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Journal of The American Society for Mass Spectrometry

, Volume 27, Issue 9, pp 1565–1574 | Cite as

Fatty Acid Structure and Degradation Analysis in Fingerprint Residues

  • Stefanie Pleik
  • Bernhard Spengler
  • Thomas Schäfer
  • Dieter Urbach
  • Steven Luhn
  • Dieter KirschEmail author
Research Article

Abstract

GC-MS investigations were carried out to elucidate the aging behavior of unsaturated fatty acids in fingerprint residues and to identify their degradation products in aged samples. For this purpose, a new sample preparation technique for fingerprint residues was developed that allows producing N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA) derivatives of the analyzed unsaturated fatty acids and their degradation products. MSTFA derivatization catalyzed by iodotrimethylsilane enables the reliable identification of aldehydes and oxoacids as characteristic MSTFA derivatives in GCMS. The obtained results elucidate the degradation pathway of unsaturated fatty acids. Our study of aged fingerprint residues reveals that decanal is the main degradation product of the observed unsaturated fatty acids. Furthermore, oxoacids with different chain lengths are detected as specific degradation products of the unsaturated fatty acids. The detection of the degradation products and their chain length is a simple and effective method to determine the double bond position in unsaturated compounds. We can show that the hexadecenoic and octadecenoic acids found in fingerprint residues are not the pervasive fatty acids Δ9-hexadecenoic (palmitoleic acid) and Δ9-octadecenoic (oleic acid) acid but Δ6-hexadecenoic acid (sapienic acid) and Δ8-octadecenoic acid. The present study focuses on the structure identification of human sebum-specific unsaturated fatty acids in fingerprint residues based on the identification of their degradation products. These results are discussed for further investigations and method developments for age determination of fingerprints, which is still a tremendous challenge because of several factors affecting the aging behavior of individual compounds in fingerprints.

Graphical Abstract

Keywords

Latent fingerprint residues Degradation products GC-MS Aging Monoenoic fatty acids Aldehydes Sapienic acid Δ8-Octadecenoic acid 

Introduction

The demand for age determination of latent fingerprints is high in forensic sciences. The issue has bothered fingerprint experts and forensic scientists severely in the past. First approaches tried to estimate the age of fingerprints based on morphologic changes after development of the trace with adhesion powder [1]. Such changes include sharpness of fingerprint ridge details, clarity, continuity, and contrast, recognized as a loss of “quality.” Typical effects are drying, dulling, narrowing of the papillae, loss of stickiness, loss of continuity, and a poor reaction to adhesion powder. Interpretation of quality changes, however, is difficult with this method, as they may originate from aging or from the formation process.

Age estimation of latent fingerprints is influenced by the original chemical composition of the fingerprint, the amount of residue, the chemical composition of the substrate, and environmental conditions such as temperature, humidity, air pollution, or rainfalls [1, 2, 3]. Baniuk stated in 1990 the importance of reconstructing the aging conditions and of considering skin physiology for a reliable interpretation of the aging behavior. She also proposed comparative examinations of the secured print with the suspect’s print [4]. A number of studies were reported on the effects of temperature and humidity on the permanency of latent prints [2]. In some cases, however, old prints could be developed with adhesion powder just as fresh prints. It is clear from these earlier attempts that new techniques are needed for age determination and analyzing changes in the chemical composition of residues resulting from the aging process.

Dermatologic studies investigated the chemical composition of palmar sweat and sebum [5, 6, 7, 8, 9, 10, 11, 12, 13, 14], allowing the identification of main components of fingerprint residues in early years [15, 16]. For typical compounds, such as fatty acids, triglycerides, and wax esters, condensation, evaporation, oxidation, racemization, absorption, and adsorption were reported as possible aging processes [2].

GC-MS was used in 1999 to identify components of fingerprint residues, including free fatty acids, triglycerides, wax ester, amino acids, glycerol, cholesterol, and squalene [17], followed by several studies combining chromatography, mass spectrometry, and spectroscopic techniques [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]. Gender determination [21], donor classification [21, 22, 23, 24, 25], and aging studies [26, 27, 28, 29, 30, 31, 32] were among the major targets of fingerprint research recently. Francese et al. show in their review article the power of MALDI mass spectrometry for the manifold analyses of fingerprints, even after aging [33].

Merkel et al. developed a method for the age estimation based on optical detectors such as the chromatic white light sensor [30, 32]. An age determination to within 24 h was found to be possible in the course of very fast evaporation processes [30]. They additionally studied morphologic changes in fingerprints after long-term aging and demonstrated a variety of factors that influence the aging behavior [32]. Muramoto et al. analyzed the spatial distribution of endogenous lipids in fingerprints by time-of-flight secondary ion imaging mass spectrometry (TOF-SIMS) and measured the diffusion of saturated lipids as a function of time for a new strategy for potential age dating [36].

Weyermann et al. recorded the aging process of squalene and cholesterol in fingerprint residues by GC-MS, discovering a fast degradation of squalene within a few days besides a slower degradation of cholesterol. They claimed a high intra- and intervariability of the original chemical composition of fingerprint residues as the most important challenge in the age determination of fingerprints. In order to overcome this problem, they used signal intensity ratios between squalene and cholesterol to minimize the high variability in fingerprint composition [28]. Koenig et al. used this approach with several newly identified wax esters in fingerprint residues to study additional compound ratios and their potential use in fingerprint age determination. Using actual cases, they studied the influence of different enhancement techniques on the initial composition of fingerprint residues [37].

