Determining Gender by Raman Spectroscopy of a Bloodstain

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Determining Gender by Raman Spectroscopy of a Bloodstain Aliaksandra Sikirzhytskaya, Vitali Sikirzhytski, and Igor K. Lednev Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02986 • Publication Date (Web): 23 Dec 2016 Downloaded from http://pubs.acs.org on December 27, 2016

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Analytical Chemistry

Determining Gender by Raman Spectroscopy of a Bloodstain Aliaksandra Sikirzhytskaya, Vitali Sikirzhytski†, Igor K. Lednev* Department of Chemistry, University at Albany, SUNY, 1400 Washington Ave., Albany, NY 12222 * Email: [email protected], Phone: 518-591-8863, Fax: 518-442-3462

ABSTRACT: The development of novel methods for forensic science is a constantly growing area of modern analytical chemistry. Raman spectroscopy is one of a few analytical techniques capable of nondestructive and nearly instantaneous analysis of a wide variety of forensic evidence, including body fluid stains, at the scene of a crime. In this proof-of-concept study, Raman microspectroscopy was utilized for gender identification based on dry bloodstains. Raman spectra were acquired in mapping mode from multiple spots on a bloodstain to account for intrinsic sample heterogeneity. The obtained Raman spectroscopic data showed highly similar spectroscopic features for female and male blood samples. Nevertheless, support vector machines (SVM) and artificial neuron network (ANN) statistical methods applied to the spectroscopic data allowed for differentiating between male and female bloodstains with high confidence. More specifically, the statistical approach based on a genetic algorithm (GA) coupled with an ANN classification showed approximately 98% gender differentiation accuracy for individual bloodstains. These results demonstrate the great potential of the developed method for forensic applications, although more work is needed for method validation. When this method is fully developed, a portable Raman instrument could be used for the infield identification of traces of body fluids and to obtain phenotypic information about the donor, including gender and race, as well as for the analysis of a variety of other types of forensic evidence.

composition of blood is different for men and women20-27. Women's blood contains 20 percent fewer red blood cells23. A dominance of the pregnancy zone protein in women over men has also been reported24, 28, 29. In addition, women’s plasma contains twice as much coagulation factor FV, alpha1antitrypsin and beta2-microglobulin as men’s plasma and 1.52-fold greater amounts of the complement factors H and C4B24. Conversely, men’s plasma has 2-fold greater protein Zdependent protease inhibitor and Fc binding protein, 3-fold greater protein S100, 4-fold higher phosphatidylinositol glycan-specific phospholipase and 14-fold higher transgelin-224. Koh et al. showed that the amount of hemoglobin, hematocrit, cholesterol, alkaline phosphatase, and α-1 and γ-globulins is different in males and females27. Evidence for the differences between genders can be observed in the amount of lipoproteins, the main cholesterol-carrying particles in human plasma, which can be detected enzymatically30. Raman spectroscopy is a powerful tool for probing the total composition of complex (bio)chemical samples3, 31-35. There have been significant and successful recent efforts to develop a portable Raman instrument for a variety of infield applications, including crime scene investigations3-5, 36-41. Forensic applications of Raman spectroscopy include the identification of fibers42, drugs43, lipsticks44, 45, paint46, gunshot residue7, 47-51, bones52, 53, and condom lubricant54. Our laboratory has developed a new approach based on Raman microspectroscopy combined with advanced statistics for characterizing dry traces of body fluids55-61. The approach is based on multidimensional spectroscopic signatures, taking into account the sample heterogeneity and possible variations with respect to the donor4. In

