O.[39, 66] Fraser et al. conducted studies yielding information about the intra- and 

inter-variability of individuals within a geographical area[39] and have demonstrated that 

information regarding recent geographical whereabouts can be obtained from bulk δ2H isotope 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

3 
 

 

 

 

 

2.1.1 

2.1.2 

2 

G. P. Jackson, Biometrics from Human Hair 

profiling of hair and nail samples.[66] In 2008, Meier-Augenstein and Fraser reported the first use 

of bulk isotope analysis in aiding the identification of an unknown victim.[40] They examined hair 

and nail samples from a dismembered body. Data on bulk δ13C and δ15N isotopic composition of 

human tissue provided information on the person's diet and, hence life style or life circumstance 

and changes therein. Using data obtained from their previous longitudinal study,[39, 66] as well as 

data from the global network for isotopes in precipitation, the authors were able to provide 

sufficient information about the likely region-of-origin of the victim and approximate time period 

of entry into the country. This result provided investigators justification for DNA analysis that, 

remarkably, resulted in ultimate identification of the victim. IRMS has already proved the ability 

to provide investigative leads where all other techniques are ineffective. 

1.3 Rationale for Research 

The ability to use isotope ratio measurements at the amino acid level in human hair to 

classify subject donors into selected groups has been successfully applied to patients with liver 

cirrhosis.[1] Whereas this result is of limited value in a forensic setting, the result indicates that 

biochemical and metabolic processes in organs remote from the hair follicle can result in a 

measureable difference in isotope ratios in the growing hair shaft. We hypothesize that there are 

other hard and soft biometric traits[67] about individuals that influence the isotope ratios of amino 

acids to the extent that they will be observable in the hair shaft regardless of dietary fluctuations. 

Certain biometric traits such as eye color and height would not be expected to influence 

metabolism in such a way, but genetic disorders, metabolic rate, hormone levels, age, sex, and 

weight certainly could. The research plan is designed to explore the possibility of classifying 

individuals based on self-reported traits, and to provide insight into the biochemical factors 

influencing any potential classification. 

Methods 

Recruitment and Selection of Human Subjects 

Hair and nail samples were collected from 94 Jordanian subjects and 97 US subjects, which 

is fewer than the original target value of 150 US subjects. Volunteers from the US were be recruited 

through various conventional and social media venues and include 69 female and 28 male 

volunteers from 22 states. Each participant has completed a thorough questionnaire on a variety of 

topics, including biometrics, nutritional habits, lifestyle, health status, region-of-origin and hair 

treatments. All 94 Jordanian samples were analyzed for bulk IRMS and 26 US subjects were 

analyzed for bulk IRMS. A total of 20 Jordanian and 20 US hair samples were analyzed for 

compound-specific IRMS. 

The collection of hair samples for this study was approved by the Ohio University 

institution review board (IRB# 12X029), King Abdullah University hospital institution review 

board (IRB# 10/215/2444), and WVU review board (IRB# 1312146430). 

Sample Preparation 

All hair samples have been washed and stored according to standard protocols used for 

several years in our laboratory. In short, external contaminants such as lipids and hair treatments 

were removed by vortexing the hair in methanol: acetone: chloroform (1:1:1) for 30 min and then 

sonicating twice (30 min each) in Milli-Q water. The samples were then dried in a vacuum oven 

at 50 °C overnight. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

4 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2.2. 1 

G. P. Jackson, Biometrics from Human Hair 

The hair samples were pulverized by placing an aliquot of the hair into replicate reinforced 

2 mL polypropylene tubes with four 3.2 mm chrome steel beads in an 8-plex-bead beater (Biospec 

Products Inc., Bartlesville, OK) for 5 min at a setting of 3450 rpm. Precisely weighed aliquots of 

each powdered sample was analyzed at the bulk level using the EA-IRMS to determine δ13C, δ15N, 

and δ34S values. Additional precisely weighed aliquots were subjected to acid hydrolysis and 

analyzed at the amino acid level using LC-IRMS for δ13C analysis. 

For hydrolysis, approximately 40 mg of pulverized hair was hydrolyzed in 6 M 

hydrochloric acid for 24 h at 110 °C before evaporating to dryness at 30 °C in a vacuum evaporator 

(Mivac, Genevac, Ipswich, UK). The dry residue was redissolved in 1 mL Milli-Q water and 

filtered using a 0.45 μm syringe filter. The filtrates were stored in a 1.5 mL vial in a freezer until 

required for isotopic analysis. According to a published study,[16] and our own in-house validation, 

protein hydrolysis under such conditions does not significantly affect the δ13C values of the 

recovered amino acids. 

2.2 Reagents and Methods 

The reagents were of analytical grade or higher purity from Sigma-Aldrich (St. Louis, MO, 

USA); sodium persulfate (99%), orthophosphoric acid (>85%), sulfuric acid (>95%), L-amino 

acids (98-99%). Sodium hydroxide (high purity, 50% solution) was purchased from Thermo 

Scientific. Isotope standards for δ13C, δ15N, and δ34S measurements were purchased from the US 

Geological Service (USGS) and the International Atomic Energy Agency (IAEA). Reference gases 

of CO2 (99.999%), N2 (99.997%), SO2 (99.98%), and cylinders of He (99.9999%) and O2 

(99.999%) were purchased from Airgas (Morgantown, WV) and Matheson (Fairmont, WV). 

Organic solvents for the washing steps including methanol, acetone and chloroform were ACS or 

HPLC-MS grade. The acids, including HCl and H2SO4 were ICP-MS grade. The 18 MΩ Milli-Q 

water and HPLC mobile phases were be degassed in an ultrasonic vacuum degasser and sparged 

with an inert gas (UHP He) each morning and during use. Rigorous degassing helps reduce the 

dissolved CO2 in the mobile phases and provides the lowest possible background levels in the 

IRMS. 

EA-IRMS 

Bulk carbon isotope measurements were made on precisely weighed sub-samples of 

approximately 0.5 mg that were placed in tin capsules in a Flash elemental analyzer (EA, Thermo 

Scientific, Waltham, MA, USA). The EA was coupled via a Conflo IV interface (Thermo 

Scientific) to the Delta V Advantage isotope ratio mass spectrometer (Thermo Scientific). Data 

acquisition was carried out using Isodat 3.0 Software (Thermo Scientific). During both the LC and 

EA, analyses used ultra high purity gases described in section 2.2. Guidelines for the selection of 

working standards and a review of strategies to institute universal isotopic referencing procedures 

have been reported by Werner et al.[68] International standards are available through the National 

Institute of Standards and Technology (NIST), and through the International Atomic Energy 

Agency (IAEA) in Vienna, Austria.[28] These standards are Vienna Pee Dee Belemnite (VPDB) 

for carbon, Vienna Canyon Diablo Troilite meteorite (V-CDT) for sulfur and laboratory air for 

nitrogen.[69] Because primary standards are expensive and are becoming environmentally depleted, 

secondary standards are often used in their place, as detailed by Valkiers et al.

[70] 

After manual verification of appropriate peak integration, peak areas were converted to 

isotope ratio values on a per mil (‰) scale according to conventional correction methods in the 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

5 
 

 

 

 

 

 

 

 

 

 

 

2.2.2 

G. P. Jackson, Biometrics from Human Hair 

Isodat software. For example, carbon isotope ratios are reported in per mill (‰) relative to the 

international standard VPDB (Vienna Pee Dee Belemnite) according to the following equation: 

δ (‰)= 1000(Rsample – Rstandard) / Rstandard (1) 

All measurements were measured relative to reference compressed gasses and converted 

to the relevant scales using at least two-point calibration curves. The reference cylinder of CO2 gas 

was calibrated using USGS40 (L-glutamic acid, δ13C = -26.389‰, 95% CI = 0.05‰), USGS41 (Lglutamic acid, δ13C = 37.63‰, 95% CI = 0.05‰), USGS42 (Tibetan human hair, δ13C = -21.09‰, 

95% CI = 0.05‰) and USGS43 (Indian human hair, δ13C = -21.28‰, 95% CI = 0.05‰) (NIST, 

Gaithersburg, MA). The SO2 cylinder was calibrated against silver sulfide isotope standards IAEAS-1 (δ34S = -0.3‰, SD = 0.2‰) and IAEA-S-3 (δ34S = -32.3‰, SD = 0.2‰). The N2 cylinder was 

calibrated against USGS42 (δ15N = 8.05‰, 95% CI = 0.05‰) and USGS41 (δ15N = 47.57‰, 95% 

CI = 0.05‰). Although we had originally planned to run IAEA isotope standard of caffeine 

(IAEA-600, δ13C = -27.771‰) to ensure the relative and absolute accuracy of the two 

measurement systems, this was not conducted because caffeine was found to undergo fractionation 

in the wet chemical oxidation step. 

LC-IRMS 

The LC-IRMS system was a Dionex ICS5000 ion chromatography system (ICS) (Dionex, 

Sunnyvale, CA, USA) coupled through an interface (LC Isolink, Thermo Scientific, Bremen,

Germany) to the IRMS. The LC system was operated under the same conditions as before.

[60, 63] 

Isotope measurements were measured relative to tank CO2, and normalized relative to a two-point 

calbibration curve based on USGS41 and USGS42 (L-glutamic acids, described above in 2.2.1). 

the Underivatized amino acids were separated on a mixed-mode column (Primesep A, 2.1 x 250

mm) and the stationary phase particle size was 5 μm, pore size 100 Å (SIELC Technologies, 

Prospect Heights, IL, USA). The mobile phase was initially pure Milli-Q water followed by a 

gradient to 0.03 M sulfuric acid. The flow rate of the mobile phase was 260 μL/min. The eluent 

from the HPLC column passed into the LC Isolink where all the carbon-containing compounds 

were quantitatively oxidized to carbon dioxide (CO2). Oxidation takes place in the aqueous phase 

at 99.9 °C using sodium peroxodisulphate (200 g/L) and phosphoric acid (1.5 M) as an oxidizer 

and catalyst, respectively. The resulting CO2 was removed quantitatively from the cooled solution 

by a membrane and transferred to the IRMS system via a split flow for analysis. An example of 

6.8 

7.0 

7.2 

7.4 

7.6 

7.8 

8.0 

8.2 

8.4 

8.6 

δ15 N and 

δ34 S (‰) 

N 

S 

-21.4 

-21.2 

-21.0 

-20.8 

-20.6 

δ13 C (‰ vs VPDB) 

0.0 1.0 2.0 3.0 4.0 0.0 2.0 4.0 

Sample quantity (mg) Sample quantitiy (mg) 

Figure 1. Measured delta values for different quantities (0.2–3 mg) of USGS42, Tibetan human hair 

(δ15N = 8.05‰, δ13C = -21.09‰, δ32S = 7.84‰), measured on the EA-IRMS. Error bars show 1 s.d., 

N=5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

6 
 

 

 

 

 

-

3.1.1 

I 

• 

... 