The influence of different factors on the aging behavior of lipids in fingerprint residues was a major focus of the work of Girod et al. [38] They studied the aging behavior of different target lipids exposed to selected factors by GC-MS. Factors influencing the aging behavior were distinguished between those that in actual casework are known (donor, substrate, enhancement technique) and unknown (deposition moment, pressure, temperature, lighting). Girod et al. proposed that reliable aging models have to protect against these unknown factors as their influence cannot be determined in real case work samples. By using principal component analysis and univariate regression, they reported that effects arising from the known influence factors (donor, substrate, enhancement technique) are larger than the aging effects itself. Thus, the known influence factors must be included in reliable aging models for fingerprints. Based on soft independent modeling of class analogy analysis (SIMCA) and calculating likelihood ratios, they were able to estimate the age of five out of eight fingerprints correctly by blind analysis as soon as the storage conditions during aging were known. When storage conditions were unknown, the results were not reliable, showing the significant impact of reported influence factors [38]. Additionally, Girod et al. published two detailed review articles about composition of fingermark residue [39] and fingermark age determination, and associated legal considerations and practical propositions [40].

Although reviews of fingerprint research are of high quality and very detailed, they reveal a general lack of degradation product analyses in studies of fingerprint aging [39, 41, 42, 43]. But Mountfort et al. should be named as they identified mono hydroperoxides as degradation products of squalene in solution and in fingerprints [31]. Based on the findings about fingerprint aging and dating methodology published in recent literature, the present study focuses on the identification of degradation products in fingerprints upon aging, including a detailed analysis of the structure and identity of prominent fatty acids in fingerprint residues.

Materials and Methods

Aluminum foil (thickness 15 μm) as substrate for aging experiments was purchased from Carl Roth GmbH, Co. KG, Karlsruhe, Germany. For sample extraction, dichloromethane (SupraSolv; Merck Chemicals GmbH, Schwalbach, Germany) was used. Δ9-Octadecenoic acid (Δ9-C18:1, oleic acid), Δ9-hexadecenoic acid (Δ9-C16:1, palmitoleic acid), tribenzylamine, decanal, and iodotrimethylsilane were purchased from Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany. For derivatization, N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) (Macherey-Nagel GmbH, Co. KG, Düren, Germany) and N-Methyl-N-(trimethyl-D9-silyl)trifluoroacetamide (Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany) were used. Δ6-Hexadecenoic acid (Δ6-C16:1, sapienic acid), Δ8-octadecenoic acid (Δ8-C18:1), and 8-oxooctanoic acid were synthesized by Lipidox (Stockholm, Sweden).

GC-MS analyses were carried out on a 'TRACE GC Ultra' gas chromatograph attached to a TSQ Quantum XLS triple quadrupole mass analyzer (Thermo Scientific, Thermo Fisher Scientific, Inc., Dreieich, Germany) as well as on a gas chromatograph 'TRACE 1310' attached to an ITQ 1100 GC-ion trap mass analyzer (Thermo Scientific, Thermo Fisher Scientific, Inc.), both equipped with electron impact (EI) ion sources. On both systems, spectra were recorded in positive-ion mode with electron energies of 70 eV. Mass spectra were recorded in the mass range of m/z 45–800.

The GC injectors operated with a split ratio of 1:10 at 250 °C. Helium served as a carrier gas with a flow rate of 1 mL/min. For separation of the compounds a 'Zebron ZB-5HT Inferno' GC column (Phenomenex Ltd., Aschaffenburg, Germany, 5% phenyl-, 95% dimethylpolysiloxane, length: 30 m, i.d..: 0.25 mm, film thickness: 0.25 μm) was used on both instruments. The temperature program was started at 70 °C, increasing up to 300 °C at a heating rate of 10 °C/min, followed by an isothermal period of 3 min.

Sample Preparation

  • Fingerprint residues 30 Fingerprint donors (14 female, 16 male) placed their fingertips on pieces of aluminum foil, preceded by so-called fingerprint grooming [26]. For the grooming procedure, donors wiped their fingertips over their forehead, nose, chin, and scalp. Based on the results of the questionnaire, 15 donors use skin care products for either face or hands regularly. During the aging process, the samples were stored in open petri dishes on the lab bench. Conditions regarding temperature, humidity, light, and air exchange were not controlled in order to resemble realistic conditions on a crime scene. As the lab is air-conditioned, the room temperature was 20 °C (±1 °C). Samples were exposed to light during daytime and to darkness during night to mimic typical light exposure. Samples were analyzed after 0, 1, 2, 5, 7, 9, 12, and 14 d of aging. A total of 240 fingerprints were measured.

For extraction of compounds from fingerprint samples, each piece of aluminum foil was folded with tweezers and placed in a glass vial. Samples were extracted twice with 200 μL dichloromethane. Afterwards, the solvent was evaporated under a nitrogen flux of 20 mL/min. For analysis, fingerprint residues were dissolved in 30 μL dichloromethane, 4 μL internal standard tribenzylamine (50 ng/μL in dichloromethane), and 6 μL N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA, containing 1.5 ng/μL iodotrimethylsilane as catalyst). For structure identification, samples were derivatized with 6 μL N-methyl-N-(trimethyl-D9-silyl)trifluoroacetamide (MSTFA-D9, containing 1.5 ng/μL iodotrimethylsilane as catalyst). Closed glass vials containing the reaction mixtures were stored in a laboratory sand bath at 70 °C for 10 min to ensure complete derivatization. Afterwards, these samples were analyzed by GC-MS without any further preparation. Detailed illustration of the applied derivatization method is provided in the Supporting Information S1.
  • Reference material of unsaturated fatty acids For aging experiments using references of Δ6-hexadecenoic acid, Δ8-octadecenoic acid, Δ9-octadecenoic acid, and Δ9-hexadecenoic acid, stock solutions of 10 μg/μL in dichloromethane were prepared; 1:100 dilutions in dichloromethane yielded samples with concentrations of 100 ng/μL. Pieces of aluminum foil (1.5 × 1.5 cm) were coated with 5 μL of each solution. During the aging process, the samples were stored in open petri dishes on the lab bench. After aging, samples were extracted twice with 200 μL dichloromethane. Afterwards, the solvent was evaporated under a nitrogen flux of 20 mL/min. Residues were dissolved in 30 μL dichloromethane, 4 μL internal standard tribenzylamine (50 ng/μL in dichloromethane), and 6 μL MSTFA (containing 1.5 ng/μL iodotrimethylsilane as catalyst). Samples were analyzed after 0 and five d of aging to obtain data from fresh and aged samples.

