FORENSIC IDENTIFICATION OF HUMAN URINE STAIN EVIDENCE USING METABOLOMICS APPROACH-Mustapha Zein
1. FORENSIC IDENTIFICATION OF HUMAN URINE STAIN EVIDENCE USING
METABOLOMICS APPROACH
In Partial Fulfillment of the Requirements for the Degree of
Master of Science in Forensic Science
Presented to the Department of Forensic Science
Sam Houston State University
Mustapha Zein
May 2015
2. ii
ABSTRACT
Zein, Mustapha, Forensic Identification of human urine stain evidence using
metabolomics approach. (Department of Forensic Science) May 2015, Sam Houston
State University, Huntsville, Texas.
Identification of biological fluids is critical in criminal investigation where this
evidence may exonerate or exculpate a suspect. Urine stain evidence has been
encountered during the investigation of criminal cases, and the identification of urine is
essential in order to prosecute the case. There are plenty of crime labs that test urine for
drugs or for toxicological purposes, but none that test a substance to determine if it is
actually urine. In this study, a headspace solid phase micro-extraction (HS-SPME)
technique coupled with gas chromatography/mass spectrometry (GC/MS) was developed
in order to detect volatile organic compounds (VOCs) evolved from a urine stain.
Different sample preparation variables, such as urine stain size, SPME fiber coating type,
extraction time, and sample incubation temperature, were tested and evaluated to
maximize the extraction and detection of headspace VOCs from urine stain samples in
order to support metabolomics data analysis. Triplicates were conducted for each
samples to ensure reproducibility and accuracy of the results obtained. More than 60
VOCs have been reported from the headspace of liquid urine samples. In our study,
under the optimal HS-SPME-GC/MS condition, as many as 18 VOCs could still be
detected from a typical human urine stain sample. To adopt the metabolomics
methodology for the identification of urine stains, the matrix effect of substrate, and the
age of urine stain should be further evaluated.
KEY WORDS: METABOLOMICS, SOLID PHASE MICRO-EXTRACTION (SPME),
GAS CHROMATOGRAPHY/MASS SPECTROMETRY (GC/MS), URINE STAIN
SAMPLE, VOLATILE ORGANIC COMPOUNDS (VOC)
3. iii
TABLE OF CONTENTS
Page
TABLE OF CONTENTS
ABSTRACT.........................................................................................................................ii
TABLE OF CONTENTS....................................................................................................iii
LIST OF TABLES..............................................................................................................iv
LIST OF FIGURES .............................................................................................................v
INTRODUCTION .............................................................................................................. 1
MATERIALS AND METHODS........................................................................................ 8
RESULTS AND CONCLUSIONS................................................................................... 13
SUMMARY...................................................................................................................... 25
REFERENCES ................................................................................................................. 27
5. v
LIST OF FIGURES
FIGURE Page
1 Overlay chromatogram of different urinary sample volumes.…………...…..…13
2 4-Heptanone and 2-Pentanone abundances against different urine volumes…..14
3 Siloxane peaks………………………………………………………………….15
4 Ethanol abundance of a beer stain sample overtime……………..…………….18
5 Acetic acid abundance of a beer stain sample over time………………..……...18
6 The recovery of standards using different fiber coating types……………...…..20
7 Cotton swab urine stain recoveries……………………………………………..21
8 4-Heptanone yields recovered from different surfaces…………………………22
7. 1
INTRODUCTION
Urine stain evidence has been encountered during the investigation of criminal
cases, and the identification of urine is essential in order to prosecute the case. There are
plenty of crime labs that test urine for drugs or toxicological purposes, but none that test
a substance to determine if it is actually urine. In Texas, Code of Criminal Procedure,
Article 38.35 requires that forensic analysis of physical evidence to be done at an
accredited crime lab. Anything exempted from accreditation under Government Code
Section Section.411.0205(c) is by definition not a forensic analysis. In the case of
identification of urine evidence for criminal prosecution, there is no accrediting body
that would certify a lab for such forensic testing. In practice, there is an exception to the
normal accredited crime lab requirement for areas where the Director of the Department
of Public Safety (DPS) certifies that an exemption to the requirement applies.
Consequently, the DPS director has found that testing for the purpose of urine
identification does not need to come from an accredited crime lab for criminal cases.
Omic technologies, such as genomics, transcriptomics and proteomics, have
contributed vastly in the pursuit of the understanding of cell and tissue functioning as
well as various diseases associated with them [1,2] The latest in the omics category is
metabolomics, the study of metabolites or metabolite profiles, which seems to be a
promising field and is so far very competent in the discovery of unique chemical
fingerprints that specific cellular processes leave behind. Metabolomics provide a
snapshot of the entire physiology of an organism and has been applied to understand
8. 2
problems related to toxicology, environment, disease cancer research, agriculture, and
nutrition. Its results is often combined with proteomics studies to provide a
comprehensive understanding of how our body works. In a typical metabolomics study,
metabolites can be identified in multiple body fluids, such as urine, blood, semen to
construct a library encompassing different analytes and consequently associate them to
their respective body fluids. It enables the assessment of a broad range of endogenous
and exogenous metabolites in an attempt to build a possible correlation between the
manifestation of a particular disease and the presence of specific biomarkers [3].