•• 

... 

•• 

3 

G. P. Jackson, Biometrics from Human Hair 

the resulting LC-IRMS chromatogram is shown in Figure 2. All amino acids except Leu/Ile were 

baseline resolved. 

Results 

3.1 Bulk IRMS: Contribution to a new global database of hair and nail isotope ratios for 

carbon and nitrogen 

In the first stages of this work, we contributed our existing data on the bulk isotope ratios of 

Jordanian hair and included the new bulk isotope ratios for the US subjects. Our bulk data was 

combined with existing and other new EA-IRMS data for the US population and then incorporated 

into the global database.[71] This database of approximately 3500 individual samples was a 

collaboration with many different groups and was spearheaded by Dr. Hülsemann in Germany. 

Approximately 58% of the samples in the global database are from Europe and North America 

(Table 1). About one fourth of the samples originate from the USA. In contrast, the amount of data 

for Asia and Africa is considerably less. Approximately 60% of the world population is located in 

Asia, but only 17% of the isotope data are from Asian people. 

Table 1. Continental distribution of available carbon and nitrogen isotope data for hair and nails. 

Table reproduced from reference 71. 

δ13C δ15 Continent Population * N

% n (%) n (%) 

Europe 10 1221 (31) 1200 (30) 

North America 8 1114 (28) 1082 (27) 

South America 6 813 (20) 809 (20) 

Asia 60 686 (17) 728 (18) 

Africa 16 58 (1) 69 (2) 

Australia 1 108 (3) 101 (3) 

total 4000 3989 

* Population percentage calculated from [22] 

Our data contributed to this part of the global database 

NIJ-Funded Contribution to Global database 

Our contribution to the study included the bulk isotope analysis of hair from a cohort of 84 subjects 

from different regions in Jordan. The hair was processed according to the procedure in section 

2.2.1, above. A total of 422 measurements (average of 4.5 measurements per subject) was 

compiled in a variety of ways, as shown in Tables 2, 3, and 4, and the results were consolidated 

with other data by Dr. Hülsemann and our team. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

7 
G. P. Jackson, Biometrics from Human Hair 

Table 2. Bulk IRMS measurements of 97 Jordanian hair samples. (average of 4.5 measurements 

per subject). 

Descriptive Statistics 

N Minimum Maximum Mean Std. 

Deviation 

Statistic Statistic Statistic Statistic Std. Error Statistic 

Delta15N 422 6.96 10.15 8.29 .022 .44 

Delta13C 422 -22.06 -15.72 -19.87 .052 1.06 

Delta34S 422 3.64 12.69 7.63 .064 1.31 

Valid N (listwise) 422 

Table 3. Bulk IRMS measurements of 97 Jordanian hair samples categorized by sex: 19 Males, 

78 measurements (average of 4.1 measurements per subject); 75 females, 344 measurements 

(average of 4.6 measurements per subject). 

Sex Delta15N Delta13C Delta34S 

Male 

Female 

Total 

Mean 

N 

Std. Deviation 

Mean 

N 

Std. Deviation 

Mean 

N 

Std. Deviation 

8.36 

78 

0.39 

8.28 

344 

0.45 

8.29 

422 

0.44 

-19.59781 

78 

0.884107 

-19.93 

344 

1.09 

-19.87 

422 

1.06 

7.22838 

78 

1.291052 

7.72 

344 

1.30 

7.63 

422 

1.3 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

8 
 

 

G. P. Jackson, Biometrics from Human Hair 

Table 4. Bulk IRMS measurements of 97 Jordanian hair samples categorized by region of origin* 

within Jordan: 64 subjects from the Mountain region, 17 subjects from the Valley Region, 8 

subjects from the Desert Region, 5 subjects from unknown region within Jordan. 

Report 

Region Delta15N Delta13C Delta34S 

Mean 

Mountains N 

Std. Deviation 

Mean Jordan N Valley Std. Deviation 

Mean Eastern N Desert Std. Deviation 

Mean 

Total N 

Std. Deviation 

8.27 

302 

0.42 

8.34 

68 

0.61 

8.41 

32 

0.25 

8.29$ 

402 

.45 

-20.13 

302 

1.0 

-19.04 

68 

0.86 

-19.63 

32 

0.71 

-19.91$ 

402 

1.06 

7.82 

302 

1.2 

7.18 

68 

1.46 

6.88 

32 

1.40 

7.64$ 

402 

1.32 

$

this mean may be different from actual total mean provided above because 5 subjects are excluded 

from this total because of their unknown residence within Jordan. 

*see http://www.kinghussein.gov.jo/geo_env1.html for details 

In addition to the large Jordanian data set, we also provided data from the first 6 US subjects to 

the global database. We did not have time to include more data from US subjects before the 

manuscript was submitted for publication. 

Table 4. Bulk IRMS measurements of 6 US hair samples (from Southeastern Ohio). Total of 24 

measurements (4 per person). For this data set, ratios were normalized to international scale with 

2-point calibration curve using USGS 40 & 41. 

Descriptive Statistics 

N Minimum Maximum Mean Std. Deviation 

13C 

15N 

Valid N (listwise) 

24 

24 

24 

-21.864 

10.310 

-17.784 

12.872 

-19.88 

11.25 

1.57 

.52 

The global database is summarized in map-format can be seen in Figures 2 and 3, which 

are taken from the published manuscript. The countries without a colour code (i.e. white) do not 

have any public available information about contemporary human isotope data. Not every country 

shown in colour had a sufficient a number of experimental samples, which is why we created 

country clusters to be able to incorporate such samples into the data set. For Africa, for example, 

so few samples have been analysed that only four-country clusters were created, which stretch 

across huge regions. It is debatable if such large regions are reasonable for creating a 

geographically distinct data set, but these were the smallest geographic areas that could be formed 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

9 
22.0 -21 .7 -21.4 -21.1 -20.8 -20.5 -20.2 -19.9 -19.6 -19.3 -19.0 -18.7 -18.4 -18.1 -17.8 -17.5 -17.2 -16.9 -16.6 -1 6.3 -16.0 

G. P. Jackson, Biometrics from Human Hair 

from the aggregate data to provide a reasonable degree of reliability. A similar situation is present 

for large countries like Russia, the USA, Canada, and China. There are large differences in the 

isotopic data of humans from different locations, and it is reasonable to assume that that there are 

geospatial differences within these countries, too. 

Figure. 2. Global spatial distribution of natural stable carbon isotope ratios of contemporary 

human hair and nails. For countries marked white no data are available. Solid black country 

borders indicate individual countries and country cluster for which isotope data were summarized. 

Figure reproduced from reference 71. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

10 
 

 

::;::::;,, 

' 

8.0 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 10.0 

3.1.2 

G. P. Jackson, Biometrics from Human Hair 

Figure 3. Global spatial distribution of natural stable nitrogen isotope ratios of contemporary 

human hair and nails. For countries marked white no data are available. Solid black country 

borders indicate individual countries and country cluster for which isotope data were summarized. 

Figure reproduced from reference 71. 

For human identification or other purposes, human hair and nail samples are used for stable 

isotope analysis among other matrices like teeth or bone. In order to compare δ13C and δ15N 

analytical results for different matrices, isotopic differences between these matrices have to be 

taken into account. Our results confirm the fact that δ13C values of human nail are lower compared 

to hair and nail δ15N values are higher compared to hair.[19,20] The differences in isotope ratios 

between hair and nail are attributed to differing amino acid compositions as well as to potentially 

different amino acid turnover in hair follicle and the nail matrix, although the exact mechanisms 

remain unclear.[19] Because most of the published carbon and nitrogen isotope data on 

contemporary ('alive') humans are from the analysis of hair, the data set of this study was 

referenced to human hair isotope ratios. Any nail isotope data was corrected by constant factors 

(described in the manuscript) for inclusion. If this data set should be used for the interpretation of 

δ13C and δ15N values of other human matrices like bone, blood cells, etc. different correction 

factors would have to be applied. 

Spatial distribution of isotope ratios 

It has been reported for smaller data sets that modern human carbon isotope ratios change 

significantly with latitude,[17] which is related to the decrease in C4 vegetation with increasing 

latitude.[48] This can be observed for our compiled dataset, wherein the δ13C country mean values 

typically decrease (i.e. more negative) nearer the poles (Fig. 2). Clearly, latitude alone is not the 

only variable for explaining the trend in modern human δ13C values. Regions like the USA or 

Southern Africa show larger (less negative) δ13C values than other countries on the same latitude. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

11 
 

 

 

 

 

G. P. Jackson, Biometrics from Human Hair 

The highest δ13C values are found for regions like Southern Africa and Brazil, which are 

influenced by the dominant use of C4 plants as dietary source for humans and animals. Besides the 

influence of dietary C4 plants, the consumption of maritime food leads to increased δ13C values in 

modern humans.[6,14,48] This has been demonstrated by the analysis of fingernails and hair of 

indigenous populations in Northern Greenland and Southwest Alaska consuming high amounts of 

maritime food.[14,49] However, such high amounts of maritime food in the modern human diet are 

exceptional and the observed spatial distribution of human δ13C values in this data set can primarily 

attributed to the proportion of C3 and C4 plants in the human diet. 

Several factors have been identified to influence human δ15N values: the amount of marine 

food, terrestrial meat, and animal products in the diet,[6,9-14,24] physiological situations[26,27] and 

disease, illness or malnutrition.[29,50,51] As the δ15N value of human hair might be influenced by 

such a multitude of factors, generalised assumptions explaining the spatial distribution of human 

nitrogen isotope ratios are difficult. There is no correlation between latitude and country δ15N mean 

values; enriched δ15N values are found near the equator as well as in higher latitudes. Small 

amounts of fish and meat and also the consumption of pulses might be the cause for the 15N 

depleted hair samples from the Indian subcontinent, including Iran and Pakistan. However this is 

not the case for other countries, like Africa, with typically scant meat and fish consumption but

15N enriched values. Additional factors known to influence human dietary δ15N values are climate 

and the use of agricultural fertilizers. 

Temperature and precipitation also have an influence on plant δ15N values, with enriched 

δ15N values for plants in hot and arid climates and depleted δ15N values for cold and humid 

regions.[52] Fertilization is another factor with a huge influence on plant δ15N values: fertilization 

with animal derived manure leads to highly enriched δ15N values for plants and crops, whereas the 

use of synthetic fertilizer leads to depleted δ15N values of the agricultural product.[53,54] As a 

conclusion, human hair δ15N values seem to be a result of different influences: dietary preferences 

and food supply, physiology and metabolism, climate and fertilization methods. 