  • Reference material of decanal and 8-oxooctanoic acid For GC-MS analysis, decanal and 8-oxooctanoic acid were dissolved in dichloromethane to obtain stock solutions of 100 ng/μL. 30 μL of tenfold dilutions were mixed with 6 μL MSTFA (containing 1.5 ng/μL iodotrimethylsilane as catalyst) and 4 μL internal standard tribenzylamine (50 ng/μL in dichloromethane). Vials were placed in a laboratory sand bath at 70 °C for 10 min to ensure complete derivatization. Samples were analyzed by GC-MS without aging procedure.

Results

Degradation of Monoenoic Fatty Acids in Fingerprint Samples

Figure 1 shows the total ion chromatograms of a fresh and a 14 d old fingerprint sample for comparison. Hexadecenoic acid (retention time (RT): 13.74 min.) and octadecenoic acid (RT: 15.43 min.), both showing high signal intensities in the total ion chromatograms of fresh fingerprint samples, were found to degrade significantly within 14 days.
Figure 1

Total ion chromatograms of a fresh fingerprint sample (top) and a 14-d-old fingerprint sample (bottom). Aging conditions: 20 °C room temperature and light influence from a regular day-night cycle. Samples were derivatized with MSTFA before GC-MS analysis. Trimethylsilyl (TMS) and MSTFA derivatives were detected

In general, unsaturated fatty acids with the same aliphatic chain length but different double bond positions cannot be differentiated by chromatographic separation or mass spectrometry. Mass spectra of Δ8-octadecenoic acid and Δ9-octadecenoic acid (TMS derivatives of reference materials) exhibit no significant differences. These isomeric unsaturated fatty acids thus cannot be differentiated by interpreting the corresponding EI mass spectra in fingerprints.

Total ion chromatograms (as shown in Figure 1) and corresponding mass spectra of aged latent fingerprint residues reveal the formation of some short chain degradation products at lower retention times. These degradation products were identified as aldehydes, carboxylic acids, and oxoacids based on characteristic fragment ions after suitable derivatization with MSTFA [44] (Supporting Information S1) and GC-MS analysis. Furthermore, after aging under the influence of light in the laboratory, decanal formed a high abundant signal in the total ion chromatograms of aged fingerprint samples (RT: 10.10 min., Figure 1).

For detection of lowly abundant aliphatic aldehydes, it is essential to use iodotrimethylsilan as a catalyst for MSTFA derivatization [45]. Without such derivatization, mass spectra of aliphatic aldehydes have a very low signal-to-noise ratio and cannot be interpreted unambiguously.

The reaction of decanal with MSTFA, as of all aldehydes, does not form mono-TMS derivatives, but MSTFA derivatives (Figure 2). The highest observable mass in the EI mass spectrum of 2,2,2-trifluoro-N-methyl-N-{1-[(trimethylsilyl)oxy]decyl}acetamide (M = 355 Da) of fingerprint samples is m/z 340, corresponding to the [M-15]+ ion (Figure 2). This peak is formed by a characteristic α-cleavage of a methyl group from a TMS-group, as expected. The fragment ions at m/z 228, 184, 134 and 110 are characteristic for spectra of aliphatic aldehydes, derivatized with MSTFA [44]. Reference material of decanal was analyzed by GC-MS under the same parameters as used for analysis of the latent fingerprint residues, to confirm the proposed structures (see Supporting Information Figure S2a). The structures were additionally verified by derivatization with deuterated MSTFA (see Supporting Information Figure S1e). Comparing mass spectra of MSTFA-H9- and MSTFA-D9-derivatized samples provided important information about the structures of the compounds and of their fragment ions [46]. The advantage of this method is a direct allocation of TMS and DMS (dimethylsilyl) groups. This approach facilitates structure elucidation of unknown compounds and provides reliable identification of structures (D9-spectra are provided in the Supporting Information Figure S1eS1g).
Figure 2

Mass spectrum of 2,2,2-trifluoro-N-methyl-N-{1-[(trimethylsilyl)oxy]decyl}acetamide obtained from a fingerprint sample with proposed structures of the presented fragments

Besides decanal, the oxoacids 6-oxohexanoic acid (RT after derivatization: 10.62 min, Figure 1) and 8-oxooctanoic acid (RT after derivatization: 12.54 min, Figure 1) were identified in aged fingerprint samples. Mass spectra of the two MSTFA-derivatized compounds were obtained from a fingerprint sample and are presented in Figures 3 and 4. MSTFA-specific fragment ions were obtained in both spectra, identical to signals in the decanal spectrum (m/z 228, 110, 134, 184). Based on these signals, the compounds can be immediately identified as aldehydes or oxoacids. Several fragment ions include the fatty acid and thus indicate its chain length (m/z 246, 259, 275, and 386 in the case of 6-oxohexanoic acid; m/z 274, 287, 303, and 414 in the case of 8-oxooctanoic acid). Identification of these compounds and proposed structures of the fragment ions were verified by MSTFA-D9 derivatization (D9-spectra are provided in the Supporting Information Figure S1f and S1g). Additionally, the reference material of 8-oxooctanoic acid was analyzed by GC-MS to confirm the proposed structures (spectrum is provided in the Supporting Information Figure S2b).
Figure 3

Mass spectrum of trimethylsilyl-6-[methyl(trifluoroacetyl)amino]-6-[(trimethylsilyl)oxy]hexanoate from a fingerprint sample with proposed structures of the presented fragment ions

Figure 4

Mass spectrum of trimethylsilyl-8-[methyl(trifluoroacetyl)amino]-8-[(trimethylsilyl)oxy]octanoate from fingerprint sample with proposed structures of the presented fragment ions