Nuclear magnetic resonance (NMR) and Mass spectrometry (MS) are by far the
most commonly used analytical methods for the identification of metabolome [4]. Gas
chromatography–mass spectrometry (GC-MS) is also a widespread technique used in
targeted and non-targeted measurements of metabolites [5,6]. Urine stain among other
body fluids is usually found in a crime scene and its identification provides important
investigative leads that may exonerate or convict a suspect. Unfortunately, forensic
identification of human urine stain is not a common forensic testing in a crime lab.
Confirmatory test for human urine stains can possibly be conducted by DNA
methodology, however, the trace amount of biological material left in the urine stain
evidence makes this DNA approach unsuccessful. Urine contains a very low numbers of
nucleated cells such as leukocytes and epithelial cells rendering successful DNA typing
close to impossible [7,8,9]. As a result, profiles generated from urinary DNA are often
incomplete because of locus/allelic dropout. Therefore, the identification of urine stain
sample using DNA typing approach is not a routine testing in a crime laboratory. Today,
the Microcrystalline testing, Jaffe test, Urine RISD™, and Uritrace are the most
9. 3
commonly used presumptive tests to identify a urine stain sample [10]. Microcrystalline
test is used to identify the presence of urea from the sample based on the formation of
dixanthylurea crystals when urea reacts with xanthydrol [11]. Other chemical
compounds exclusive to urine such as creatinine can also generate specific crystal
shapes under acidic environments and can be easily visualized under a polarized light
microscope. However, the microcrystalline test is not a human specific test and does not
prevent the possible false negative resulting from environmental source of urea on the
evidence. Jaffe test is one of the reactions indicating the presence of creatinine. It
depends on the complex formation of creatinine with alkaline picrate solution leading to
a change to a bright orange color. The change in color is proportional to the amount of
creatinine present in solution thus facilitating the quantification of this analyte [12]. The
determination of creatinine levels can also be determined by measuring the absorbance
of the unknown compared to a set of standards using spectrophotometry techniques [13].
Unfortunately, several interfering compounds such as glucose, proteins, and ascorbic
acid also react with picric acid, producing similar colored complexes. Because urea and
creatinine are commonly found in animal urine, the detection of these target compounds
does not constitute human urine specificity [14]. Moreover, the presence of creatinine in
other biological fluids like blood and its potential cross-reaction with other metabolites
renders its usage for identification of urine more challenging.
Other tests such as the Rapid Stain Identification of Urine (RISD™) are
designed to provide its user a fast, reliable and easy detection of a urine stain
sample [15]. RSID™-Urine is an immunochromatographic strip test that can detect the
presence of the Tamm-Horsfall (THP) glycoprotein (also known as uromodulin). THP is
10. 4
one of the most abundant proteins present in urine and is secreted by the ascending limb
of the loop of Henle. However, RISD™ usage is also restricted due to potential cross-
reactions with other proteins. Finally, Uritrace® is a presumptive test aiming at
presumptively detecting urine based on its creatinine composition. Uritrace® comes in an
easy to use format aiming at the identification of suspected urine stain and is used at a
crime scene because of its portability [16]. Once the samples are added to the well, it
flows laterally and reaches the pad impregnated with chemicals that reacts with
creatinine.
Although DNA in urine is found in very small quantities, few studies
successfully obtained full profiles of urine stains indicating the importance of this
biological sample to be collected by crime scene investigator [17]. To make this
possible, it is important to first identify the stain first to prove its identity (urine) and its
origin (human). Many human-specific metabolites such as 2-heptanone, 4-heptanone, 2-
butanone, acetone, ethyl acetate, and pyrrole were identified from urine liquid samples
[18]. However, metabolite identification from urine liquid samples does not reflect a real
crime scene setting. Usually, urine is found as an almost dried stain embedded in a
particular surface. Nakazono et al. identified five main keto-steroid conjugates utilizing
high performance liquid chromatography (HPLC-Reverse phase) [19]. These five
metabolites are not exclusive to a urine sample but its presence in conjunction with urea
and uric acid definitely indicate a human urine sample. To this date, no validated
confirmatory method is present to identify a urine stain sample.
11. 5
In a forensic laboratory, chemical confirmatory tests of a sample is to confirm a positive
or sometimes, negative results and are typically conducted using gas
chromatography/mass spectrometry (GC/MS) or Liquid Chromatography (LC/MS) [20].