3.2 Biometric Traits from the bulk isotope ratio analysis of human hair (Jordanian hair) 

For a subset of the Jordanian hairs samples, we had extensive questionnaire data that enabled 

us to investigate the ability to classify the hair donors into groups according to biometric traits. 

The results of this work have appeared in Science and Justice.

[72] A summary of those findings is 

provided, below. A summary of the subject volunteers is provided in Table 4. 

Table 4. Summary of characteristics and isotope ratio data for bulk hair analysis of the eighty-four 

subjects from the Jordanian database and a subset of 20 female subjects used for the LC-IRMS 

experiments. N=5 for each subject in the subset; N=4 or 5 for the remaining subjects. Body Mass 

Index (BMI) is from self-reported data, which approximately 60% of 84 subjects provided. 

Age BMI δ15Na δ13Cb δ34Sc 

84 subjects 

(65 female, 

14 female) 

Mean 27 25.2 8.29 -19.87 7.63 

Min. 1 14.7 6.96 -22.06 3.64 

Max. 77 40.0 10.14 -15.72 12.69 

Std. Dev. 17 5.2 0.44 1.06 1.31 

Subset of Mean 34.1 24.8 8.04 -21.00 8.23 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

12 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

G. P. Jackson, Biometrics from Human Hair 

20 female Min. 17 14.7 6.96 -22.06 7.04 

subjects Max. 50 40.0 8.66 -20.28 10.09 

Std. Dev. 9.2 6.5 0.37 0.43 0.65 a

Versus air N2; bVersus VPDB; cVersus VCDT 

Tables 5 and 6 provide summary bulk isotope data and MANOVA results, respectively, 

using body mass index as the fixed factor. The results show that when the hair donors are 

categorized into 4 broad BMI groups, the within group variance for ∂13C and ∂15N are significantly 

smaller than the between-group variance, which indicates that bulk isotopes may offer some degree 

of classification. ∂34S ratios were not significantly different between different BMI groups. Table 

7 shows the results of the attempt to classify the subjects into BMI groups, but the classification 

rate was only successful 47% (biased prediction) or 44% (unbiased, leave-one-out crossvalidation) of the time. 

Table 5. δ13C, δ15N and δ34S isotope ratio values of bulk hair samples from twenty female subjects 

in the Jordanian database using EA-IRMS (n=5 for each subject, so N=25 implies 5 subjects). The 

inclusion of replicate measurements for each individual does not constitute true independent 

measures of the variables with respect to each grouping factor, but captures the variance within 

the individuals. 

BMI N Mean Std. 

Dev. 

∂15N 

<= 20 25 8.023 0.24 

20-23 30 8.090 0.30 

23-30 20 8.273 0.35 

30+ 25 7.833 0.47 

Total 100 8.045 0.37 

∂13C 

<= 20 25 -20.821 0.36 

20-23 30 -20.951 0.37 

23-30 20 -21.279 0.27 

30+ 25 -20.998 0.55 

Total 100 -20.996 0.43 

∂34S 

<= 20 25 8.174 0.96 

20-23 30 8.280 0.50 

23-30 20 8.076 0.43 

30+ 25 8.370 0.60 

Total 100 8.235 0.65 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

13 
 

 

 

G. P. Jackson, Biometrics from Human Hair 

Table 6. MANOVA results of the same data in Table 5. 

Sum of 

Squares df Mean Square F Sig. 

∂15N * BMI Between Groups 2.2 3 0.74 6.18 .001 

Within Groups 11.5 96 0.12 

Total 13.8 99 

∂13C * BMI Between Groups 2.42 3 0.80 4.91 .003 

Within Groups 15.7 96 0.16 

Total 18.2 99 

∂34S * BMI Between Groups 1.1 3 0.37 0.86 .463 

Within Groups 41.2 96 0.43 

Total 42.3 99 

Table 7. Results of discriminant analysis (classification) of subjects according to BMI groups for 

twenty female subjects from the Jordanian database. The inclusion of replicate measurements for 

each individual does not constitute true independent measures of the variables with respect to each 

grouping factor, but does capture the measurement variance.a,b 

BMI (Binned) Predicted Group Membership 

<=20 20-23 23-30 >30 Total 

Original Count <= 20.00 12 2 4 7 25 

20.01 - 23.00 5 11 8 6 30 

23.01 - 30.00 0 1 15 4 20 

30.01+ 8 8 0 9 25 

% <= 20.00 48.0 8.0 16.0 28.0 100.0 

20.01 - 23.00 16.7 36.7 26.7 20.0 100.0 

23.01 - 30.00 0.0 5.0 75.0 20.0 100.0 

30.01+ 32.0 32.0 0.0 36.0 100.0 

Crossvalidatedc Count <= 20.00 11 3 4 7 25 

20.01 - 23.00 5 10 8 7 30 

23.01 - 30.00 0 2 14 4 20 

30.01+ 8 8 0 9 25 

% <= 20.00 44.0 12.0 16.0 28.0 100.0 

20.01 - 23.00 16.7 33.3 26.7 23.3 100.0 

23.01 - 30.00 0.0 10.0 70.0 20.0 100.0 

30.01+ 32.0 32.0 0.0 36.0 100.0 

a. 47.0% of original grouped cases correctly classified. 

b. 44.0% of cross-validated grouped cases correctly classified. 

c. Cross validation is done only for those cases in the analysis. In cross validation, each case is 

classified by the functions derived from all cases other than that case. 

Our publication in Science and Justice highlights the limitations of bulk IRMS 

measurements, which is why we originally proposed to investigate amino-acid isotope ratio 

measurements, as described below. 

3.3 Biometric Traits from the compound-specific isotope ratio analysis of human hair 

(Jordanian hair) 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

14 
 

 

 

 

 

•« • •s •.a 12000 

10000 

> .s 8000 

!' 

• . C 

6000 l: 

4000 

2000 

1000 2000 3000 4000 5000 6000 7000 8000 9000 

Time(s] 

. 44 • • 5 . 46 12000 

10000 

> .s 8000 

~ 

I 

la 6000 

4000 

2000 

1000 2000 3000 400 0 5000 6000 7000 8000 .... Time [s) 

G. P. Jackson, Biometrics from Human Hair 

Figure 4A shows the different isotopes of CO2 measured as a function of time during an LC 

separation when a standard mixture of amino acids was separated using our optimized method. 

Ref Figure 4 CO2 B is a chromatogram of a human hair hydrolysate using the same conditions as LC as the 

standard amino acid mixture in 4A. Although Leu/Ile cannot be baseline resolved in either 

chromatogram, the isotope values derived from the co-eluting Leu/Ile peaks was usually included 

in the data analyses because it still represents an independent measurement, or variable, for each 

person. Methionine (Met) gave the smallest absolute ion signals for the hair hydrolysates. This is 

most likely due to: 1) the fact that of the seventeen amino acids studied, it is the least abundant in 

hair, and 2) oxidative losses to sulphone or sulphoxide. The small Met peak for the hair 

hydrolysates rendered the amino acid unsuitable for inclusion at this time. 

Asp 

Glu 

Ser 

Gly Ala 

Pro 

Val 

Met 

Cys 

Ile/Leu 

Tyr 

Lys

His 

Phe 

Arg 

Thr 

A 

Asp 

Glu 

Ser Gly 

Pro 

Thr 

Val Cys Ile/Leu 

Tyr Lys Phe 

Arg 

Ala 

B 

His 

Figure 4. Example of ion chromatograms of 16 amino acids acquired on the LC-IRMS system 

using a mixed-phase Primesep A column and no organic modifiers: A) Standard free amino acids; 

B) From a hair hydrolysate. Only Leu and Ile were not baseline resolved. Although Met was 

resolved, it was of too low abundance in hair hydrolysates to be useful. 

Exploratory data analysis of the amino-acid specific isotope ratios was performed using 

principal component analysis (PCA) and MANOVA. Details are provided in Table 8 and Figure 

5. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

15 
 

G. P. Jackson, Biometrics from Human Hair 

Table 8. One-way ANOVA results of four different groupings of people using body mass index 

(BMI) as the fixed factor. All twenty subjects were females from Jordan and were grouped the 

same as the Tables S.6, S.7, and S.8. N=3 for each subject. 

F Sig. 

Asp 2.22 9.5 x 10-2 

Glu 3.00 3.8 x 10-2 

Ser 0.58 6.3 x 10-1 

Thr 14.51 4.1 x 10-7 

Gly 6.18 1.0 x 10-3 

Ala 8.27 1.2 x 10-4 

Pro 9.32 4.3 x 10-5 

Val 2.79 4.9 x 10-2 

Cys 17.13 5.2 x 10-8 

Leu/Ile 5.21 3.0 x 10-3 

Tyr 4.62 5.9 x 10-3 

Lys 3.80 1.5 x 10-2 

His 5.85 1.5 x 10-3 

Phe 3.03 3.7 x 10-2 

Arg 1.72 1.7 x 10-2 

PCA was performed using the unweighted, mean-centered isotope ratio values for each 

subject in two different ways. In one approach, each of the three instrument replicates for each 

person was included in the data analysis (N=60 for 20 subjects). Mathematically speaking, this 

approach incorrectly assumes that each replicate measurement of a person’s hair is an independent 

measurement of a given grouping factor of that person. However, this approach captures more of 

the variance expected within each grouping factor because it captures more of the measurement 

variance for each person. We also performed the calculations using averaged-within-person data 

for each person to see if using the mean values for each person assisted in clustering and 

classification (N=20 for 20 subjects). Grouping factors in the present study include age, BMI, meat 

intake and dairy intake. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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16 
 

 

B person 

-: 1010 2. 2. 10 12 

~ 1013 

0 -G- 10 15 

1016 

1. 1. -e- 10 17 

-e- 101& 

0 1020 

~ ~ 0 -e- 2001 

"' v o -G- 20os ..i ..i 0 -G- 2006 N 8--o N 0 0 2012 r{ r{ 0 0 -e- 20 13 

~ ~ 2017 

N N :g:- 2025 u u 2027 II. - 1. II. - 1. 0 -G- 2031 

2039 

20 52 

-G- 2061 

- 2. - 2. 

~ G 0 

0 

- 3. - 3. - 3.0 - 2.0 - 1.0 .o 1.0 2.0 3.0 -3.0 - 2.0 - 1.0 .0 1.0 2.0 3.0 

PCl (7.6, 50.4%) PCI (8.1, 54,2%) 

G. P. Jackson, Biometrics from Human Hair 

Figure 5. Principal component analysis (PCA) plots for twenty female subjects using isotope ratios 

of sixteen peaks (representing seventeen amino acids) as input data: A) used triplicate 

measurements for each person; B) used averaged-within-person data. The absolute and percent 

variance explained by the first two components with Eigenvalues >1 are shown in the axis titles. 