The discussed target compounds were analyzed in the 240 fingerprint samples of different ages to prove reproducibility of obtained results and to monitor the signal intensities for fatty acids and identified degradation products. Table 1 presents the number of samples at each analyzed age that exhibit significant signal intensities for discussed target compounds. At 0 d aging, no degradation products could be detected, but fatty acids were analyzed in most of the samples. In two samples, the total signal intensity was too low for reliable detection of any target compounds. At 1 d aging, the degradation products can be detected in addition to the fatty acids. With increasing age, signal intensities for degradation products increase, whereas signal intensities for the fatty acids decrease. For aged fingerprint residues, the number of samples containing detectable amounts of the fatty acids decrease significantly. Reasons can be poor quality of the original fingerprint sample or rapid degradation of the compounds. After 14 d aging, only in four fingerprint samples out of 30 analyzed samples, the fatty acids are detectable by using the reported method. Degradation products can still be analyzed in 14 samples. The quantitative decrease and, respectively, increase of compound signal intensities in course of aging is affected massively by a number of influence factors. Thus, the illustration of time-dependent aging curves for individual compounds is not significant without consideration of relevant influence factors.
Table 1

Number of Samples at Each Analyzed Age of Fingerprint Samples that Exhibit Significant Signal Intensities for Discussed Target Compounds. For Each Fingerprint Age, 30 Samples Were Analyzed. The Percentage of Samples with Detectable Amounts is Specified in Parentheses

Age / d

Hexadecenoic acid

Octadecenoic acid

Oxohexanoic acid

Oxooxooctanoic acid

Decanal

0

29 (97%)

29 (97%)

0 (0%)

0 (0%)

0 (0%)

1

22 (73%)

22 (73%)

22 (73%)

22 (73%)

22 (73%)

2

19 (63%)

19 (63%)

19 (63%)

19 (63%)

19 (63%)

5

14 (47%)

14 (47%)

29 (97%)

29 (97%)

29 (97%)

7

11 (37%)

13 (43%)

18 (60%)

28 (93%)

28 (93%)

9

8 (27%)

8 (27%)

17 (57%)

24 (80%)

24 (80%)

12

5 (17%)

5 (17%)

20 (67%)

28 (93%)

28 (93%)

14

4 (13%)

4 (13%)

14 (47%)

13 (43%)

14 (47%)

After identification of the degradation products in aged fingerprints and the proof of reproducibility, we determined the course of the corresponding reaction during fingerprint aging. As already mentioned, unsaturated fatty acids, as main components of fresh fingerprint residues, degrade in the course of the aging of fingerprints. According to the known autoxidation process [47] of unsaturated compounds, it is assumed that the identified aldehydes and oxoacids in aged fingerprints are the products of an autoxidation process of the unsaturated fatty acids. For this reason, nonanal and 9-oxononanoic acid were expected as autoxidation products of the supposed Δ9-octadecenoic acid. Accordingly, heptanal and 9-oxononanoic acid were expected as autoxidation products of Δ9-hexadecenoic acid. Instead, decanal, 8-oxooctanoic acid, and 6-oxohexanoic acid were detected as the main degradation compound in aged fingerprint residues (Figure 1), indicating that the supposed double bond positions in the detected unsaturated fatty acids are wrong.

For confirmation of this observation, aging experiments with reference material of unsaturated fatty acids were carried out to elucidate their aging behavior and to explain the origin of the detected compounds in fingerprint residues. Decanal was found as a degradation product of Δ6-hexadecenoic acid and Δ8-octadecenoic acid in aged reference material (Figure 5a, b). 6-Oxohexanoic acid was found in the aged sample of Δ6-hexadecenoic acid (Figure 5a) and 8-oxooctanoic acid was found in the aged sample of Δ8-octadecenoic acid (Figure 5b). These three compounds were detected in high concentrations in aged fingerprint samples (Figure 5a, b, red marked TIC m/z 45–800). These results justify the assumption that the original fatty acids in fingerprint residues are Δ6-hexadecenoic acid and Δ8-octadecenoic acid, rather than Δ9-hexadecenoic acid and Δ9-octadecenoic acid, as stated in some literature [17, 28]. (For the proof of the absence of degradation products in the non-aged unsaturated fatty acid sample from reference material see the Supplementary Figure S3a in the Supporting Information.)
Figure 5

(a) Extracted ion chromatograms for Δ6-hexadecenoic acid (first row) and their corresponding degradation products from reference material after 5 d aging (third row) are presented (black). The second row presents the total ion chromatogram of an aged fingerprint sample (red). (b) Extracted ion chromatograms for Δ8-octadecenoic acid (first row) and their corresponding degradation products from reference material after 5 d aging (third row) are presented (black). The second row presents the total ion chromatogram of an aged fingerprint sample (red)

The degradation products of the ubiquitous Δ9-hexadecenoic acid aged reference material were identified as heptanal and 9-oxononanoic acid by GC-MS (Figure 6a). In aged samples of the ubiquitous Δ9-octadecenoic acid, nonanal and 9-oxononanoic acid were detected (Figure 6b). In aged fingerprints, traces of nonanal were detected, indicating the presence of small amounts of Δ9-octadecenoic acid in fresh fingerprint residues. Δ9-Octadecenoic acid cannot be separated from Δ8-octadecenoic acid as both acids have the same retention time under the conditions used. It is assumed that these small amounts of Δ9-octadecenoic acid as well as Δ9-hexadecenoic acid may originate from environmental contaminants (e.g., skin care products).
Figure 6

(a) Extracted ion chromatograms for Δ9-hexadecenoic acid (first row) and their corresponding degradation products from reference material after 5 d aging (second row) are presented. (b) Extracted ion chromatograms for Δ9-octadecenoic acid (first row) and their corresponding degradation products from reference material after 5 d aging (second row) are presented

In addition to the degradation products of unsaturated fatty acids, the oxidation products of the aldehyde decanal and the oxoacids were identified by GC-MS. The oxidation of decanal yielded the monocarboxylic acid decanoic acid (TMS derivative RT: 7.69 min, Figure 1). Oxidation products of the oxoacids were determined as the corresponding dicarboxylic acids hexanedioic acid (TMS derivative RT: 8.33 min, Figure 1) and octanedioic acid (TMS derivative RT: 10.50 min, Figure 1). Compounds were identified by GC-MS and data base search (spectra are provided in the Supporting Information Figure S4a–c).