However, sample preparation or extract clean up should be performed before the use of
instrument for chemical identification. Solid phase micro-extraction (SPME) is a
solvent-free extraction technique invented by Pawliszyn in 1989 [21]. SPME integrates
extraction, sampling, concentration and sample introduction in one single solvent free
step [22]. SPME involves the introduction of a fused-silica fiber stationary phase into
the sample (partitioning) followed by desorption into the instrument. Fiber introduction
to the sample can be conducted in three different ways: Membrane protection, Direct
immersion (Di), and Headspace (HS) [23]. Membrane protection involves the usage of a
membrane serving as “physical barrier” to protect the fiber from any unwanted high
molecular weight analytes. Direct is the immersion of the fiber into the sample. Finally,
in the headspace mode, the different volatiles are extracted from the gaseous phase lying
above the sample of interest. The partitioning between the solid phase and the headspace
“gaseous phase” reflects the degree of saturation of the fiber with different analytes. The
number of analytes extracted by the fibre is proportional to its concentration in the
sample as long as equilibrium is reached. Equilibrium and maximum recovery for a
given analyte can be only reached at specific parameter. Factors affecting
equilibrium are sample volume, extraction time and agitation [24]. However, it is
almost impossible to find a perfect parameter encompassing all analytes. Also, the
physical nature of the analyte and any limitation in terms of volatility can affect the
number of analytes detected.
12. 6
HS-SPME is a powerful solventless technique requiring minimal to no sample
preparation and is now widely used in forensic science and other clinical studies. Zhang
et al. already investigated HS-SPME to extract urinary volatile components and found
that urine mainly contains alcohols, aldehydes, furans, ketones and pyrroles [25].
However, the correlation between the presence of specific volatiles and human-specific
urine stain identification was never evaluated. In fact, odorants or volatiles present in
urine may indicate a particular disease. Mastsumura et al. argued that if dogs are able to
distinguish between urine of a subject with lung cancer and a healthy subject, then there
are definitely different volatiles emitted by both samples. Two major compounds were
detected: 1-butanone and 3-hydroxy-2-butanone at a significantly higher concentration
than healthy individual [26] Other extensive studies focused on multiple cancer types
such as leukemia, colorectal and lymphoma and found 82 volatile metabolites in the
control and oncological groups belonging to distinct chemical families including
ketones, acids, alcohols, benzene derivatives, sulfur conpounds etc… The study
concluded that for the control group (healthy), the chemical families with higher
contribution to the urinary metabolomic profile were ketone and sulfur specifically 4-
heptanone and methanethiol. Benzene derivatives, terpenes and phenols were most
commonly found in the oncological group [27]. In this study, we adopted HS-SPME
methodology to evaluate the extraction of chemical signatures from human urine stain
evidence.
Different fiber polarities were selected to conduct the experiment: from PDMS
(Non-polar) to Carboxen-PDMS (bipolar). Multiple fiber usage will maximize the
recovered analytes and the incidence of polarity on the quality and quantity of the
13. 7
metabolites extracted by the fiber can be assessed [28]. The aim of this study was to
explore the different analytes in an attempt to associate them to a human-specific urine
stain sample. The successful detection of important metabolites from urine stain
samples is crucial for the future development of a laboratory method for the
confirmatory testing of a human urine stain sample.
14. 8
MATERIALS AND METHODS
i-Sample Preparation:
To develop and optimize a method for urine stain identification using HS-SPME-
GC/MS, urine samples were collected from a healthy volunteer (Male, 25 yrs old).
Urine samples were stored at 4 °C until analysis. One 1x1 cm napkin was placed in a 10
mL headspace vials (Restek®
, Bellefonte, PA, USA). Urine stain samples were prepared
by transferring 500 µL urine onto the precut napkin, and dry under the fumehood for 24
hours. Ten ng hexadecane, used as an internal standard (IS), was then added to each
sample for quality control. The 10 mL vials were capped with magnetic screw caps
(Restek®) equipped with Polytetrafluoroethylene (PTFE) silicone septum for an easier
fiber penetration and headspace extraction.
ii-Instrumentation:
A bench top GC-MS system 5995 series (Agilent®) was used for the separation
and identification of headspace VOCs extracted by HS-SPME. Helium gas was used as
the carrier gas with a flow rate of 1 mL/min. Agilent Autosampler 80 was used to
eliminate analyst-to-analyst variability and reduce the time needed for data generation.