The results of PCA analysis of seventeen amino acids (in sixteen peaks) are shown in Figure 

5. The data are grouped according to subject ID and help show the within-person variance and 

between-person variance. The first two principal components in each analysis (not averaged and 

averaged-within-person) have Eigenvalues greater than one and together explain between 72 or 

76% of the total variance, respectively. The Varimax rotated component coefficients shown in 

Tables 9 and 10 show that in both cases (averaged-within-person and not-averaged-within-person), 

the amino acids clustering close to y-axis (PC2) were mainly the essential amino acids Tyr, Lys, 

Phe, His and Leu/Ile, whereas the amino acids clustering close to x-axis (PC1) were mainly the 

non-essential amino acids Asp, Glu, Gly, Cys, Ser and Ala. Although tyrosine is not strictly an 

essential amino acid, its carbon skeleton is derived directly from the essential amino acid Phe, so 

is included here. The phenotypic non-essential amino acids (PC1) therefore explained more of the 

variance between subjects than the diet-derived essential amino acids (PC2). 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

17 
 

 

G. P. Jackson, Biometrics from Human Hair 

Table 9. Rotated Component 

Matrix for not-averaged data.

a 

(N=60 for 20 persons) 

Component 

1 2 

Asp .865 

Glu .846 

Gly .829 

Ser .797 

b

Thr .755 

Cys .682 .450 

Ala .675 

b

Val .672 

Pro .597 

b

Lys .960 

b

Tyr .945 

b

Phe .942 

b

His .903 

Arg .770 

b

Leu/Ile .419 .761 

Extraction Method: Principal 

Component Analysis. 

Rotation Method: Varimax 

with Kaiser Normalization. 

Only values > 0.4 are shown. 

a. Rotation converged in 3 

iterations. 

b. Essential amino acids 

In short, with the exception of three amino acids (Arg, Thr and Val), the isotope ratios of the 

amino acids naturally converged into two orthogonal (i.e. unrelated) components described 

respectively by the essential and non-essential amino acids. In general, dispensable (non-essential) 

amino acid carbon skeleton also ultimately comes from the diet with alanine reflecting dietary 

carbohydrates and glutamate and aspartate reflecting dietary energy. However, the orthogonal 

components here indicate that, regarding the ability to separate individuals, the isotope ratios of 

the non-essential amino acids are unrelated to the isotope ratios of the essential amino acids. This 

natural convergence may provide some assurance as to the validity of the present results, and to 

the fact that, broadly speaking; external factors and internal factors of isotope fractionation are 

independent, at least in this data set of twenty female subjects. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

18 
 

 

 

3.3.1 

G. P. Jackson, Biometrics from Human Hair 

Table 10. Rotated 

Component Matrix for 

averaged-within-person 

data.

a (N=20 for 20 persons) 

Component 

1 2 

Gly .905 

Asp .890 

Glu .830 

Thr .815 

Val .762 

Cys .730 .439 

Ala .684 

b

Tyr .972 

b

Lys .954 

b

His .938 

b

Phe .923 

b

Leu/Ile .496 .754 

Extraction Method: Principal 

Component Analysis. 

Rotation Method: Varimax 

with Kaiser Normalization. 

Only values > 0.4 are shown. 

a. 

b. 

Rotation converged in 3 

iterations. 

Essential amino acids 

Classification by BMI group 

After grouping the subjects into approximately equal-sized ordinal scale of BMI, MANOVA 

was performed using BMI as the fixed factor. Thr and Cys gave the largest F values (p= 4x10-7 

and 5x10-8, respectively), suggesting that they may be the most useful in terms of classifying 

according to BMI. However, post-hoc comparisons using Tukey’s honestly significant difference 

(HSD) showed that although the δ13C values of Thr showed very significant differences between 

the obese group (BMI > 30) and all the other groups, the δ13C values for Thr were not significantly 

different between the underweight, normal or overweight groups. Tukey’s HSD provides for better 

control of Type 1 errors than a simple T-test, so is less likely to find significant differences between 

sample means when the means are not significantly different. Therefore, Thr is only effective in 

identifying the obese subjects. The mean δ13C value for Thr of the BMI > 30 group (-7.16 ±0.66‰) 

was different from BMI below 20 group (δ13C = -8.40 ±0.86‰), BMI 20-23.99 group (δ13C = -

8.52 ±0.34‰) and BMI 24-30 group (δ13C = -8.67 ±0.90‰) at the 99.9% CI (p = 6.10x10-5; 

4.41x10-6; 4.36x10-6 individually). According to post-hoc Tukey’s HSD tests, no single amino acid 

provided significant differences between more than two groups of BMI, so multivariate approaches 

were necessary. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

19 
 

 

 

 

0 

oci 

m 

.,,--

LI\ 

cl ON 

C 

.5! 

u 

~ -3 ... 

- 6 

A 

- 6 

~ 30.00+ 

24.001.2

-

9.99 ~ c2QOO 

20.00 - 23.99 

- 3 0 6 

Function 1 (J.40, SO.ll'.!6) 

BMI (group) 

8 < 20.00 

20.00 - 23.99 

24.00 - 29. 99 

0 30.00+ 

• Croup Centroid 

- < 20.00 

- 20.00 - 23.99 

24.00 - 29.99 

- 30.00+ 

N 

e 

.~ - 2 .., 

" ::, 

... - 5 

- 8 

B 

- 30 

l 30.00+ 

le 20.00 

f--20.00 - 23.99 

j 24.00 - 29.99 

- 20 - 10 0 10 20 

Function 1 (3611, 97.1'6) 

30 

BMI (group) 

8 < 20.00 

20.00 - 23.99 

0 24.00 - 29. 99 

0 30.00+ 

• Group Centroid 

- < 20.00 

- 20.00 - 23.99 

- 24.00 - 29.99 

- 30.00+ 

G. P. Jackson, Biometrics from Human Hair 

Discriminant analysis was applied to classify the twenty female subjects according to the 

BMI groups. The results are shown in Table 11 and graphically in Figure 6 for both the notaveraged-within-person and averaged-within-person data. According to the classification results, 

93% of the not-averaged data points can be classified to the correct BMI group on the basis of the 

original discriminant rules, and 77% of the data points can be correctly classified using leave-oneout cross-validation. In leave-one-out cross-validation, each case (person or replicate from a 

person) is classified by the functions derived from all the cases other than that case. The result of 

the classification is repeated for each case to provide a more rigorous measure of the classification 

ability. 

Figure 6. Canonical discriminant analysis (CDA) plots for twenty female subjects using isotope 

ratios of sixteen peaks (representing seventeen amino acids) as input data and BMI group as the 

classification factor: A) triplicate measurements for each person; B) used averaged-within-person 

data. The absolute and percent variance explained by the first two functions with Eigenvalues >1 

are shown in the axis titles. 

Classification for the averaged-within-person data was quite different than the not-averagedwithin-person data because the number of data points is considerably different. The subjects cluster 

very tightly and most of the variance (93%) is explained in one function. The very large Eigenvalue 

indicates that the group means are very significantly different in the averaged-within-person data. 

Classification success in this case was 100%, based on the original functions, but only 80% using 

leave-one-out cross-validation. The function coefficients for each amino acid can be retroactively 

investigated to link the classification ability to certain key amino acids or sources, but we have not 

found any obvious trends between the amino acids and their function coefficients at this time. 

Amino acid specific δ13C values were determined using the proposed LC-IRMS method 

for triplicate measurements of 20 female test subjects from Jordan (summary results are shown in 

the supporting data section). One-way ANOVA was performed for each amino acid to assess the 

within-group variance to between-group variance with body mass index (BMI) as the fixed factor 

(group). Cysteine and threonine had the largest F values and are the most discriminating amino 

acids, and all the amino acids except for serine provided significantly different F values. Post-hoc 

pair-wise comparisons using Tukey’s HSD showed that the δ13C values for threonine in subjects 

with BMI>30 were always significantly different (less negative) than subjects from the other 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

20 
 

 

 

3.3.2 

G. P. Jackson, Biometrics from Human Hair 

groups. The mean δ13C value for the BMI>30 group was -7.16±0.66‰, whereas the means for the 

other groups were δ13C(BMI<20) = -8.40±0.86‰, δ13C(20<BMI<24) = -8.52±0.34‰, δ13C(24<BMI<30) = -

8.67±0.90‰. These results show that for this small test group, we can measure significant 

differences in the δ13C values of amino acids between severely obese subjects and the general 

population. 

Table 11. Classification results for CDA of twenty female subjects into four broad body mass 

index (BMI) groups. The original data is the average of three replicate measurements and 

comprises sixteen isotope ratio values of seventeen amino acids and three replicates from each 

individual.a,b 

BMI (group) Predicted Group Membership 

< 20.00 20.00 - 23.99 24.00 - 29.99 30.00+ Total 

Original Count < 20.00 

20.00 - 23.99 

24.00 - 29.99 

30.00+ 

% < 20.00 

5 

0 

0 

0 

0 

6 

0 

0 

0 

0 

4 

0 

0 

0 

0 

5 

5 

6 

4 

5 

100.0 .0 .0 .0 100.0 

20.00 - 23.99 .0 100.0 .0 .0 100.0 

24.00 - 29.99 .0 .0 100.0 .0 100.0 

30.00+ .0 .0 .0 100.0 100.0 

Cross- Count < 20.00 

validatedc 20.00 - 23.99 

24.00 - 29.99 

30.00+ 

% < 20.00 

3 

1 

0 

0 

2 

5 

0 

0 

0 

0 

3 

0 

0 

0 

1 

5 

5 

6 

4 

5 

60.0 40.0 .0 .0 100.0 

20.00 - 23.99 16.7 83.3 .0 .0 100.0 

24.00 - 29.99 .0 .0 75.0 25.0 100.0 

30.00+ .0 .0 .0 100.0 100.0 

a. 100.0% of original grouped cases correctly classified. 

b. 80.0% of cross-validated grouped cases correctly classified. 

c. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified 

by the functions derived from all cases other than that case. 

Classification by age group 

A second discriminant analysis was applied to classify the subjects according to arbitrary 

age groups: < 25, 25-45 and 45+ years old. The results are plotted in Figure 7 and also provided 

in the supplemental material (Tables 12 and 13). Using the un-averaged data, 96.7% of the cases 

were correctly classified based on the original functions and yet only 10% were correctly classified 

in the leave-one-out cross-validation. The averaged-within-person results were similar, at 100% 

and 25%, respectively. The source of the poor validation success is likely due to the small 

Eigenvalues associated with the discriminant functions and the weaker significant difference 

between the group means. At this stage, it is difficult to tell if classification success would improve 

with a larger cohort, because of the improved sampling and lack of stochastic effects, or if 

classification would be worse because the captured variance may reveal that the small difference 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

21 
 

~ 2.5 

.; 

"' ..-f 

!:::!. 0.0- N 

C 

.5? 

ti 

C 

~ - 2.S 

- 5.0-

A 

- S.0 

Age (Binned) 

0 <- 25 

0 26 - 44 0 45 + a Group Centroid 

- <= 25 

- 26 - 44 

- 45+ 

- 2.5 0.0 2.5 s.o 

Function 1 (3.4, 61.5%) 

B 5.0-

g 2.5 t+ .; 

"' 

N 

!, 0.0- N 

C 

.5? 

ti 

C 

~ - 2.5 ... 