Introduction Suspect profiling at the very early stages of a criminal investigation plays a significant role and could affect the duration and outcome of the overall investigation1. Recent advancements in analytical chemistry have led to exciting opportunities in the detection and characterization of trace evidence at a crime scene2-9. As the source of individual DNA evidence, biological stains have always been important targets of forensic scientists10-12. Although significant effort has focused on miniaturizing DNA analysis and bringing it to the scene of a crime13, the method remains expensive and time-consuming and must be performed in the laboratory. Very few attempts have been made to date to develop infield methods that provide specific information about the individuals involved, which could be used for immediate suspect profiling3, 9, 14. Halamek and co-workers recently developed an analytical assay for determining gender based on the analysis of the fingerprint of the amino acid composition15-16. The ability to determine gender from traces of body fluids at the scene of a crime would further advance the infield capabilities of crime scene investigation. Traces of human blood are one of the most important types of biological evidence originating from a violent crime. It is well established that men and women differ constitutionally in many ways, including their chromosomal pattern, skeletal structure, average organ sizes, and physiology17, 18. Menstruation, pregnancy, and lactation influence behaviour and contribute to physiological differences between men and women17-19. Most important for this study is that the biochemical

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tion about 2 cm-1. The fingerprint region between 625-1730 cm-1 was used for further analysis. A silicon standard was used for the calibration. Data treatment and analysis The original Raman spectra were imported into MATLAB 7.11 (MathWorks, Natick, MA) for preprocessing, statistical and exploratory analyses. A simple method of spectrum quality control was implemented by removing spectra with no noticeable Raman spectroscopic features. The fluorescent background in the Raman spectra of the blood was removed using an adaptive iteratively reweighted penalized least squares (airPLS) baseline correction algorithm69. A local median filter was used to remove intensity spikes arising from cosmic ray events (MATLAB). Statistical analyses including significant factor analysis (SFA), principal component analysis (PCA), hierarchical clustering such as k-nearest neighbour (KNN), support vector machine discriminant analysis (SVMDA), artificial neuron network analysis (ANN), and genetic algorithm (GA) were performed using the corresponding functions from the PLS toolbox (Eigenvector, Wenatchee, WA) and MATLAB. Results and discussion Raman spectra of female and male blood: comparison of average spectra Human blood is a complex mixture of diverse biochemical constituents, of which relative the contributions tend to vary with the donor55. The main components of blood are red and white blood cells and thrombocytes. Red blood cells are composed of approximately 30% hemoglobin by volume. The plasma biochemistry is dominated by proteins, with albumins, globulins, and fibrinogen as the main proteins21. A Raman spectrum of blood obtained at 785-nm excitation is dominated by the contribution of hemoglobin55. However, the Raman microspectroscopic mapping of a dry bloodstain shows a noticeable heterogeneity, indicating contributions from other biochemical components55. We hypothesized that there are characteristic variations in the Raman spectra of blood related to gender. All individual Raman spectra collected during the microscopic mapping of dry bloodstains were combined into two datasets: male and female. As expected, the spectra show slight variations within each dataset due to the sample heterogeneity and possible variation due to the donor. First, we calculated and compared the average Raman spectra for male and female blood. Figure 1 shows the average spectra normalized by the total area and the one-standard-deviation spectra for each dataset. Visual inspection shows that the average female and male spectra have very similar profiles (Figure 1A). Some minor differences can be observed within the 930-975 and 1210-1230 cm-1 regions and for the 1560, 1580, and 1600 cm-1 Raman peaks. The difference spectrum between the normalized, average female and male blood spectra is shown in Figure 1B along with the one-standard-deviation (STD) spectra (Figure 1B). One can see that the difference spectrum is well within one standard deviation, indicating that this spectral difference might not be statistically significant.