The HS-SPME-GC/MS was operated under an incubation temperature of 50 °C, an
extraction time of 20 mins, vial fiber penetration of 31 mm, agitator speed at 250 rpm,
and a splitless injection to the column with desorption time of 2 mins. Chromatographic
separation was carried out using a capillary column Rxi®-35Sil MS (15 m x 0.25 mm x
0.25 um) from Restek®. The Agilent 5975C MSD was programmed to a full scan mode
15. 9
(25 to 450 m/z). The GC oven temperature was hold for 5 mins at 40°C, 10 °C/min up to
250 °C, then hold at 250 °C for 5 mins.
iii-Preliminary Sample Testing:
In the preliminary qualitative analysis of urine headspace volatiles organic
compounds (VOCs), the number of VOC was monitored and their abundances were
measured relative to different sample volumes. Stain samples were created using a
volume of 50 to 2000 µL (n=3). The purpose of this test was to determine the minimum
urine volume that was required for HS-SPME.
iv-Evaporation of Volatile:
Volatile component in a urine stain may evaporate over time. In order to
determine the general evaporation rate, ethanol and acetic acid in a beer sample were
tested as a model. Busch® Beer was purchased and spilled on a T-shirt and left to dry.
One 1x1 cm size of clothes was cut from the beer stain after 3, 6, 24, 48, and 100 hours
dryness. The sample was analyzed by the HS-SPME-GC/MS method. Alcohol and
acetic acid were monitored from each sample.
v. Fiber choice/conditioning
Polydimethylsiloxane (PDMS), Polyethylene Glycol (PEG), Carboxen
Polydimethylsiloxane (CAR/PDMS), Divenylbenzene Carboxen Polydimethylsiloxane
(DVB/CAR/PDMS) of SPME fibers (Supelco®, Bellfonte, PA, USA) with different
polarities were tested to evaluate their extraction performance for VOCs from urine
16. 10
stains. All four fibers types were conditioned according to the manufacturer’s
recommendations before use. PDMS fiber (100 µm) was conditioned at 250 °C for 30
mins, PEG fiber (60 µm) was conditioned at 240 °C for 30 mins, CAR/PDMS fiber (75
µm) was conditioned at 300 °C for 60 mins and DVB/CAR/PDMS fiber (50/30 µm) was
conditioned at 270 °C for 60 mins. The number of VOCs was monitored using four
different fiber types from urine stains prepared from the same source. Urine stain
samples were incubated at 50 °C for 5 mins prior to HS-SPME for 20 mins.
vi-Metabolites Identification:
The MS data was collected by the detector and analyzed using Hewlett-Packard
(HP) Chemstation® software. Different analytes were identified and compared to the
SWGDRUG and NIST08 reference library and assigned qualitative match percentages.
For metabolite identifications, standards such as acetone, toluene, ethyl acetate, and
pyrrole, 4-heptanone, and 2-pentanone were purchased from Sigma Aldrich (St. Louis,
MO, USA). Peaks in the total ion chromatogram (TIC) were identified through
comparison with the probability matching of mass spectra of the SWGDRUG and
NIST08 and retention times of standards. All TIC chromatograms were exported as .csv
files by Chemstation for Principal Component Analysis (PCA).
vii-HS-SPME-GC/MS for Urine Stains on Clothe substrate
Fifty mL of urine was spilled on a T-shirt and left to dry over night. One 1x1 cm
size of clothes was cut from the urine stain area and placed in a 10 ml vial for HS-
17. 11
SPME-GC/MS. Another 1x1 cm size of clothes was cut from a non-stained area to
serve as a blank. Six samples were collected for the purpose of PCA analysis.
viii-Cotton swab (Qtip) Sampling of Urine Stains
In this experiment 50 mL of urine was spilled on clothes and left to dry completely. A
Q-tip moistened with deionized water was swabbed over the dried urine stain area and
placed in a 10 mL headspace vial for HS-SPME-GC/MS. A Q-tip cotton blank was also
ran for comparison. Six samples of each group (Q-tip swab and Q-tip blank) were
collected and analyzed by HS-SPME-GC/MS.
ix-Principal Component analysis (PCA):
The main purpose of principal component analysis (PCA) is the analysis of data
to identify important patterns while reducing the dimensions of the dataset with minimal
loss of information.
Many journal articles used PCA to better understand and be able to analyze the
significance of a particular dataset: Felder et al (2004) used PCA to assimilate the
difference between oligonucleotide isolated from Archea, Eubacteria, and Eukarya (see
figure below). They found that oligonucleotides were different depending on the bacteria
group they originate from. They were able to spatially plot the different group with no
overlap, which in turn suggests the difference between data.
18. 12
PCA approach used in Biology: The spatial grouping of three bacterial species
We incorporated PCA analysis to better understand the data generated for urine
stain sample. PCA is a tool available for free in the R-Studio software for Macintosh and
Windows. It allows the user to generate a graph depicting the repartition of different
major groups in order to assess their differences. R-software and ChemoSpec library
were installed on an Apple Macbook Air computer and each group was associated with
at least 3 datafiles for graph generation. Moreover, the R-software has the ability to
process .CSV Chemstation files, which proved efficient for a fast processing of the data.