-5.0-

- S.0 - 2.5 0.0 2.5 

Function 1 (7.S, 63.9%) 

s.o 

Age (Binned) 

0 <- 25 

0 26 - 44 0 45 + 

• Group Centroid 

- <=2 5 

- 26 - 44 

- 45 + 

G. P. Jackson, Biometrics from Human Hair 

between the means is insignificant. A larger sample size of ~30 measurements per group (N~90) 

would resolve this question. 

Figure 7. Canonical discriminant analysis (CDA) plots for twenty female subjects using isotope 

ratios of sixteen peaks (representing seventeen amino acids) as input data and age group as the 

classification factor: A) triplicate measurements for each person; B) used averaged-within-person 

data. The absolute and percent variance explained by the first two functions with Eigenvalues >1 

are shown in the axis titles. 

Table 12. Classification results for CDA of twenty female subjects into three broad age groups 

groups. The original data includes three replicate measurements for each subject and comprises 

sixteen isotope ratio values of seventeen amino acids and three replicates from each individual.a,b 

Age (Binned) Predicted Group Membership Total 

<= 25 26 - 44 45+ 

Count 

Original 

% 

Count 

Crossvalidatedc 

% 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

15 

2 

0 

100.0 

5.6 

.0 

0 

1 

0 

.0 

2.8 

.0 

0 

34 

0 

.0 

94.4 

.0 

1 

6 

9 

6.7 

16.7 

100.0 

0 

0 

9 

.0 

.0 

100.0 

14 

29 

0 

93.3 

80.6 

.0 

15 

36 

9 

100.0 

100.0 

100.0 

15 

36 

9 

100.0 

100.0 

100.0 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

22 
 

 

 

 

 

3.3.3 

G. P. Jackson, Biometrics from Human Hair 

a. 96.7% of original grouped cases correctly classified. 

b. 10.0% of cross-validated grouped cases correctly classified. 

c. Cross validation is done only for those cases in the analysis. In cross validation, 

each case is classified by the functions derived from all cases other than that 

case. 

Table 13. Classification results for CDA of twenty female subjects into three broad age groups 

groups. The original data includes the average of three replicate measurements for each subject 

and comprises sixteen isotope ratio values of seventeen amino acids.a,b 

Age (Binned) Predicted Group Membership Total 

<= 25 26 - 44 45+ 

Original 

Cross-validatedc 

Count 

% 

Count 

% 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

<= 25 

26 - 44 

45+ 

5 

0 

0 

100.0 

.0 

.0 

1 

0 

1 

20.0 

.0 

33.3 

0 

12 

0 

.0 

100.0 

.0 

1 

2 

0 

20.0 

16.7 

.0 

0 

0 

3 

.0 

.0 

100.0 

3 

10 

2 

60.0 

83.3 

66.7 

5 

12 

3 

100.0 

100.0 

100.0 

5 

12 

3 

100.0 

100.0 

100.0 

a. 100.0% of original grouped cases correctly classified. 

b. 25.0% of cross-validated grouped cases correctly classified. 

c. Cross validation is done only for those cases in the analysis. In cross validation, each case 

is classified by the functions derived from all cases other than that case. 

Classification by diet 

Figure 8 includes an example of CDA using meat intake as a fixed factor. Using the 

averaged-within-person data, Eigenvalues are relatively large (~12) for function 1, enabling 100% 

classification from the original functions. However, leave-one-out cross-validation was only 40% 

successful, which is not significantly better than random probability. Using the non-averaged data 

provided leave-one-out cross-validation success rate of 85%, which is considerably better. Because 

the original analysis of variance showed that most of the variance between the subjects can be 

explained by the non-essential amino acids, which fractionate in a manner that is independent of 

dietary source, one would expect that phenotypic factors would actually have better classification 

success than diet, assuming that one could identify the most influential phenotypic factors by 

which to classify. These questions are the basis of our ongoing work. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

23 
 

N 

C 

.~ 

.., 

C 

:::, 

10-

s 

.._ - S 

- 10-

- 10 

1- 2 / day 

• 

- S 0 s 

Function 1 (12.6, 80%) 

10 

Meat intake (grouped) 

0 > 2 / day 

0 1- 2 / day 0 rarely/never 

• Group Centroid 

- > 2 / day - 1- 2 / day 

- rarely/never 

Classification Results a.-c 

Predicted Group Membership 

Meat larounedl > 2 /day 1-2 /day rarely/ never Total 

Original Count > 2 /day 4 0 0 

1- 2 /day 0 1 0 

rarely/ never 0 0 1S 

% > 2 /day 100.0 .0 .0 

1- 2 /day .0 100.0 .0 

rarely/ never .0 .0 100.0 

Cross-validated 0 Count > 2 /day 2 0 2 

1-2 /day 0 0 1 

rarely/ never 4 5 6 

% > 2 /day 50.0 .0 50.0 

1- 2 /day .0 .0 100.0 

rarely/ never 26.7 33.3 40.0 

a. 100.0% of original grouped cases correctly classified. 

b. Cross validation is done only for those cases in the analysis. In cross validation, each 

case is classified by the functions derived from all cases other than that case. 

c. 40.0% of cross-validated grouped cases correctly classified. 

4 

1 

15 

100.0 

100.0 

100.0 

4 

1 

15 

100.0 

100.0 

100.0 

G. P. Jackson, Biometrics from Human Hair 

Figure 8. Classification results for CDA of twenty female subjects into groups according to meat 

consumption The original data includes the average of three replicate measurements for each 

subject and comprises sixteen isotope ratio values of seventeen amino acids.b,c 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

24 
 

 

Canonical Discriminant Functions 

3 

2 

~ 

"' ai 1 .... 

~ .... 0 

I ~ N 

C: 

.g 

u - 1 C: 

::, 

... -2 

-3 

-5 -3 -1 1 3 

Function 1 (5.5, 75.3%) 

Classification Results'·' 

I IBMI Predicted Group Membership 

I lgroup -<20 20-25 25-30 0 30 Total 

Originel Comt <20 4 0 0 0 4 

20-25 0 3 0 0 3 

25-30 0 0 8 0 8 

>30 0 0 0 4 4 

% <20 1000 0 .0 .0 1(0 .0 

20-25 0 100 0 .0 .0 100.0 

25-3(1 0 0 ' 00 0 0 100.0 

:---:m .U .U .U 100.U 1ll 1.U 

Cross:- Com! <20 0 1 2 4 

val dated• 20-25 0 2 1 0 3 

25-30 0 2 5 1 8 

>30 2 0 2 0 4 

% <70 75 0 0 75 0 50 0 100 0 

70-75 0 flil7 :l.1 ~ 0 100 0 

7:1-Xt 0 ?:iO fi? !"1 ~7 !"1 1ri10 

>30 50.0 0 50.0 .0 100.0 

a. 100.0% <I ong.nal grouped cases correctly classh ,d. 

b. O as,; vahd:t1on 1s done only tor those cases ,n the onaty,is_ In cross vclidct,on, 

eadl case i; cl:tssrfied by !he functions derf.i-ed from FIi case; other tli2n that case. 

c 42.1 % of cross-validaied grouped cases correctly c~ssifie:J. 

G. P. Jackson, Biometrics from Human Hair 

3.4 Compound-specific isotope ratio analysis of amino acids in human hair (US hair) 

We have so far only been able to acquire and work-up the compound-specific IRMS data on 

the first 20 US subjects. These included 10 male subjects and 10 female subjects. We have 

performed a variety of multivariate statistics to evaluate and interpret the results, and we have 

compared the 20 US subjects to our original database of 20 Jordanian subjects. However, the 

preliminary results are very promising. 

Initially, we ran one-way ANOVA followed by Tukey’s HSD for each classification factor 

to reveal which amino acids are most significant for each factor. We then applied linear 

discriminant analysis (LDA) as a classification tool. Based on original rules (a biased approach), 

US subjects were correctly classified with probabilities of 100% (BMI, sex and age) using all the 

measured amino acids. However, based on an unbiased (leave-one-out cross-validation) approach, 

classification results were significantly reduced, as one might expect for a relatively small number 

of samples per class. See Figure 9 for details. 

Figure 9. Discriminant analysis results for classifying US subjects into groups according to selfreported body mass indexes (BMI) using the δ13C values of eight amino acids, ASP, GLU, THR, 

ALA, VAL, HIS, PHE, and ARG. 

Using ANOVA to reject the least discriminating amino acids, leave-one-out cross 

validation (unbiased) and original rules (biased) both determine sex group with 90% accuracy 

(Figure 10). In a similar approach, USA hair could be discriminated from Jordanian hair with more 

than 75% certainty using leave-one-out cross-validation (Figure 11). These results demonstrate 

that compound-specific isotope ratio analysis combined with multivariate statistics provides a 

potential forensic tool to provide soft biometric traits and investigative leads beyond diet. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

25 
 

 

4,0 

~ 2.0 ~ 

"' ; 

C .0 

,2 

~ ,2 

-2.0 

-4.0 

4.0 

~ 

~ 2.0 

.0 

C 

~ -2.0 

,l: 

•' 

E¥ 

Sex 

Country 

Classificalion Resultsa.c 

Predicted Group Membership 

Sex l l Total 

Original Count l 9 l 10 

2 l 9 10 

" l 90.0 10.0 100.0 

2 10.0 90.0 100.0 

Cross-validated" Count l 9 l 10 

2 l 9 10 

" l 90.0 10.0 100.0 

2 10.0 90.0 100.0 

a. 90.0\11, of original grouped cases correctly classified. 

b. Cross validation Is done only for those cases In the analysis. In cross 

validation, each case is classified by the functions derived from all cases 

other than that case. 

c. 90.0\11, of cross-validated grouped cases correctly classified. 

4.0 

.., 

C 

2.0 &. 

; 

C. 

.o a· 

;j' 

~-

:!. 

-2.0 § 

. 