addition to the identification of a specific body fluid, this method allows for differentiating between the blood different species58, 62, 63, menstrual and peripheral blood61, and bloodsemen mixtures64. To move forward towards practical forensic applications, this method was validated for biological stains on various common substrates65-66 and for heavily contaminated samples67. Most recently, the ability of Raman spectroscopy to determine the time since the deposition of a bloodstain was reported9. We also reported on the great potential of Raman spectroscopy in determining race from a bloodstain68. We hypothesized that Raman microspectroscopy of dry blood from people of different races should allow for the determination of gender based on the differences in the biochemical composition of the blood of men and women. Here, we report the comparative Raman microspectroscopic characterization of dry blood traces acquired from 30 men and 30 women. An automatic mapping technique was used for collecting multiple Raman spectra from different spots on each sample. Spectral preprocessing steps, including fluorescence and cosmic ray removal, background subtraction and normalization by total area, utilized prior statistical analysis of the spectroscopic data. Exploratory statistical analysis was conducted to test the variability of and level of similarity of female and male datasets. The application of advanced statistical methods based on a support vector machines discriminant analysis (SVMDA) and an artificial neuron network (ANN) accounted for the variations and heterogeneity in dry blood samples and minimized the possibility of false gender identification. In particular, approximately 98% gender differentiation accuracy at the level of individual bloodstains and more than 80% accuracy at the level of individual Raman spectra were achieved. Experimental section Samples and data acquisition A total of 30 male and 30 female blood samples were purchased from Bioreclamation Inc (Westbury, NY). All donors were found to be negative for HIV ½ AB and HCV AB and non-reactive for HBSAG, HIV-1 RNA, HCV RNA and STS. The age of the male and female donors varied between 24 – 64 (42.1±9.6) and 23-57 (41.4±9.7) years, respectively. Dry bloodstains were prepared by placing a 10-µl drop of blood on a piece of aluminium foil covering a glass microscope slide. No other pre- or post-treatments steps were performed. The procedure for Raman microspectroscopy of dry traces of body fluids has been described previously4. Briefly, a Renishaw inVia confocal Raman spectrometer equipped with a researchgrade Leica microscope with a 50x long-distance objective (numerical aperture of 0.35) was used to collect the spectra in automatic mapping mode. The PRIOR automatic stage controlled the mapping over a 4x4 mm area (9x9 matrix with 0.5mm step). We intentionally did not select Raman spectra based on their localisation within the dried spot to mimic workflow with samples from real crime scenes with unpredictable deposition conditions. A 785-nm laser light was used for the excitation. The laser power of 6 mW and the spectrum accumulation time of 10 sec were used to minimize the effect of photodegradation. Each individual Raman spectrum was accumulated over the range of 300–3200 cm-1 with the spectral resolu-

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Figure 1. Average and difference Raman spectra of bloodstains. (A) Average Raman spectra of female (red line) and male (green line) blood normalized to the total area. (B) Difference spectrum between the normalized, average female and male blood spectra (black line) and spectral variations around the mean (±1 STD) for female (red line) and male (green line) blood. All spectra are shown using the same units on the y-axis.

Statistical analysis of spectroscopic data We have recently demonstrated that, regardless of the gender of the donor, the Raman spectra of blood can be distinguished from other body fluids using the multidimensional spectroscopic blood signature4. The direct application of this approach to the problem of gender differentiation was not practical (data not shown) due to a high level of similarity between the Raman spectral characteristics of male and female blood. Instead, we utilized several statistical methods for differentiating female and male blood spectral datasets and compared the performance of these methods. We started with an unsupervised, exploratory data analysis based on PCA, the results of which are shown in Figure 2. The three-component score plots show strong overlap between the female and male data with small areas dominated by individual gender points. The appearance of such space domains might indicate the presence of Raman spectral characteristics specific to a particular gender.

Figure 2. PCA score plots of female (red) and male (green) blood spectra built using three principal components. Panels A and B show two different projections of the same plot. Each point represents an individual Raman spectrum.

Hierarchical clustering Hierarchical clustering methods were used to explore the internal structure of Raman spectroscopic data from female and male blood samples. These methods typically allow for splitting spectra into hierarchical subgroups based on certain universal similarity criteria without any knowledge of actual classes (genders, in our case). Such a hierarchal relation can be visualized using dendrograms, where similar spectra are represented by neighbouring branches. As observed in Figure 3, KNN clustering using Ward’s approach (all spectra are organized according to their proximity in the virtual space of PCs, where the closest elements form groups) exposed a complex hierarchy of diverse clusters. The clustering hierarchy is outlined by grey lines (Figure 3.A). Note the appearance of two characteristic clusters of spectra. Approximately 1000 “female” (represented by red lines at the bottom of panel A in Figure 3) and 1150 “male” (green lines at the top of panel A in Figure 3) spectra were placed together. The appearance of two such zones within the dendrogram that are dominated by “female” and “male” Raman spectra indicates that our initial hypothesis (female and male blood can be differentiated based on their Raman spectra) is potentially correct.