19. 13
RESULTS AND CONCLUSIONS
Sample volume Optimization:
To find the minimum urine sample that can provide chemical information by HS-
SPME-GC/MS, the abundance and the overall number of volatiles were compared for
different urine volumes spilled on a napkin: 250, 500 and 750 µl. (Figure 1). Urine stain
samples with lower urine volumes (250 µL) showed lower number of VOCs (14 VOCs)
as well as lower abundances for VOCs commonly observed in every sample. Overall,
major peaks were observed among all samples indicating that volumes as low as 250 µL
can generate a decent urinary profile. VOCs found in 500 µL urine stain are tabulated
below (Table 1). A pilot study was also conducted with different urine volumes in an
attempt to recognize the minimum volume that can generate the maximum number of
volatiles. We discovered that at 500 µL, the number of volatile reached a plateau.
Therefore, 500 µL was the volume of choice for the rest of the study.
Figure 1: Overlay chromatogram of the (1) blank, 250 ul, 500 ul and 750 ul urine stain samples.
20. 14
Metabolomics:
In the samples we conducted for the volume optimization, 4-heptanone and 2-
pentanone were detected in all our samples. (Figure 2). These two metabolites are of
particular interest since their presence is ubiquitous in human urine samples
(Statheropoulous et al., 2005). Thus, the presence of 4-heptanone and 2-pentanone in a
questioned or unknown suspected biological stain sample might indicate the presence of
urine originating from humans. We noticed an increase in abundance with larger sample
volumes. This correlation is a logical one since the increase in volume is often
associated by an increase in the abundance of the overall metabolites present in urine. It
is worth noting that at volumes as low as 250 µL, we were able to recover the two
metabolites of interest. These results are significant since an average human emit a
volume of 2 Liters/day, which is approximately 10,000 folds larger than the volume
utilized to conduct the experiment.
Figure 2: 4-Heptanone and 2-Pentanone abundances in 250, 500 and 750 ul urine volume creating the
stain
0
200000
400000
600000
800000
1000000
1200000
0 250 500 750 1000
Areaunderthepeak
Urine Volume (µl)
4heptanone
2-pentanone
21. 15
Siloxane peaks:
The blank was characterized by the presence of three major peaks, which were also
observed in every urine stain samples. (Figure 3). These peaks were identified using the
NIST08 library as complex molecules containing Siloxane. Siloxane is a major molecule
present in different components of the SPME apparatus. The outer surface of a typical
SPME fiber has Siloxane as its major composition and its introduction or desorption to
the GC/MS instrument can lead to its partial degradation leading to the peaks observed.
Another potential contaminant is the siloxane septum, which gets perforated prior to
desorption into the inlet. This issue can be resolved when using The Merlin Microseal®
System. This system is a septum-less system and as a result, septum fragments will not
be formed and deposited in the inlet liner.
Figure 3: The three silica-containing molecules found in blanks and urine stain samples
22. 16
Table 1- VOCs identified using NIST08 library match in black. In blue are the VOCs that were identified
by comparing retention times of standards. In Red is the Internal Standard.
peak # VOC
Retention
time Area
2 Acetone 0.444 109013
3 1-Propanol 0.463 85669
6 2-Pentanone 0.854 452857
8 Acetic acid 1.402 162331
12 4-Heptanone 2.93 632206
13 Oxime-Methoxy-phenyl 4.149 2903715
16 octanal 7.278 75721
20 Benzyl alcohol 9.049 69146
23 octanoic acid 10.449 66402
25 decanal 10.983 125253
30 propanoic acid, 2 methyl propyl ester 13.035 143490
31 butanoic acid butyl ester 13.416 249544
33 butylated hydroxytoluene 14.473 89487
34 Hexadecane IS 14.997 67263
35 phenol, 2,5, bis dimethylethyl 15.245 52202
36
2,2,4 trimethyl 1,3 pentanediol
diisobutyrate 15.945 2426954
40 1,2 Benzenedicarboxylic acid 19.816 85101
23. 17
Detection of volatiles over time:
In order to prove the persistence of different volatiles over a time period, beer
was spilled on a shirt and the ethanol abundance was evaluated over time. As shown in
Figure 4 ethanol, being a chemical with a high vapor pressure, it tends to evaporate even
before reaching its boiling point. However, the major targeted metabolites have a lower
vapor pressure facilitating their persistence over an extended period of time. Results
showed that ethanol levels persisted for a period of 48 hours before going under the limit
of detection of our instrument (HS-GC/MS) at around 100+ hours.