Discussion

Literature claims that Δ9-hexadecenoic acid and Δ9-octadecenoic acid are two compounds with high concentration in fingerprint residues [17, 28]. Identification of the unsaturated fatty acids in fingerprint residues by GC-MS and database search, however, often leads to misinterpretation. Comparing mass spectra of Δ8- and Δ9-octadecenoic acid, typically no differences are observed between the two. The double bond position of unsaturated fatty acids thus cannot be determined by EI mass spectra. Derivatization and analysis of their degradation products instead provide for a reliable identification of structures.

For Δ9-octadecenoic acid as well as for Δ9-hexadecenoic acid, decanal was not expected as a degradation product, as their double bond positions suggest nonanal as a degradation product of Δ9-octadecenoic acid and heptanal as a degradation product of Δ9-hexadecenoic acid. The fact that decanal was actually detected suggests that the hexadecenoic and the octadecenoic acid in fingerprint residues are in fact not the ubiquitous acids Δ9-hexadecenoic acid and Δ9-octadecenoic acid but the isomeric compounds Δ6-hexadecenoic acid and Δ8-octadecenoic acid. Δ6-Hexadecenoic acid is known as a human sebum-specific fatty acid, and Michalski et al. expected it to be present in fingerprint residue [20]. It constitutes more than 25% of the entire human sebum composition [48].

Destaillats et al. describe several Δ6-monounsaturated fatty acids in human hair and nail samples [48], which are generated by the enzyme Δ6-desaturase [49]. In a so-called “sebaceous type” reaction, Δ6-desaturase converts hexadecanoic acid into Δ6-hexadecenoic acid via desaturation [49]. It can be assumed that octadecanoic acid undergoes the same enzymatic reaction with Δ8-desaturase to form Δ8-octadecenoic acid, but no reference was found to prove the activity of Δ8-desaturase in human sebum. Counted from the position distal to the carboxyl carbon atom (ω-site), both unsaturated fatty acids exhibit the same double bond position. Δ6-Hexadecenoic acid can therefore be named as ω-10-hexadecenoic acid and Δ8-octadecenoic as ω-10-octadecenoic acid.

In the present study, we could show that analysis of autoxidation products of unsaturated fatty acids is an effective and simple method for determination of double bond positions of unsaturated fatty acids, using the MSTFA derivatization technique and low resolution mass spectrometry.

For the degradation of both Δ6-hexadecenoic acid and Δ8-octadecenoic acid, decanal is expected and detected as a degradation product. Additionally, the aging of Δ6-hexadecenoic acid yields 6-oxohexanoic acid, and the aging of Δ8-octadecenoic acid yields 8-oxooctanoic acid, both detected in aged fingerprint residues. The high signal intensity of decanal, compared with the intensities of 6-oxohexanoic acid and 8-oxooctanoic acid in aged fingerprint samples, indicate the formation of decanal from both fatty acids and probably from additional unsaturated lipids. The unsaturated fatty acids are detected as initial compounds in non-aged fingerprint samples. After 5 d aging, in 47% of the analyzed fingerprint samples the unsaturated fatty acids are still detectable. Within 14 d aging after deposition, the unsaturated fatty acids can be detected in only 13% of the analyzed samples. In general, it can be stated that the discussed fatty acids are not detectable in aged fingerprint residues after 14 d aging in most of the analyzed samples. Only in very rich samples, the fatty acids can still be detected after 14 d aging by the reported method.

Elucidation of the aging process of unsaturated fatty acid reference samples is in accordance with the degradation products found in aged fingerprint samples. The determined type of reaction also clarifies why no degradation products of squalene can be detected by GC-MS. Squalene is the main compound of fresh fingerprint residues, degrading completely within a few days. Assuming that every double bond of this highly unsaturated hydrocarbon compound is cleaved, only low molecular weight volatile compounds are formed, which cannot be detected by GC-MS after liquid extraction of aged fingerprint residues.

Aging experiments performed with reference material of the concerned fatty acids confirmed the identification of Δ6-hexadecenoic acid and Δ8-octadecenoic acid and degradation products in fingerprint residues after derivatization with deuterated MSTFA. Finally, the double bond positions of the unsaturated fatty acids in fingerprints were determined. Position analysis of double bonds in aliphatic structures is always a challenge. The results show that oxidation by atmospheric oxygen or other suitable oxidants can be used for analysis.

The described catalyzed MSTFA derivatization method in combination with GC-MS analysis is particularly suitable for the sensitive detection of unsaturated fatty acids and related compounds. Derivatization does not only improve the chromatographic performance but also enhances the identification of these compounds. Without catalyzed MSTFA derivatization, neither the aldehydes nor the oxoacids could be detected and identified in aged fingerprint samples. Due to poor chromatographic performance, aldehydes could not be detected in the TIC diagrams with sufficient intensities. Mass spectra of non-derivatized even-numbered oxoacids and those of odd-numbered saturated fatty acids do not differ in low resolution mass spectrometry because CH2–CH3 and CH=O have the same nominal mass. Thus, these compounds are difficult to distinguish without MSTFA derivatization.