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Figure 3. A) Composed image representing results of hierarchical Ward’s clustering. Black braces outline grouping of Raman spectra dominated by “female” (red lines) and “male” (green lines) blood (left panel). Right panel shows higher level clusters delineated by grey lines. B) Cross-validated (see “SVMDA model” section in the text) SVMDA analysis targeting the differentiation between Raman spectra of female (red) and male (green) blood. Predicted probability for a single Raman spectrum of a blood sample to be assigned to the female class.

SVMDA model tive contributions from uninformative variables by converting a set of raw observations into a set of values of new linearly As the next step, we tested the possibility of gender discrimuncorrelated variables defined as principal components. PCA ination using the results of hierarchical Ward’s clustering. We significantly speeds up the processing time by aggregating built SVMDA classification models using the characteristic only essential information into new variables called “principal clusters described above as a training dataset. The omitted components.” spectra were combined in a separate dataset, which will be discussed below. Specifically, Figure 3.B shows the results of Finally, the described SVMDA classification model was apthe SVM discriminant analysis using two classes defined as plied to the omitted dataset, the group of spectra omitted from “female” – class 1 (first 1000 female spectra positioned by the initial treatment. Expectedly, poorer selectivity and sensiWard’s clustering at the lower extremity of Figure 3.B) and tivity parameters were obtained (Table 1, line 2). To assess the “male” – class 2 (top 1150 male spectra ). Note, “female” effect of spectra selection, we performed SVMDA using train(“male”) class consists of only Raman spectra originated from ing and test datasets of different sizes (Table 1). Notably, in all female (male) donors. The latter means that, for example, a cases (except the one explained below), the first x and the last y spectra in the Ward’s clustering were used as “female” and few female spectra placed by clustering algorithm within the “male” classes, respectively, where the x and y numbers are top part of dendrogram were not selected. As expected, the SVMDA classification demonstrated high selectivity (percentshown in Table 1. For example, when we used a larger trainage of positives that were correctly identified as positives) and ing dataset (Table 1, line 1), the performance of the SVMDA sensitivity (percentage of negatives that were correctly identiclassification decreased. This decrease was due to the new fied as negatives) in the gender determinations (Figure 3.B). training data that was compromised by a greater contribution For the SVMDA model delineated in Figure 3.B, the crossof uncharacteristic (in terms of gender) spectra from the centre validated selectivity and sensitivity parameters for both classes of Ward’s dendrogram (Figure 3. A). A training dataset that is were >95% (Table 1, line 2). The cross-validation was contoo small is also not an optimal choice (Table 1, line 4). Altducted using a sample-wise leave-one-out approach: all spechough the developed model results in excellent classification tra except the spectra collected from one sample were used to within the training dataset, the classification of omitted data is build an SVMDA model and then to classify the left-out specnot as good. In this case, a reduced training dataset does not tra. This procedure was repeated for each individual sample. cover the spectral variability of blood samples. For compariNote that all SVMDA models were built using spectroscopic son, the random selection of spectra resulted in worse performance parameters (Table 1, line 5). Therefore, the construcdatasets that had been dimensionally reduced by PCA (Figure tion of the training and test datasets is a crucial step in this S1, Supporting Information). This spectral pretreatment chemometric analysis. technique helps to minimize noise, redundancy and unproducTable 1. Chemometric analysis of the gender differentiation problem. The selectivity and sensitivity parameters are shown for the “female” class. Li ne #

Number of spectra used to build the classification model (see text)

Classification method

Training dataset

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Test dataset

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1* 2* 3*

Female 1500 1000 600

Male 1500 1150 600

SVMDA SVMDA SVMDA

Selectivity, % 90 98 99

Sensitivity, % 89 97 100

Selectivity, % 64 73 68

Sensitivity, % 61 69 62

60 61

57 52

81

82

4* 200 200 SVMDA 100 100 5* 1500 1500 SVMDA 67 72 * 6* 1000 1150 ANN 95 95 * selection based on hierarchical Ward’s clustering (see text); ** random selection

Table 2. Assignment of spectroscopic features of blood selected by GA coupled with ANNs.