Over the course of our experiment, we found that acetic acid is also present in
beer and has a lower vapor pressure than ethanol. As a result, we tested its persistence
over time as well. As shown in Figure 5, the results showed that acetic acid persisted
over time and its abundance in a beer stain was more pronounced than ethanol even
though a downhill pattern was seen. After 100 hours of dryness, acetic acid could still be
detected at an abundance that was comparable to its original level. This higher
persistence of acetic acid over time compared to ethanol can be explained because of its
low vapor pressure. A lower vapor pressure leads to the persistence of the volatile in
question for a longer period of time unless the boiling point of this particular analyte is
reached. Thus, the detectability of volatiles (VOCs) is not problematic for dried stains.
24. 18
Figure 4: Ethanol abundance of a beer stain sample over time.
Figure 5: Acetic acid abundance ofa beer stain sample over time
25. 19
Fiber coating optimization:
In order to find the best fiber to be used for developmental methods concerning
the urinary identification of stain samples, different chemical metabolites commonly and
reportedly found in high concentration in human urine (acetone, ethyl acetate, toluene
and pyrrole) were mixed in one stain sample and left to dry. The sample was then
extracted using multiple fiber coatings based on their availability in the laboratory
namely: DVB/CAR/PDMS, DVB/PDMS, PDMS and PEG. Results showed that all fiber
types were able to extract and detect all mixture components, however each fiber had
extracted analytes at different abundances depending on its interaction and its affinity
with the target analyte. As shown in Figure 6, DVB/PDMS had the lowest standard
recovery compared to the other fiber coating type. PDMS showed a noticeable affinity to
pyrrole and had the highest abundance relative to other fiber types however it wasn’t
successful at extracting the other analytes as effectively. We recommend using all fiber
types when available since their usage allow greater diversity in terms of the metabolites
recovered. However, if a single fiber shall be used, we suggest DVB/CAR/PDMS for
urinary studies since it was the most efficient at extracting the four standard analytes
with decent yields.
26. 20
Figure 6: The abundance recoveries of Acetone, Ethyl Acetate, Toluene and Pyrrole using different fiber
coating types including: DVB/CAR/PDMS, DVB/PDMS, PEG, and PDMS fibers.
Cotton-swab:
The ability to recover metabolite on a piece of cloth and their successful transfer
to a cotton swab was evaluated in this experiment. As shown in Figure-7, we noticed an
increase in the number of peaks when the cotton was swabbed with the urine stain
sample. Blanks showed only two peaks representing siloxane residue, which was found
in all our samples, however no other significant peak was detected in the blank proving
that our samples were free of any contaminants that may be present prior to sample
loading. The cotton swab sample was successful at extracting multiple metabolites,
which is proved by the presence of many extraneous peaks compared to the cotton
blank. Cotton immersed in urine had the best signal in terms of the number of peaks
identified compared to the cotton swab. The loss of metabolites is explained by their
27. 21
evaporation over a period of time. We believe that the metabolites transferred from the
stain to the swab are indicative of urine because of the presence of the observed
extraneous peak compared to cotton blank and the overlap of these peaks with cotton dip
sample.
Figure-7: Chromatogram of a urine stain swabbed from a Tshirt (green), cotton swab dipped in liquid
urine (purple), Cotton blank (red) and an empty vial (blue)
28. 22
4-Heptanone:
Finally we compared 4-heptanone yields for different surfaces namely Napkin, Clothe
and Cotton Swab in Figure 8
Figure 8: 4-Heptanone yields recovered from different surfaces:Clothes, Cotton Swab, and Napkin
Clearly, the recovery of this important metabolite is different depending on the nature of
the surface urine was spilled on. T-shirt and Napkin surfaces showed very similar yields
compared to the cotton swab. The cotton was immersed in water and swabbed against a
dry urine stain surface (Shirt) and placed in a vial for testing. It is important to
emphasize that metabolite loss is inevitable when having dry urine stain samples,
however we are looking at the persistent and major metabolites such as 4-heptanone and
2-pentanone. The detection of these metabolites in a sample may help the analyst decide
on the nature of the biological fluid to be identified. Swabbing the area is clearly not an
29. 23
efficient technique to recover this metabolite and we recommend using the sample itself
although its usage may be destructive in nature.
Principal Component analysis and the R-software:
We incorporated PCA to our study in order to better understand the significance
of the data we generated in our experiment. PCA can help us assess the differences and
similarities between groups depending on their spatial repartition on the graph.
Overlapping groups can suggest the common origin of the sample.
Different groups were created: Blank, Urine stain sample on clothes, and urine
swab using Qtip, As shown in Figure 9, Urine and blank were clustered in different
areas, which suggests the difference between those two samples. Also, we noticed the
overlap between urine spilled on clothe and urine swabbed. This is indicative that
substrate/surface does not drastically affect the results since urine is present in both
samples. Therefore, even though 4-heptanone was not detected in the cotton swab, the
overall differences between urine spilled on clothes and swab are not significant because
of the group overlap observed using principal component analysis.