Original Count 

% 

Cross-\'alidated' Count 

% 

Cl.1~~ifir.irinn RPiuh~a,c 

Pr ediuetl Group Mernber:,hip 

Country I 2 

1 17 3 

2 1 19 

Ungrouped cases 0 1 

1 85.0 lS.O 

2 5.0 9!.0 

Ungrouped cases .0 100.0 

l 13 7 

2 2 18 

l 6S.O JS .O 

2 10.0 90.0 

Total 

20 

20 

1 

100.0 

100.0 

100.0 

20 

2 0 

100.0 

100.0 

-4.0 .......,--~-~----+-~-~----~~ -4.0 a. YU.U)'6 of original grouped cases correctly class1f1ed. 

b. Cross salidation is done only for those cases in rhe analysis. In cross validation, each 

u:,1: i!> llc:1:,:,ified by the runuion:, Ut:riw:tl fr um all l a~e:, other than that u:,e. 12 10 8 6 4 2 0 2 4 6 8 10 12 

Fr~qu~ncy Frecauency c. 77.5% of cross-·,alidated grouped cases correctly classified. 

G. P. Jackson, Biometrics from Human Hair 

Figure 10. Discriminant analysis results for classifying US subjects into groups by sex using the 

δ13C values of eight amino acids, ASP, GLU, VAL, ILE/LEU, TYR, HIS, PHE, and ARG. 

Figure 11. Discriminant analysis results for classifying subjects into country of origin (US or 

Jordan) using the δ13C values of nine amino acids, ASP, SER, THR, ALA, ILE/LEU, TYR, LYS, 

HIS, and ARG. 

Our larger US study will require the use of single factor ANOVA to assess significant 

differences for a single factor (amino acid), multiple factor ANOVA to assess interactions 

(covariance) between factors (groups, according to questionnaire responses) and multivariate 

ANOVA (MANOVA) to search for interactions between different response variables (amino 

acids). In addition to exploratory data analysis using ANOVA, we will also use discriminant 

analysis (DA) to help classify the subjects into prior groups. Unlike principal component analysis, 

which maximizes the total variance between all points, discriminant analysis maximizes the ratio 

of the within-group variance to between group variance for prior groups. Linear discriminant 

analysis (LDA) works well for classifying two groups at a time, but canonical discriminant analysis 

(CDA) is most efficient for multiple groups. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

26 
 

Classification Resuns•,0 

Predicted Group Membership 

Sex 1 2 Total 

Original Count 1 10 0 10 

2 0 9 9 

% 1 100.0 .0 100.0 

2 .0 100.0 100.0 

Cross-validated" Count 1 9 1 10 

2 1 8 9 

% 1 90.0 10.0 100.0 

2 11 .1 88.9 100,0 

a. 100.0% or original grouped cases correctly classified. 

b. Cross validation is done only for those cases in the analysis. In cross 

validation, each case Is classified by the functions derived from all cases other 

than that case. 

c. 89.5% of cross-validated grouped cases correctly classified. 

Classification Results'·< 

Prediaed Group Membership 

Countr( I 2 Total 

Original Count I 19 l 20 

2 0 20 20 

Ungrouped cases 0 l l 

% I 95,0 5,0 100.0 

2 ,0 100,0 100.0 

Ungrouped cases ,0 100,0 100.0 

Cross-validared' Count I 11 9 20 

2 l 19 20 

% I 55,0 45,0 100.0 

2 5,0 95,0 100.0 

a. 97.5% oforiginal grouped cases correctly classified. 

b. Cross validation is done only for rhose cases in th? analysis. In cross \'alidation, each 

case is classified by the functions derived from all cases other rhan that case. 

c. 75.0% of cross-\'alidated groupe:J cases correctly classified. 

G. P. Jackson, Biometrics from Human Hair 

Table 14 shows that the sex of a person can be predicted with better than 89% accuracy (using 

unbiased, leave-one-out cross-validation) using the δ13C values of the amino acids Asp, Glu, Ser, 

Thr, Gly, Val, Ile-Leu, Lys, His, Phe, Arg. 

Table 14. CDA of not-averaged δ13C values from USA hair samples after CSIA. Sample size 

needs to be increased for a more successful classification based on sex. Sex: 1 = male, 2 = female. 

AAs = Asp, Glu, Ser, Thr, Gly, Val, Ile-Leu, Lys, His, Phe, Arg. 

Table 15 shows that the country of origin (USA or Jordan) of a person can be predicted with 

approximately 5% accuracy (using unbiased, leave-one-out cross-validation) using the δ13C values 

of the amino acids Asp, Glu, Ser, Thr, Gly, Val, Ile-Leu, Lys, His, Phe, Arg. The database 

comprised of 40 individuals, 20 from each country. 

Table 15. CDA of not-averaged δ13C values from USA hair samples after CSIA. Sample size needs 

to be increased for a more successful classification based on country of origin. Country:1= USA, 

2= Jordan. AAs = Asp, Glu, Ser, Thr, Gly, Ala, Val, Ile-Leu, Tyr, Lys, His, Phe, Arg. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

27 
 

 

AGE - Canonical Discriminant Functions 

4. 

-2. 

-•. 

-6.S -4 .0 -u 1.0 J.s .. Function 1 (10.6, 84.2%} 

Age group 

§

~,s 

, .... 

45+ 

• Group Controld 

- <=25 - 26-44 

- •S+ 

Classification Results"' 

PrPrtirtFrl r.rnun MPmhPrli-hin 

AQc group <=7'i 76-44 4<;+ Total 

Original c,unt <-;s 6 0 0 6 

26-H 0 10 0 10 

4S+ 0 0 3 3 

?6 "'=~S 100.0 .0 .0 100.0 

26-H .0 100.0 .0 100.0 

4S+ .0 .0 100.0 100.0 

Lro!s-val1dated" Lount <=, ~ ;j l L b 

26-H 2 7 1 10 

45+ 2 0 1 3 

% <=;5 50.0 16. 7 33.3 100.0 

26-H 20.0 70.0 10.0 100.0 

45+ 66. 7 .0 33.3 100.0 

a. 100.0% of original grouped ca;es correctlv classified. 

b. Cross vali.:lation is done only for those cases in the analysis. In cross ·,alidatior,, 

each case is classified bv the functions :le rived from all uses other thar, that case. 

c. 57 .9% of cross-validated grouped case; correctly das;ified. 

G. P. Jackson, Biometrics from Human Hair 

Figure 12 shows the ability to predict age group of individuals from the US. Leave-one-out crossvalidation success is not as reliable in this case (58% accuracy), unlike the biased or original rules, 

which have 100% accuracy. 

Figure 12. CDA of not-averaged δ13C values from USA hair samples after CSIA. Sample size 

needs to be increased for a more successful classification based on age. Age group: <=25 (blue), 

26-44 (green), 45+ (red). AAs = Asp, Glu, Ser, Thr, Gly, Ala, Val, Met-Cyt, Ile-Leu, Lys, His, 

Arg. 

Figure 13 shows the ability to predict physical activity level of a subject from the δ13C values of 

the amino acids Glu, Ser, Thr, Gly, Ala, Val, Met-Cyt, Ile-Leu, Lys, His and Arg. Similar to age 

prediction, the leave-one-out cross validation prediction rates are worse then the prediction rates 

using all the data, which indicates that at least twice as many samples are needed in the database. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

28 
 

 

PHYSICAL ACTIVITY - Canonical Discrim inant Functions 

~-------------------Hour,.lweekdofng 

C 

i 

I&. • l , 

- 3. 

-2.S 0 .0 1.S 

Function 1 (S.78. 77.1%) 

3.4.1 

, .. 

exercise 

l

;:j~ 3 • 2-4h 

4 • 0-2h 

• Group Centroid 

- 1 • >6h 

- 2 • Uh 

- 3 • 2-4h 

- 4• 0-2h 

c1u,r1eat1on RHulti~.c 

Prediad Gro.1p ~enibershio 

Ext:rthe I . ; • Total 

Original Count 1 I o 0 ) 1 

2 0 4 0 ) 4 

3 0 o 7 ) 7 

4 0 n 1 5 7 

)t 1 10).0 .0 .0 .) 100.0 

2 .0 100.0 .0 .) 100.0 

; .u .u 100.U .~ lUO.l• 

4 .0 .0 14.3 &S.7 100.0 

Cru»-·1.:t!i:Jalel' CllVnt 1 0 o 0 I 1 

2 0 3 0 1 4 

3 4 1 l I 7 

4 ; 1 I , i 

1⁄4 1 .0 .0 .0 100.) 100.0 

2 .0 1:.0 .0 2S.) 100.0 

3 S7.1 H.3 !U 14.3 100.0 

4 n9 14.3 !U 28.5 100.0 

a. 94. 7'N. of original grouped cases correctly classified. 

b. Cross validation is done only for Ihose ca!e! in the analysis. In cross 'validation. each case is classified b·1 the functions defr1d from all cases orher thin that case. 

c. 31 .6% of cross-valldued groupaj .:a;e; correaly classltled. 

G. P. Jackson, Biometrics from Human Hair 

Figure 13. CDA of not-averaged δ13C values from USA hair samples after CSIA. Sample size 

needs to be increased for a more successful classification based on weekly exercise routine. AAs 

= Glu, Ser, Thr, Gly, Ala, Val, Met-Cyt, Ile-Leu, Lys, His, Arg. 

Predicting Type II Diabetes from the amino acids in human hair 

As described in our publication,

[73] we applied a slightly different, and complementary approach 

to amino acid analysis of human hair, which was based on the relative quantity of each amino 

acid,[74] instead of the isotope ratios. The method used herein for the amino acid determination in 

hair included keratin protein acid hydrolysis using 6 M hydrochloric acid (HCl), followed by 

amino acids derivatization using N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA), and the 

determination of derivatized amino acids by gas chromatography/mass spectrometry (GC/MS). 

Amino acid profiles of scalp hair of 27 Jordanian subjects (15 diabetes mellitus (DM) type 2 

patients and 12 control subjects) were analyzed. A fuzzy rule-building expert system (FuRES) 

classified the amino acid profiles into diabetic and control groups based on multivariate analyses 

of the abundance of 14 amino acids, as shown in Figure 14. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

29 
 

 

 

 

 

0.3 

0.2 

0.1 

~ 0 C: 

2 -0.1 C: 

-

w -0.2 

> 

~ -0.3 

ai 

a:: -0.4 

-0.5 

-0.6 

3.4.2 

G. P. Jackson, Biometrics from Human Hair 

Figure 14. FuRES average variable loadings of the 14 amino acids of diabetes classification with 

95% confidence intervals. Negative loadings are for diabetic subjects. 

The sensitivity and specificity were 100% for diabetes detection using leave-one-individual-out 

cross-validation. The areas under the receiver operative characteristics (ROC) curves were 1.0, 

which represents a highly sensitive and specific diabetes test. The nonessential amino acids Gly 

and Glu, and the essential amino acid Ile were more abundant in the scalp hair of diabetic patients 

compared to the hair of control subjects. The associations between the abundance of amino acids 

of human hair and health status may have clinical applications in providing diagnostic indicator or 

predicting other chronic or acute diseases. 