Raman band /cm-1 715 740 743 750 754 790 895 904 936 942 956 967 1000 1027 1055 1102 1124

1170 1247 1248

Assignment73-81 N+(CH3), lipids ν15 (pyr breathing) ν15 (pyr breathing) ν15(pyr breathing) ν15(pyr breathing) ν46 (C-C), glycine, glutathione, glutamine C−C skeletal ν46 (C-C), leucine, lipids lipids, proteins ν47 (CbC1)asym phenylalanine δ(=CbH2)asym, Phe

Raman band /cm-1 1311 1338 1343 1347 1367 1369 1401

Assignment73-81 ν21 some amino acids tryptophan some amino acids ν (pyr half-ring)sym ν4 ν20

1447 1543 1550 1563 1565 1575

1600 1604 1617

δ (CH2/CH3), ν20 ν11, ν28 ν11, ν28 ν11, ν28 ν11, ν28 ν(CαCm)asym, phenylalanine ν(CαCm)asym, phenylalanine ν(C=C) vinyl ν(C꞊C) Heme, phenylalanine

1639

ν10

1653 1660

ν37, Amide I ν37, Amide I

1578

δ(=CbH2)asym heme, polysaccharides, ν22 (porphyrin half ring), observed in the spectra of a single human RBC ν (pyr half-ring)sym, tyrosine ν30 guanine, cytosine, proteins

A combined ANN-GA model Finally, we tested a discrimination approach based on a genetic algorithm coupled with artificial neuron network (ANN) classification. A genetic algorithm (GA) is a powerful statistical method that allows for determining the spectral features that contribute the most to the differentiation efficiency70. ANN analysis was selected as a powerful method to account for the nonlinear relationships between variables71, 72. By coupling the ANN and GA approaches72, we tested what variables (spectroscopic features) lead to better gender discrimination.

Here, we used feedforward ANNs with one or two hidden layers and a variable number of neurons. The spectroscopic features selected by GA are shown in Table 2. The obtained classification model demonstrated high selectivity and sensitivity in gender discrimination for both datasets: 95% for training and >80% for test (omitted) data. When the ANN-GA model was applied to the entire dataset, 59 out of 60 (98%) blood samples had more than 60% of their spectra with the correctly assigned gender. This result is very encouraging because it indicates the potential for 98% gender

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differentiation accuracy for individual bloodstains. At the same time, further method development is necessary to improve the differentiation at the level of individual spectra. A significant increase in the number of samples should help further improve the differentiation power of the method. Although it is not necessary for practical forensic applications, knowledge of the biochemical basis of gender differentiation by Raman spectroscopy of a bloodstain could be useful for further method development. It is evident from Table 2 that the majority of Raman peaks contributing the most to the differentiation between female and male blood are associated with proteins, although some contribution from lipids and DNA is also possible. This general observation is consistent with the difference in the biochemical composition of female and male blood described in the Introduction based on the literature data. More detailed analyses including the assignment of the Raman features in Table 2 to specific protein, lipid and DNA components would require significant effort involving additional biochemical methods, which is beyond the scope of this work. Conclusions Modern forensic science has experienced a significant burst due to the development and implementation of new analytical methods. Raman spectroscopy is one of the few methods capable of nondestructive and nearly instantaneous analysis of a wide variety of materials and objects, including bloodstains, at the scene of a crime. This work demonstrates the potential of Raman spectroscopy in the analysis of bloodstains for determining the gender of the donor. In this study, Raman spectra were acquired in a mapping manner from multiple spots on each bloodstain to account for intrinsic sample heterogeneity. The exploratory statistical analysis of the combined dataset, including the entire set of Raman spectroscopic data for the female and male samples showed that the female and male data subsets were highly similar in terms of their spectroscopic features. More advanced statistical methods based on support vector machines and artificial neuron networks allowed for differentiating male and female bloodstains with high confidence. In particular, the statistical approach based on a genetic algorithm coupled with artificial neuron network classification showed approximately 98% gender differentiation accuracy at the level of individual bloodstains and 80% accuracy at the level of individual Raman spectra. These results demonstrate the great potential of the developed method for forensic applications. However, more work is needed to further validate the method. In particular, other factors including age, race, diet, and illnesses, which can potentially affect the biochemical composition of blood and, consequently, the corresponding Raman spectra, need to be evaluated. Furthermore, common substrate interferences and potential contamination should be taken into account before the method can be utilized in practice. Quality of gender identification can be also improved by using quality control methods to select only “good” Raman spectra. However, from our practical experience we know that all mentioned above complications with blood samples require individual tuning of these methods. Simplicity of the used quality control offers accelerated dissemination of the developed method.