Odd-numbered saturated fatty acids are actually found in fingerprint spectra as lipids from skincare products. It might also be conceivable that in addition to the observed oxidation process, odd-numbered fatty acids indicate microbial degradation of long chain fatty acids [50]. In order to consider microbial action in fingerprint residues, it should be mentioned that the breakdown of triglycerides and wax esters may result in the production of free fatty acids in aged fingerprint samples. But as most of the bacteria need special conditions to be active, this aging process can be neglected for fingerprint aging. Nevertheless, unsaturated lipids like triglycerides and wax esters provide additional targets for the discussed degradation process. It should be considered that detected aldehydes can also result from the oxidation of unsaturated triglycerides and wax esters. Thus, the amount of these lipids has a direct impact on the amount of aldehydes that can be detected in aged fingerprints residues. Therefore, the identified oxoacids 6-oxohexanoic and 8-oxooctanoic acid are best suited as potential aging markers in fingerprint residues as they originate only from free fatty acid oxidation.

In general, we report a fast degradation of unsaturated fatty acids in fingerprint residues, but upon closer examination we observed different aging curves associated with changing influences. A very fast degradation can be explained by poor initial composition or by particular aging conditions. In some cases, target compounds cannot be detected because of the poor quality of the analyzed fingerprint. Protective substances, such as skin care products, may explain slower degradation of some samples. As we already know from literature, the initial composition of fingerprints, substrate material, and storage conditions affect the aging behavior enormously.

For this reason, the rapid degradation of discussed fatty acids within 14 d after deposition in most of the analyzed aged fingerprint samples disagree with some results from literature. Mong et al. reported significant degradation of unsaturated fatty acids after 60 d of exposure to air [17]. In this study, fingerprint residues were deposited on filter paper for aging studies, touched twice by the donors to enrich sebaceous material, and stored packed in foil and protected from light. These circumstances explain the slow degradation of unsaturated fatty acids compared with our results, as our samples were deposited on aluminum foil and were stored without any protection to light and air.

Archer et al. discussed time-dependent changes in the lipid composition of fingerprint residue in order to estimate the age of fingerprints. Their analyses revealed an increase of signal intensities of saturated and unsaturated fatty acids in aged fingerprint samples within 20 d after deposition [26]. They hypothesized these results were a result of microbial degradation of triglycerides and wax esters producing free fatty acids. Their fingerprint residues were deposited on filter paper and samples were stored in covered boxes placed in an incubator at 25 °C. These storage conditions are the reason for the lack of oxidation processes in their fingerprint samples as the target compounds were protected from air. Reported conditions seem to favor microbial degradation of fingerprint ingredients and thus result in the increase of signal intensities of fatty acids instead of a decrease, as we reported in this study.

But we should consider the possibility of different degradation processes occurring simultaneously in fingerprint residues. In this study, no aging curves of discussed target compounds are presented because influencing factors on observed degradation process should be further investigated. Girod et al. proposed providing fingerprint aging models based on known influence factors to be able to interpret results from aged samples unambiguously. Their study is a promising approach to overcome the strong influences of a variety of factors on the aging behavior of fingerprints. Our study gives essential new information on the chemical composition of aged fingerprint residues. The most important and crucial advantage of our findings is the relative signal intensity value based on the aging of one single and unique fingerprint ingredient which is independent from initial fingerprint composition and absolute amounts. Ratios between the sebum-specific unsaturated fatty acid and its natural oxidation product provide a relative value based on the individual degradation of one single and unique compound in fingerprint residues.

Compared with the approach of Girod et al. [38], which uses signal intensities of multiple initial compounds for age estimation of fingerprints, we are able to generate a relative aging value based on the degradation of one single initial compound. Identified fatty acid degradation products are powerful markers in aged fingerprint residues and important for the development of a reliable method for age estimation of fingerprints. For application in crime scene investigations, a number of factors influence the aging behavior of individual compounds and must be considered. As already known from literature, the initial compositions of fingerprint residues have a substantial impact on the aging process of target compounds. Aging conditions, including temperature, humidity, air exchange, etc., are additional unknown factors that affect the aging enormously. Currently, time-dependent aging curves of discussed target compounds are not significant for age determination of fingerprints in case works as long as the mentioned influence factors are not considered.

Keeping the discussed limitations in mind, the results presented here are promising for the development of a robust analytical method for age estimation of fingerprints based on detection of natural oxidation products. Δ6-Hexadecenoic acid, Δ8-octadecenoic acid, and their natural degradation products are important targets for age estimation of latent fingerprints since they are unique human sebum-specific compounds [51]. The initial unsaturated fatty acids degrade quickly in the course of aging and are not detectable anymore after 14 d aging in most of the analyzed samples. Identification of the discussed degradation products provides the generation of relative aging values based on the degradation of single and unique initial fingerprint ingredients, for the first time, and is of significance for age estimation of fingerprints. Thus, degradation studies of related unsaturated lipids in fingerprint residues will be a subject for further investigations.

Notes

Acknowledgments

The authors gratefully acknowledge financial support by KT12 and KI32 at Federal Criminal Police Office, Wiesbaden, Germany.

Supplementary material

13361_2016_1429_MOESM1_ESM.docx (744 kb)
ESM 1 (DOCX 744 kb)