Figure 9-Principal component analysis (PCA) for urine spilled on T-shirt, swab, and blank
30. 24
Conclusion:
Volatiles are prone to vaporization, however we were interested to identify the major
and persistent VOCs in an attempt to associate them back to the stain source. For the
targeted approach, we conducted a pilot study to determine the minimum urinary
volume that was required for the generation of a chromatogram with maximum number
of VOCs. We were able to recover 17 different VOCs, of particular interest 2-pentanone
and 4-heptanone that were previously reported to be characteristic of human urine
samples [18]. The two metabolites were detected at low volumes of 250 uL, which
renders this discovery significant knowing that an individual emits an average of 2
Liters urine daily (10,000 folds). We also investigated substrate variability by testing
urine stain formed on different surfaces namely: Napkin, T-shirt and cotton swab. 4-
heptanone was successfully detected from Napkin and T-shirt however this metabolite
was not found on the cotton swab. In order to avoid sampling error, we recommend
using the sample stain directly for HS-SPME-GC/MS as opposed to swabbing the
suspected stain. For the non-targeted approach, we used the PCA tool in an attempt to
assess the similarities and differences of the total ion chromatogram (TIC). Based on the
PCA analysis, we were able to distinguish between the blank and the urine stain sample.
The overlap between the urine swab and the T-shirt urine stain can explain the common
origin of the biological fluid despite the loss or evaporation of specific VOCs (ie: 4-
heptanone). Further investigation is needed to determine the possible metabolic pathway
of using 4-heptanone and how its presence can lead the forensic identification of human
urine stain sample.
31. 25
SUMMARY
Identification of an unknown stain can help understand a crime scene and the
sequence of events associated with it. Urine, being the most abundant fluid emitted from
the body on a daily basis has a high probability to be found at any given scene. The Jaffe
test among other urine tests available nowadays can presumptively prove the presence of
urine through the identification of creatinine, one of the main components of urine.
However, these tests can’t reliably prove the presence of creatinine and its presence do
not necessarily indicate a urine sample since creatinine may be found in small quantities
in other bodily fluids. Other methods for identifying urine have been reported based on
the detection of urea and creatinine, but because these compounds are commonly found
in animal urine, this detection does not constitute human urine specificity. Many studies
identified 4-heptanone and 2-pentanone as metabolites found exclusively in humans,
thus their presence may indicate a human stain sample. We were able to recover the two
metabolites among others (17 total, 500 µL volume) when analyzing the headspace of
the questioned stain using HS-SPME-GC/MS technique. We also selected
DVB/CAR/PDMS as the fiber of choice since it was able to recover common urinary
metabolites. To assess substrate variability, 4-heptanone recoveries were monitored for
different surfaces urine was spilled on. We found that urine stain found on a T-shirt and
napkin have comparable yields, however swabs were not able to recover the metabolite
in question. Thus, we recommend the usage of the stain sample directly to the HS-
SPME-GC/MS or cutting a piece of the substrate when transportation of heavy material
is an issue. Lastly, R software was used to compare our data spatially to assess their
similarities and differences. We noted that urine samples and blanks were perfectly
32. 26
resolved, suggesting the ability to differentiate the two groups. The overlap between the
two urine groups can suggest that the two groups were very similar despite the absence
of 4-heptanone.
33. 27
REFERENCES
1 Seema Patel, Shadab Ahmad. Emerging field of metabolomics: Big promise for cancer
biomarker identification and drug discovery. Journal of Pharmaceutical and Biomedical
Analysis, 2015, 107, 63-74.
2 C. Denkert, E. Bucher, M. Hilvo, R. Salek, M. Oresic, J. Griffin, et al. Metabolomics of
human breast cancer: new approaches for tumor typing and biomarker discovery.
Genome med. 2012, 4, 37
3 Matsumura. K, Opiekun.M, Oka. H, Vachani. A, Albelda. S, Yamazaki.K, Beauchamp.G.
Urinary volatile compounds as biomarkers for lung cancer: a proof of principle study
using odor signatures in mouse models of lung cancer. Plos ONE. 2009; 5
4 Millis. G, Walker.V. Headspace solid phase microextraction profiling of volatile
compunds in urine: application to metabolic investigations. Journal of chromatography.