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The ultimate goal of this project is to expand the developed technology to a portable Raman instrument for the infield identification of traces of bodily fluid and to obtain phenotype information of the donor, including gender, race, age, etc. Furthermore, a portable Raman instrument can be potentially used for the analysis of a variety of other types of evidence.

AUTHOR INFORMATION Corresponding Author * Email: [email protected], Phone: 518-591-8863, Fax: 518442-3462

Present Addresses †

Wadsworth Center, Albany, New York 12201.

Author Contributions The manuscript was written through contributions of all authors. / All authors have given approval to the final version of the manuscript.

ACKNOWLEDGEMENTS This project was supported by Award No. 2014-DN-BX-K 016, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Jus-tice. The opinions, findings, and conclusions or recommen-dations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice.

Supporting Information Figure S1 showing the PCA reduction of SVMDA training dataset, specifically the standard deviation spectra, PCA score plot, and PCA loading profiles. The Supporting Information is available free of charge on the ACS Publications website.

REFERENCES (1) Douglas, J. E.; Ressler, R. K.; Burgess, A. W.; Hartman, C. R. Sciences & the Law 1986, 4, 401-421. (2) Stoney, D. A.; Stoney, P. L. Forensic Sci. Int. 2015, 251, 159-70; (3) Muro, C. K.; Doty, K. C.; Bueno, J.; Halamkova, L.; Lednev, I. K. Anal. Chem. 2015, 87, 306-27; (4) Sikirzhytski, V.; Sikirzhytskaya, A.; Lednev, I. K. Appl. Spectrosc. 2011, 65, 1223-32; (5) Virkler, K.; Lednev, I. K. Forensic Sci. Int. 2009, 188, 117; (6) Barron, L.; Gilchrist, E. Anal. Chim. Acta. 2014, 806, 2754; (7) Tarifa, A.; Almirall, J. R. Sci. Justice 2015, 55, 168-75; (8) Hoehse, M.; Paul, A.; Gornushkin, I.; Panne, U. Anal. Bioanal. Chem. 2012, 402, 1443-50; (9) Doty, K. C.; Muro, C. K.; Bueno, J.; Halámková, L.; Lednev, I. K. J. Raman Spectrosc. 2016, 47, 39-50. (10) Shaler, R. C. In Forensic Science Handbook, Saferstein, R., Ed. Prentice Hall: Upper Saddle River, 2002; Vol. 1, pp 529546; (11) Li, R. In Forensic biology: identification and DNA analysis of biological evidence CRC Press/Taylor & Francis: Boca Raton, 2008; p 448; (12) Jones Jr., E. L. In Forensic Science Handbook, Saferstein, R., Ed. Prentice Hall: Upper Saddle River, NJ, 2005; Vol. 2, pp 329-382.