References

  1. 1.
    Holyst, B.: Kriminalistische Abschätzung des Spurenalters bei Fingerpapillarlinien. Arch. Kriminol. 179, 94–103 (1987)Google Scholar
  2. 2.
    Barnett, P.D., Berger, R.A.: The effects of temperature and humidity on the permanency of latent fingerprints. J. Forensic Sci. Soc. 16, 249–254 (1976)CrossRefGoogle Scholar
  3. 3.
    Olsen, R.D.: Chemical dating techniques for latent fingerprints: a preliminary report. Identif. News 1, 10–12 (1987)Google Scholar
  4. 4.
    Baniuk, K.: Determination of age of fingerprints. Forensic Sci. Int. 46, 133–137 (1990)CrossRefGoogle Scholar
  5. 5.
    Mackenna, R.M.B., Wheatley, V.R., Wormall, A.: The composition of the surface skin fat ('sebum') from the human forearm. J. Invest. Dermatol. 15, 33–47 (1950)CrossRefGoogle Scholar
  6. 6.
    Wheatley, V.R.: Studies of sebum—the estimation of squalene in sebum and sebum-like materials. Biochem. J. 55, 637–640 (1950)CrossRefGoogle Scholar
  7. 7.
    Wheatley, V.R.: Studies of sebum–5. The composition of some sebum-like materials of human origin. Biochem. J. 58, 167–172 (1952)CrossRefGoogle Scholar
  8. 8.
    Hodgson-Jones, I.S., Wheatley, V.R.: Studies of sebum—methods for the collection and estimation of small amounts of sebum. Biochem. J. 52, 460–464 (1952)CrossRefGoogle Scholar
  9. 9.
    Mackenna, R.M.B., Wheatley, V.R., Wormall, A.: Studies of sebum—some constituents of the unsaponifiable matter of human sebum. Biochem. J. 52, 161–168 (1952)CrossRefGoogle Scholar
  10. 10.
    James, A.T., Wheatley, V.R.: Studies of sebum—the determination of the component fatty acids of human forearm sebum by gas-liquid gaschromatography. Biochem. J. 63, 269–273 (1954)CrossRefGoogle Scholar
  11. 11.
    Boughton, B., Hodgson-Jones, I.S., Mackenna, R.M.B., Wheatley, V.R., Wormall, A.: Some observations on the nature, origin and possible function of the squalene and other hydrocarbons of human sebum. J. Invest. Dermatol. 9, 179–190 (1954)Google Scholar
  12. 12.
    Wheatley, V.R., James, A.T.: Studies of sebum–7. The composition of the sebum of some common rodents. J. Invest. Dermatol. 65, 36–42 (1956)Google Scholar
  13. 13.
    Boughton, B., Mackenna, R.M.B., Wheatley, V.R., Wormall, A.: Studies of sebum–8. Observations on the squalene and cholesterol content and the possible functions of squalene in human sebum. Biochem. J. 66, 32–38 (1957)CrossRefGoogle Scholar
  14. 14.
    Boughton, B., Wheatley, V.R.: Studies of sebum–9. Further studies on the composition of the unsaponifiable matter. Biochem. J. 73, 144–149 (1957)CrossRefGoogle Scholar
  15. 15.
    Olsen, R.D.: The chemical composition of palmar sweat. Fingerprint Identif. Mag. 53, 3–23 (1972)Google Scholar
  16. 16.
    Wilkinson, D.I., Karasek, M.A.: Skin lipids of a normal and a mutant (asebic) mouse strain. J. Invest. Dermatol. 47, 449–455 (1966)CrossRefGoogle Scholar
  17. 17.
    Mong, G.M., Petersen, C.E., Clauss, T.R.W.: Advanced fingerprint analysis project— fingerprint constituents. Pacific Northwest National Laboratory, Richland (1999)Google Scholar
  18. 18.
    Croxton, R.S., Baron, M.G., Butler, D., Kent, T., Sears, V.G.: Variation in amino acid and lipid composition of latent fingerprints. Forensic Sci. Int. 199, 93–102 (2010)CrossRefGoogle Scholar
  19. 19.
    Croxton, R.S., Baron, M.G., Butler, D., Kent, T., Sears, V.G.: Development of a GC-MS method for the simultaneous analysis of latent fingerprint components. J. Forensic Sci. 51, 1329–1333 (2006)CrossRefGoogle Scholar
  20. 20.
    Michalski, S., Shaler, R., Dorman, F.L.: The evaluation of fatty acid ratios in latent fingermarks by gas chromatography/mass spectrometry (GC/MS) Analysis. J. Forensic Sci. 58, 215–220 (2013)CrossRefGoogle Scholar
  21. 21.
    Asano, K.G., Bayne, C.K., Horsman, K.M., Buchanan, M.V.: Chemical composition of fingerprints for gender determination. J. Forensic Sci. 47, 805–807 (2002)CrossRefGoogle Scholar
  22. 22.
    Buchanan, M.V., Asano, K., Bohanon, A.: Chemical characterization of fingerprints from adults and children. Forensic Evid. Anal. Crime Scene Investig. Proc. SPIE 2941(89), 89–95 (1997)CrossRefGoogle Scholar
  23. 23.
    Girod, A., Weyermann, C.: Lipid composition of fingermark residue and donor classification using GC/MS. Forensic Sci. Int. 238, 68–82 (2014)CrossRefGoogle Scholar
  24. 24.
    De Puit, M., Ismail, M., Xu, X.: LCMS analysis of fingerprints, the amino acid profile of 20 donors. J. Forensic Sci. 59, 364–370 (2014)CrossRefGoogle Scholar
  25. 25.
    Antoine, K.M., Mortazavi, S., Miller, A.D., Miller, L.M.: Chemical differences are observed in children’s versus adults’ latent fingerprints as a function of time. J. Forensic Sci. 55, 513–518 (2010)CrossRefGoogle Scholar
  26. 26.
    Archer, N.E., Charles, Y., Elliott, J.A., Jickells, S.: Changes in the lipid composition of latent fingerprint residue with time after deposition on a surface. Forensic Sci. Int. 154, 224–239 (2005)CrossRefGoogle Scholar
  27. 27.
    Popa, G., Potorac, R., Preda, N.: Method for fingerprints age determination. J. Leg. Med. 18, 149–154 (2010)Google Scholar
  28. 28.