Amino acid composition of human scalp hair as a biometric classifier and investigative 

lead 

In this sub-project, we analyzed the amino acid composition of scalp hair of 64 Jordanian subjects 

(33 males and 31 females) with ages ranging from 1 and 77 years.

[75] Statistical comparisons 

between classification groups were based on the abundance of 14 abundant and acid-resistant 

amino acids,

[74] and included classification of hairs with a fuzzy rule building system (FuRES). 

Using leave-one-individual-out cross validation, the FuRES classification rate was 94% for sex, 

83% for age group, and 61% for the region of origin, as summarized in Figure 15. For predicting 

sex from amino acid concentration in hair, the essential amino acids Phe and Thr gave the most 

significant differences with respect to their F statistic (i.e., ratio of between-group to within-group 

variation), so they are the most discriminating for sex. Based on the same hair analyses, the nonessential amino acids Gly and Ala provide the largest loading scores classifying the subjects into 

two arbitrary age groups, <44 and >44 yrs. For region of origin classification, the amino acids Cys 

and Tyr had the highest loading scores in the classification rules and were therefore most 

discriminating. The techniques developed through this paper could complement the current 

methods of hair analysis, which include physical examinations and genomic or mitochondrial 

DNA analysis. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

30 
QUANT AMINO ACIDS 

7 11 

5 

13 

3 

2 14 

I 

5 6 7 9 10 12 13 

Retention time (min) 

HAIR CLASSIFICATION 

SEX 194% accurate I 

AGE 183% accurate I 

REGION 161% accurate I 

4 .1.1 

4 

G. P. Jackson, Biometrics from Human Hair 

Figure 15. Schematic showing how FuRES classification tree was used to predict certain biometric 

traits from the abundance of 14 amino acids in human hair. The success rates are for leave-oneout cross-validation. 

Conclusions 

Conclusions of global database of bulk hair isotope data 

In this work, new experimental δ13C and δ15N data of more than 400 hair and nail samples 

of contemporary humans living all over the world were summarised and added to the present public 

accessible data set. We contributed more than 100 of these new data points. However, there is still 

a lack of data on modern humans for huge regions, especially Africa. Our data on human δ13C 

values support the established interpretation that the global spatial distribution of human carbon 

isotopic composition is strongly related to the amount of C4 plant in the diet. The influence of a 

marine carbon isotope signal to modern human isotope ratios seems to be lower. The global spatial 

distribution of human δ15N values cannot be directly related to dietary or environmental factors. 

Nevertheless, the analysis of carbon—and to lesser extent nitrogen—isotope ratios of post-mortem 

human remains may help in geo-location and subsequent identification of human victims. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

31 
 

 

4. 1.2 

4.1.3 

G. P. Jackson, Biometrics from Human Hair 

Conclusions for CSIA results on Jordanian hair 

This study has compared the bulk isotope ratios of hair with compound specific isotope ratio 

values of amino acids from human hair hydrolysates. We were able to measure the carbon isotope 

ratios of 15 of the amino acids in hair independently, and Leu/Ile as a co-eluting pair. Data analysis 

confirmed that, regarding the differences between individuals; most of the essential amino acids 

and non-essential amino acids were independently variable, thereby enabling the separation of 

dietary factors from intrinsic or phenotypic factors within the subjects. Multivariate analysis 

revealed at least two potential sources of non-dietary factors influencing the carbon isotope ratio 

values of the amino acids in human hair: BMI and age. We are currently in the process of increasing 

the number of participants and investigating other potential sources of intrinsic or phenotypic 

variance, such as sex, exercise habits, metabolic disorders and disease-state. We welcome 

communication from anyone with a reference collection of hair samples who might be interested 

in this approach. 

Conclusions for CSIA results on Jordanian hair 

At present, the CSIA of US hair has supported the earlier findings using Jordanian hair. That 

is, that metabolic factors provide a sufficiently large and consistent fractionation effect on certain 

amino acids to enable subjects to be classified into different soft biometric groups. In addition, we 

can distinguish between subjects from the US and Jordan using the CSIA of amino acids in the 

hair. We are still in the process of examining more hair samples and examining the canonical 

discriminant function coefficients to determine the amino acids that most influence the ability to 

classify the subjects according to the different grouping variables. We have shown the ability 1) 

to collect reproducible and reliable amino acid-specific δ

13C values from hair, 2) to perform 

statistical comparisons and data reduction to classify individuals into biometric groups, and 3) to 

extract specific amino acid biochemistry that could point to biochemical reasons for the 

classification. However, to make the results more useful to the US criminal justice system, we 

need to continue to analyze the existing hair samples from our larger cohort of US subjects. 

4.2 Implications for policy and practice 

At this time, we do not expect these fundamental research findings to affect public policy or 

routine casework in crime laboratories. However, we have made significant strides towards the 

development of novel investigative leads that law enforcement could use in the future to help solve 

crimes. More importantly, we are building upon a body of work that is enabling objective chemical 

measurements of human hair that can provide statistical models for the probability of inclusion of 

exclusion. Such objective and metric-based analyses are the future of forensic science. Of course, 

we have a long way to go, but we have made a small step in the direction of statistical-based 

determinations of hair comparisons. 

4.3 Implications for future research 

The implications here are more obvious. In addition to working up the remaining data for 

the US subjects, we must establish more rigorous multivariate approaches to data analysis. We 

need to better understand the biochemical process affecting fractionation, and better understand 

how to compensate for different factors, when we know they are present. In future work, the 

carbon-based CSIA of this work could be combined with other stable isotope data, such as oxygen, 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

32 
 

 

 

5 

G. P. Jackson, Biometrics from Human Hair 

hydrogen, nitrogen and strontium, to provide even more independent variables into objective hair 

comparisons. 

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scalp hair as a biometric classifier and investigative lead. Anal. Methods 2015, 7, 1707. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

38 
 

 

 

 

 

 

 

 

 

6 

G. P. Jackson, Biometrics from Human Hair 

Dissemination of research findings 

6.1 Publications 

Since commencing this grant, we have published the following 7 peer-reviewed articles 

related to this project: 

1) A. H. B. Rashaid, G. P. Jackson, Peter B. Harrington, “Quantitation of Amino Acids in Human Hair

by Trimethylsilyl Derivatization Gas Chromatography/Mass Spectrometry” Enliven: Bio Anal. 

Techniques, 2014, 1(1) 1-12. (http://enlivenarchive.org/bioanalytical-002.html) 

Abstract: The distribution of amino acids in hair can divulge information regarding the health 

(e.g., diabetes) and provide a means for detecting the history of the disease by segmentation of the 

hair as well as attributes of an individual (e.g., sex and age). Therefore, an nonenzymatic method 

of hair digestion and profiling is required. In addition to optimizing and validating a method for 

measuring the distribution of amino acids in human hair, a robust and comprehensive approach to 

objectively compare the most effective means of extracting and manipulating chromatographic 

data to obtain the best limits of detection, linearity, and sensitivity are provided. Data comparisons 

were made by operating the mass spectrometer in a mode that rapidly switches between total ion 

current (TIC) and selected ion monitoring (SIM) modes during each sample injection. In this way, 

any external confounding factors were negated that may otherwise influence the comparison of the 

linearity and sensitivity between the two modes of operation. The use of SIM, peak areas, and an 

internal standard provided significantly better sensitivity and limits of detection than using peak 

heights, TICs, or no internal standard. The sample preparation steps included protein acid 

hydrolysis using hydrochloric acid and trimethylsilyl (TMS) derivatization using N,Obis(trimethylsilyl)trifluoroacetamide (BSTFA). The optimal derivatization conditions were 

acetonitrile as reaction solvent, temperature of 100°C, and a reaction time of 30 min. 

The method was validated by measuring the amino acid content of myoglobin. This 

validation was accurate for nine of the fourteen amino acids found in myoglobin and gave detection 

limits in the range of 0.04–0.1 μmol/L, quantitation limits in the range of 0.1–0.5 μmol/L,

recoveries between 80% and 110%, and linear models with coefficients of determination (R2) 

greater than 0.99 in the tested range from 1 to 300 μmol/L. The remaining five amino acids of

myoglobin were deleteriously affected by acid 

2) A. H. B. Rashaid, P. B. Harrington and G. P. Jackson “Amino Acid Composition of Human 

Scalp Hair as a Biometric Classifier and Investigative Lead” Anal. Methods, 2014, 7, 1707-1718. 

doi:10.1039/C4AY02588A 

Abstract: Hair shaft analysis is becoming increasingly important in several applications of 

forensic science. Keratin is the key component of human scalp hair and is composed of all 21 

known amino acids, albeit in very different proportions. The method for the amino acid 

determination included protein acid hydrolysis followed by trimethylsilyl (TMS) derivatization of 

the amino acids, and the subsequent quantitation using gas chromatography/mass spectrometry 

(GC/MS). The amino acid composition of scalp hair of 64 Jordanian subjects (33 males and 31 

females) with ages ranging from 1 to 77 years have been analyzed. Statistical comparisons between 

classification groups were based on the abundance of 14 abundant and acid-resistant amino acids, 

and included classification of hairs with a fuzzy rule building system (FuRES). Using leave-oneThis document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

39 
 

 

 

 

 

 

 

G. P. Jackson, Biometrics from Human Hair 

individual-out cross-validation, the FuRES classification rate was 94% for sex, 83% for age group, 

and 61% for the region of origin. For predicting sex from amino acid concentration in hair, the 

essential amino acids Phe and Thr gave the most significant differences with respect to 

their F statistic (i.e., ratio of between-group to within-group variation), so they are the most 

discriminating for sex. Based on the same hair analyses, the non-essential amino acids Gly and 

Ala provide the largest loading scores classifying the subjects into two arbitrary age groups, <49 

and >49 years. For region of origin classification, the amino acids Cys and Tyr had the highest 

loading scores in the classification rules and were therefore most discriminating. The techniques 

developed through this paper could complement the current methods of hair analysis, which 

include physical examinations and genomic or mitochondrial DNA analysis. 