2001, 753, 259-268
5 Sean O'Callaghan, David P De Souza, Andrew Isaac, Qiao Wang, Luke Hodkinson, Moshe
Olshansky, Tim Erwin, Bill Appelbe, Dedreia L Tull, Ute Roessner, Antony Bacic, Malcolm J
McConville and Vladimir A Likić, PyMS: a Python toolkit for processing of gas
chromatography-mass spectrometry (GC-MS) data. Application and comparative study
of selected tools, BMC Bioinformatics 2012, 13:115
6 Fiehn O, Extending the breadth of metabolite profiling by gas chromatography coupled
to mass spectrometry. Analytical Chemistry 2008, 27(3):261–269
7 Prinz M, Grellner W, Schmitt C. DNA typing of urine samples following several years of
storage. International Journal of Legal Medicine 1993;106(2):75–9
8 Brinkmann B, Rand S, Bajanowski T. Forensic identification of urine samples.
International Journal of Legal Medicine 1992;105(1):59–61.
9 Nakazono T, Kashimura S, Hayashiba Y, Hara K, Miyoshi A. DNA: Successful typing of
urine stain using a DNA purification kit following dialfiltration. Journal of Forensic Sc.
2005 Jul;50(4):860-4
34. 28
10 Kelly Virkler, Igor K. Lednev Analysis of body fluids for forensic purposes: From
laboratory testing to non-destructive rapid confirmatory identification at a crime scene.
Forensic science international 2009, 188, 1-17
11 AOAC Official Methods of Analysis, 959.14
12 Avinash Krishnegowda et al. Spectrophotometric assay of creatinine in human serum
sample. Arabian journal of chemistry. 2013;
13 Micheal Peake, Malcolm Whiting. Measurement of serum creatinine-current status and
future goals. Clinical Biochemistry, 2006, 27(4): 173–184
14 Zhang.A, Hui.S, Xiuhong.W, Xijun.W. Urine metabolomics. Clinica Chimica Acta. 2012;
414, 65-59
15 Akutsu T, Watanabe K, Sakurada K. Specificity, sensitivity, and operability of RSID™
for forensic identification of urine: Comparison with Elisa for Tamm-Horsfall protein.
Journal of Forensic Sc. 2012 Nov; 57(6)
16 Li Richard. The identification of Urine: Creatinine. Forensic Biology. 2015, 2, p.314-315
17 Yusuda T, et al. A simple method of DNA extraction and STR typing from urine
samples using a commercially available DNA/RNA extraction kit. Journal of Forensic Sc.
2003 Jan;48(1):108-10.
18 Statheropoulos M, et al. Preliminary investigation of using volatile organic compounds
from human expired air, blood, and urine for locating entrapped people in earthquakes.
Journal of Chromatography B. 2015, 822, 112-117
19 T. Nakazono, S. Kashimura,1 Y. Hayashiba, T. Hisatomi,and K. Hara, Identification of
Human Urine Stain by HPLC Analysis of 17-Ketosteroid Conjugates. Journal of Forensic
Sciences. 2002, 43, 568-572
20 Millis. G, Walker.V. Headspace solid-phase microextraction procedures for gas
chromatography analysis of biological fluids and materials. Journal of Chromatography.
2000; 920, 267-287
35. 29
21 Katajamaa.M, Oresic. M. Data processing for mass-spectrometry based metabolomics.
Journal of Chromatography. 2007, 1158, 318-328
22 Millis.G, Walker.V, Mughal. H. Headspace solid-phase microextraction with 1-
pyrenydiazomethane in-fibre derivatisation for analysis of feacal short-chain fatty acids.
Journal of Chromatography. 1999; 730, 113-122
23 Catarina Silva, Carina Cavaco, Rosa Perestrelo, Jorge Pereira and José S. Câmara.
Microextraction by Packed Sorbent (MEPS) and Solid-Phase Microextraction (SPME) as
Sample Preparation Procedures for the Metabolomic Profiling of Urine. Metabolites 2014,
4, 71-97
24 Pawlinszyn J. (2011) Handbook of Solid Phase microextraction. The basics of SPME
25 Aihua Zhang, Hui Sun, Xiuhong Wu, Xijun Wang. Urine metabolomics. Clinica Chimica
Acta 2012, 414, 65–69
26 C L Silva, M Passos and J S Câmara. Investigation of urinary volatile organic metabolites as
potential cancer biomarkers by solid-phase microextraction in combination with gas
chromatography-mass spectrometry. British Journal of Cancer. 2011, 105, 1894–1904.
27 Belda-Iniesta C, de Castro Carpeno J, Carrasco JA, Moreno V, Casado Saenz E, Feliu J,
Sereno M, Garcia Rio F, Barriuso J, Gonzalez Baron M. New screening method for lung
cancer by detecting volatile organic compounds in breath. 2007. Clin Transl Oncol 9: 364–
368
28 D.A. Lambropoulou V.A. Sakkas, T.A. Albanis. Validation of an SPME method, using
PDMS, PA, PDMS–DVB, and CW–DVB SPME fiber coatings, for analysis of
organophosphorus insecticides in natural waters. Anal Bioanal Chem.2002.374 :932–941