    Weyermann, C., Roux, C., Champod, C.: Initial results on the composition of fingerprints and its evolution as a function of time by GC/MS analysis. J. Forensic Sci. 56, 102–8 (2011)CrossRefGoogle Scholar
  29. 29.
    Koenig, A., Girod, A., Weyermann, C.: Identification of wax esters in fingermark residues by GC/MS and their potential use as aging paramenter. J. Forensic Identif. 61, 606–630 (2011)Google Scholar
  30. 30.
    Merkel, R., Gruhn, S., Dittmann, J., Vielhauer, C., Bräutigam, A.: On noninvasive 2D and 3D chromatic white light image sensors for age determination of latent fingerprints. Forensic Sci. Int. 222, 52–70 (2012)CrossRefGoogle Scholar
  31. 31.
    Mountfort, K.A., Bronstein, H., Archer, N., Jickells, S.M.: Identification of oxidation products of squalene in solution and in latent fingerprints by ESI-MS and LC/APCI-MS. Anal. Chem. 79, 2650–2657 (2007)CrossRefGoogle Scholar
  32. 32.
    Merkel, R., Otte, K., Clausing, R., Dittmann, J., Vielhauer, C.: First investigation of latent fingerprints long-term aging using chromatic white light sensors, pp. 95–104. ACM, New York (2013)Google Scholar
  33. 33.
    Francese, S., Bradshaw, R., Ferguson, L.S., Wolstenholme, R., Clencha, M.R., Bleay S.: Beyond the ridge pattern: multi-informative analysis of latent fingermarks by MALDI mass spectrometry. Analyst 138, 4215–28 (2013)Google Scholar
  34. 34.
    Duff, J.M., Menzel, E.R.: Laser-assisted thin-layer chromatography and luminescence of fingerprints: an approach to fingerprint age determination. J. Forensic Sci. 23, 129–134 (1978)CrossRefGoogle Scholar
  35. 35.
    Dikshitulu, Y.S., Prasad, L., Pal, J.N., Rao, C.V.N.: Aging studies on fingerprint residues using thin-layer and high performance liquid chromatography. Forensic Sci. Int. 31, 261–266 (1986)CrossRefGoogle Scholar
  36. 36.
    Muramoto, S., Sisco, E.: Strategies for potential age dating of fingerprints through the diffusion of sebum molecules on a nonporous surface analyzed using time-of-flight secondary ion mass spectrometry. Anal. Chem. 87, 8035–8 (2015)CrossRefGoogle Scholar
  37. 37.
    Koenig, A., Girod, A., Weyermann, C.: Identification of wax esters in fingermark residues by GC/MS and their potential use as aging parameters. J. Forensic Identif. 61, 1–24 (2011)Google Scholar
  38. 38.
    Girod, A., Spyratou, A., Holmes, D., Weyermann, C.: Aging of target lipid parameters in fingermark residue using GC/MS: effects of influence factors and perspectives for dating purposes. Sci. Justice 56, 165–180 (2016)CrossRefGoogle Scholar
  39. 39.
    Girod, A., Ramotowski, R., Weyermann, C.: Composition of fingermark residue: a qualitative and quantitative review. Forensic Sci. Int. 223, 10–24 (2012)CrossRefGoogle Scholar
  40. 40.
    Girod, A., Ramotowski, R., Lambrechts, S., Misrielal, P., Aalders, M., Weyermann, C.: Fingermark age determinations: legal considerations, review of the literature and practical propositions. Forensic Sci. Int. 262, 212–226 (2016)CrossRefGoogle Scholar
  41. 41.
    Wertheim, K.: Fingerprint age determination: is there any hope? J. Forensic Identif. 53, 42–49 (2003)Google Scholar
  42. 42.
    Hazarika, P., Russell, D.A.: Advances in fingerprint analysis. Angew. Chem. Int. Ed. 51, 3524–3531 (2012)CrossRefGoogle Scholar
  43. 43.
    Ramotowski R.S.: Chapter 3: Composition of latent print residues. In: Advances in fingerprint technology. Lee, H.C., Gaensslen, R.E. (Eds.). CRC Press, Boca Raton (2001)Google Scholar
  44. 44.
    Ende, M.: [Unerwartete Reaktionsprodukte von MSTFA mit Aldehyden.] Tetrahedron 40, 5167–5170 (1984)Google Scholar
  45. 45.
    Olah, G.A., Narang, S.C.: Iodotrimethylsilane. Tetrahedron 38, 2225–2277 (1982)CrossRefGoogle Scholar
  46. 46.
    Urbach, D.: Improved detection and identification using MSTFA/MSTFAd9 derivatization. Reporter U.S. (2012)Google Scholar
  47. 47.
    Frankel, E.N.: Lipid oxidation. Prog. Lipid Res. 19, 1–22 (1980)CrossRefGoogle Scholar
  48. 48.
    Destaillats, F., Guitard, M., Cruz-Hernandez, C.: Identification of Δ6-monounsaturated fatty acids in human hair and nail samples by gas-chromatography-mass-spectrometry using ionic-liquid coated capillary column. J. Chromatogr. A 1218, 9384–9389 (2011)CrossRefGoogle Scholar
  49. 49.
    Ge, L., Gordon, J.S., Hsuan, C., Stenn, K., Prouty, S.M.: Identification of the D-6 desaturase of human sebaceous glands: expression and enzyme activity. J. Invest. Dermatol. 120, 707–714 (2003)CrossRefGoogle Scholar
  50. 50.
    Swann, L.M., Forbes, S.L., Lewis, S.W.: Analytical separations of mammalian decomposition products for forensic science: a review. Anal. Chim. Acta 682, 9–22 (2010)CrossRefGoogle Scholar
  51. 51.
    Picardo, M., Ottaviani, M., Camera, E., Mastrofrancesco, A.: Sebaceous gland lipids. Dermatoendocrinology 1, 68–71 (2009)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2016

Authors and Affiliations

  • Stefanie Pleik
    • 1
    • 2
  • Bernhard Spengler
    • 1
  • Thomas Schäfer
    • 2
  • Dieter Urbach
    • 2
  • Steven Luhn
    • 2
  • Dieter Kirsch
    • 2
    Email author
  1. 1.Institute of Inorganic and Analytical ChemistryJustus Liebig University GiessenGiessenGermany
  2. 2.Federal Criminal Police Office, Forensic Science InstituteWiesbadenGermany

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