3) G. P. Jackson, Y. An. K. I. Konstantynova, A. H. B. Rashaid “Biometrics from the Carbon 

Isotope Ratio Analysis of Amino Acids in Human Hair” Sci. Justice (FIRMS 2013 Special 

Edition), 2015, 55, 43-50. doi:10.1016/j.scijus.2014.07.002 

Abstract: This study compares and contrasts the ability to classify individuals into different 

grouping factors through either bulk isotope ratio analysis or amino-acid-specific isotope ratio 

analysis of human hair. Using LC–IRMS, we measured the isotope ratios of 14 amino acids in hair 

proteins independently, and leucine/isoleucine as a co-eluting pair, to provide 15 variables for 

classification. Multivariate analysis confirmed that the essential amino acids and non-essential 

amino acids were mostly independent variables in the classification rules, thereby enabling the 

separation of dietary factors of isotope intake from intrinsic or phenotypic factors of isotope 

fractionation. Multivariate analysis revealed at least two potential sources of non-dietary factors 

influencing the carbon isotope ratio values of the amino acids in human hair: body mass index 

(BMI) and age. These results provide evidence that compound-specific isotope ratio analysis has 

the potential to go beyond region-of-origin or geospatial movements of individuals—obtainable 

through bulk isotope measurements—to the provision of physical and characteristic traits about 

the individuals, such as age and BMI. Further development and refinement, for example to genetic, 

metabolic, disease and hormonal factors could ultimately be of great assistance in forensic and 

clinical casework. 

4) J. R. Almirall, G. P. Jackson, “Review: 27th ASMS Sanibel Conference on Mass 

Spectrometry—Security and Forensic Applications” J. Am. Soc. Mass Spectrom. 2015, 26, 695-

698. (This report does not specifically acknowledge NIJ sponsorship) 

Abstract: The 27th Annual ASMS Sanibel Conference on Security and Forensic Applications of 

Mass Spectrometry was held from January 22 to 25, 2015 at the Hilton Clearwater Hotel in 

Clearwater Beach, FL. The co-organizers were José R. Almirall of Florida International University 

and Glen P. Jackson of West Virginia University. The conference brought together leading 

researchers and practitioners from universities, research institutions, and forensic laboratories to 

discuss the application of mass spectrometry to issues of national security and forensic science. 

Attendees represented many corners of the world, including Australia, Brazil, Canada, China, 

Czech Republic, Denmark, France, Germany, Sweden, United Kingdom, and the United States. 

Although the weather was very windy and cold by Florida standards, the enthusiastic spirit of the 

attendees was unaffected. Graham Cooks of Purdue University gave the conference a boost by 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

40 
 

 

 

 

 

 

 

 

G. P. Jackson, Biometrics from Human Hair 

providing NSF funds to support student travel. The grant (CHE-1262145) titled Strengthening 

Forensic Science through Connections with the Analytical Sciences^ provided 20 travel awards 

for students to attend the conference. ASMS provided an additional 20 awards, all of which were 

filled. The organizers and the students remain very grateful for the exposure to cutting-edge 

research and researchers in forensic mass spectrometry. The conference attracted a total of 150 

registrants (Figure 1) and more than 56 poster presentations. In addition, five attendees signed up 

to receive two continuing education units (CEU) from West Virginia University. 

5) W. D. Hoffmann and G. P. Jackson “Forensic Mass Spectrometry” Ann. Rev. Anal. Chem. 

2015, 8, 419-440. free eprint: DOI: 10.1146/annurev-anchem-071114-040335. 

Abstract: Developments in forensic mass spectrometry tend to follow, rather than lead, the 

developments in other disciplines. Techniques of great forensic utility or potential born 

independently of forensic applications include ambient ionization, imaging mass spectrometry, 

isotope ratio mass spectrometry, portable mass spectrometers, and hyphenated chromatography– 

mass spectrometry instruments, to name a few. Forensic science has the potential to benefit 

enormously from developments that are funded by other means, if only the infrastructure and 

personnel existed to adopt, validate, and implement the new technologies into casework. Perhaps 

one unique area in which forensic science is at the cutting edge is in the area of chemometrics and 

the determination of likelihood ratios for the evaluation of the weight of evidence. Such statistical 

techniques have been developed most extensively for ignitable-liquid residue analyses and isotope 

ratio analysis. This review attempts to capture the trends, motivating forces, and likely impact of 

developing areas of forensic mass spectrometry, with the caveat that none of this research is likely 

to have any real impact unless: (a) The instruments developed are turned into robust black boxes 

with red and green lights for positives and negatives, respectively, or (b) there are PhD graduates 

in the workforce who can help adopt these sophisticated techniques. 

6) A. H. B. Rashaid, P. B. Harrington, G. P. Jackson “Profiling Amino Acids of Jordanian Scalp 

Hair as a Tool for Diabetes Mellitus Diagnosis: A Pilot Study” Anal. Chem. 2015, 87, 7078-7084 

Abstract: Hair analysis is an area of increasing interest in the fields of medical and forensic 

sciences. Human scalp hair has attractive features in clinical studies because hair can be sampled 

easily and noninvasively from human subjects, and unlike blood and urine samples, it contains a 

chronological record of medication use. Keratin protein is the major component of scalp hair shaft 

material and it is composed of 21 amino acids. The method used herein for the amino acid 

determination in hair included keratin protein acid hydrolysis using 6 M hydrochloric acid (HCl), 

followed by amino acids derivatization using N ,O bis(trimethylsilyl)trifluoroacetamide (BSTFA), 

and the determination of derivatized amino acids by gas chromatography/mass spectrometry 

(GC/MS). Amino acid profiles of scalp hair of 27 Jordanian subjects (15 diabetes mellitus (DM) 

type 2 patients and 12 control subjects) were analyzed. A fuzzy rule-building expert system 

(FuRES) classified the amino acid profiles into diabetic and control groups based on multivariate 

analyses of the abundance of 14 amino acids. The sensitivity and specificity were 100% for 

diabetes detection using leave-one-individual-out cross-validation. The areas under the receiver 

operative characteristics (ROC) curves were 1.0, which represents a highly sensitive and specific 

diabetes test. The nonessential amino acids Gly and Glu, and the essential amino acid Ile were 

more abundant in the scalp hair of diabetic patients compared to the hair of control subjects. The 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

41 
 

 

 

6.2. 1 

G. P. Jackson, Biometrics from Human Hair 

associations between the abundance of amino acids of human hair and health status may have 

clinical applications in providing diagnostic indicator or predicting other chronic or acute diseases. 

7) F. Huelsemann, C. Lehn, S. Schneiders, G. P. Jackson, S. Hill, U. Flenker, “Global Spatial 

Distributions of Nitrogen and Carbon Stable Isotope Ratios of Modern Human Hair” Rapid 

Commun. Mass Spectrom. 2015, 29, 2111-2121. 

Abstract: RATIONALE: Natural stable carbon (δ13C) and nitrogen isotope ratios (δ15N) of 

humans are related to individual dietary habits and environmental and physiological factors. In 

forensic science stable isotope ratios of human remains like hair and nail are used for geographical 

allocation. Thus the knowledge of the global spatial distribution of human δ13C and δ15N values is 

an essential component in the interpretation of stable isotope analytical results. 

METHODS: Up to now no substantial global data sets of human stable isotope ratios are available, 

although the amount of available (published) data has increased within the last years. We hereby 

summarized published data on human global δ13C and δ15N values (around 4000 samples) and 

added experimental values of more than 400 additional worldwide human hair and nail samples. 

In order to summarize isotope ratios for hair and nail samples correction factors were determined. 

RESULTS: The current available data set is biased towards Europe and North America with only 

limited data for countries in Africa, Central and South America and Southeast Asia. The global 

spatial distribution of carbon isotopes is related to latitude and supports the fact that human δ13C 

values are dominated by the amount of C4 plant in the diet, either due to direct ingestion as plant 

food, or by the use as animal feed. In contrast, the global spatial distribution of human δ15N values 

is apparently not exclusively related to the amount of fish or meat ingested, but also to 

environmental factors influencing agricultural production. 

CONCLUSIONS: There are still a large proportion of countries, especially in Africa, where there 

is no existing data for human carbon and nitrogen isotope ratios. Whereas the interpretation of 

modern human carbon isotope ratios at the global scale is quite possible, and correlates with the 

latitude, the potential influences of extrinsic and/or intrinsic factors on human nitrogen isotope 

ratios have to be taken into consideration. 

6.2 Presentations 

Our group has presented the results of our work in the form of invited oral presentations at more 

than 15 universities and conferences since commencing this grant. The presentations based on this 

project from Jan 2014-present are listed below: 

Non-conference venues 

University of South Carolina, Columbia, SC (Nov 2016). 

Genetics and Developmental Biology Program, WVU, Morgantown, WV (Oct 2015) 

Food and Drug Administration (FDA), Washington, DC (March 2015) 

Roads Scholar Academy (WVU), Naples, FL (Feb 2015) 

Duquesne University, Pittsburgh, PA (Feb 2015) 

Georgetown University, Washington, DC (Sept 2014) 

Society for Applied Spectroscopy (SAS) Speaker Tour, John Carroll University, Cleveland, OH 

(March 2014) 

Department of Chemistry, Purdue University, West Lafayette, IN (April 2014) 

Department of Chemistry, Indiana University, Bloomington, IN (April 2014) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

42 
 

 

6.2.2 

G. P. Jackson, Biometrics from Human Hair 

Conferences 

Mayara, P. V. de Matos, Glen P. Jackson, “Compound-Specific Isotope Analyses of Hair Samples: 

Discrimination that goes Beyond Dietary Factors”, Forensic Isotope Ratio Mass Spectrometry 

Conference” Auckland, NZ, September 2016. 

Glen P. Jackson, “The Future of Forensic Instrumental Methods of Analysis”, AAFS Meeting, Las 

Vegas, NV, Feb 2016. (Oral) 

G. P. Jackson, K. I. Konstantynova, M. P. V. de Matos, R. Mohr "Forensic Source Attribution 

using Stable Isotopes: Hairs to Humans and Insects to Carrion”, Pittsburgh Conference on 

Analytical Chemistry and Applied Spectroscopy, New Orleans, LA, Mar 2015. (Oral). 

G. P. Jackson, K. I. Konstantynova, M. P. V. de Matos, R. Mohr "Forensic Source Attribution 

using Stable Isotopes: Hairs to Humans and Insects to Carrion”, 67th Meeting of the American 

Academy of Forensic Sciences, Orlando, FL, Feb 2015. (Oral). 

*G. P. Jackson "Linking Insects to Humans and Hair to Human Phenotypes using Stable Isotopes”, 

ASMS Sanibel Conference on Security and Forensic Applications of Mass Spectrometry, 

Clearwater Beach, FL, Jan 2015. (Oral). 

*G. P. Jackson, R. Mohr, K. I. Konstantynova "Forensic attribution using stable isotopes: hairs to 

humans and insects to carrion”, SciX Conference, Reno, NV, Oct 2014. (Oral). 

The following presentations have also been scheduled, or submitted for consideration 

Glen P. Jackson, “Forensic Attribution using stable isotopes: Hairs to Humans and insects to 

carrion”, at the Spring meeting of the American Chemical Society, San Francisco, CA, April 2017. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

43