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Analytical Chemistry (13) Butler, J. M. In Advanced Topics in Forensic DNA Typing: Methodology, Academic Press: Waltham, MA, USA, 2011; pp 497-514. (14) Edelman, G. J. Spectral analysis of blood stains at the crime scene. PhD thesis, Academic Medical Center, University of Amsterdam, 2014. (15) Brunelle, E.; Huynh, C.; Le, A. M.; Halamkova, L.; Agudelo, J.; Halamek, J. Anal. Chem. 2016, 88, 2413-20; (16) Huynh, C.; Brunelle, E.; Halamkova, L.; Agudelo, J.; Halamek, J. Anal. Chem. 2015, 87, 11531-6. (17) Blechschmidt, E.; Gasser, R. F. In Biokinetics and Biodynamics of Human Differentiation: Principles and Applications. North Atlantic Books: Berkeley, California, 2012; (18) Tarnopolsky, M. In Gender Differences in Metabolism: Practical and Nutritional Implications (Nutrition in Exercise & Sport) 1ed.; CRC Press: USA, 1998; p 368. (19) Hyde, J. S. Annu. Rev. Psychol. 2014, 65, 373-98. (20) Miike, K.; Aoki, M.; Yamashita, R.; Takegawa, Y.; Saya, H.; Miike, T.; Yamamura, K. Proteomics 2010, 10, 2678-91; (21) Altman, P. L.; Dittmer, D. S. In Blood and Other Body Fluids. Federation of American Societies for Experimental Biology: Washington, DC, 1961; (22) Merimee, T. J.; Fineberg, S. E. J. Clin. Endocrinol. Metab. 1971, 33, 896-902; (23) Rampling, M. W. In Compositional properties of blood. In Handbook of Hemorheology and Hemodynamics, 1 ed.; Baskurt, O. K.; Hardeman, M. R.; Rampling, M. W.; Meiselman, H. J., Eds. IOS Press: Amsterdam, Netherlands, 2007; pp 34-44; (24) Silliman, C. C.; Dzieciatkowska, M.; Moore, E. E.; Kelher, M. R.; Banerjee, A.; Liang, X.; Land, K. J.; Hansen, K. C. Transfusion 2012, 52, 417-24; (25) Dzieciatkowska, M.; D'Alessandro, A.; Hill, R. C.; Hansen, K. C. J. Proteomics 2015, 120, 1-6; (26) Bukowski, C.; Grigg, M. A.; Longcope, C. Clin. Chem. 2000, 46, 1415-6; (27) Koh, E. T.; Chi, M. S.; Lowenstein, F. W. Am. J. Clin. Nutr. 1980, 33, 1828-35. (28) Folkersen, J.; Teisner, B.; Grunnet, N.; Grudzinskas, J. G.; Westergaard, J. G.; Hindersson, P. Clin. Chim. Acta 1981, 110, 139-45; (29) Sand, O.; Folkersen, J.; Westergaard, J. G.; SottrupJensen, L. J. Biol. Chem. 1985, 260, 15723-35. (30) Gordon, D. J.; Rifkind, B. M. N. Engl. J. Med. 1989, 321, 1311-6. (31) Byrne, H. J.; Knief, P.; Keating, M. E.; Bonnier, F. Chem. Soc. Rev. 2016, 45, 1865-1878. (32) Bocklitz, T. W.; Guo, S.; Ryabchykov, O.; Vogler, N.; Popp, J. Anal. Chem. 2016, 88, 133-51; (33) Chernenko, T.; Sawant, R. R.; Miljkovic, M.; Quintero, L.; Diem, M.; Torchilin, V. Mol. Pharm. 2012, 9, 930-6; (34) Diem, M.; Mazur, A.; Lenau, K.; Schubert, J.; Bird, B.; Miljkovic, M.; Krafft, C.; Popp, J. J. Biophotonics 2013, 6, 85586; (35) Lemler, P.; Premasiri, W. R.; DelMonaco, A.; Ziegler, L. D. Anal. Bioanal. Chem. 2014, 406, 193-200. (36) Vitek, P.; Ali, E. M.; Edwards, H. G.; Jehlicka, J.; Cox, R.; Page, K. Spectrochim Acta A Mol. Biomol. Spectrosc. 2012, 86, 320-7; (37) Vandenabeele, P.; Edwards, H. G.; Jehlicka, J. Chem. Soc. Rev. 2014, 43, 2628-49; (38) Boyd, S.; Bertino, M. F.; Ye, D.; White, L. S.; Seashols, S. J. J. Forensic Sci. 2013, 58, 753-6; (39) Han, Z.; Liu, H.; Meng, J.; Yang, L.; Liu, J.; Liu, J. Anal. Chem. 2015, 87, 9500-6; (40) Kourkoumelis, N.; Balatsoukas, I.; Moulia, V.; Elka, A.; Gaitanis, G.; Bassukas, I. D. Int. J. Mol. Sci. 2015, 16, 14554-70;

(41) Lauwers, D.; Hutado, A. G.; Tanevska, V.; Moens, L.; Bersani, D.; Vandenabeele, P. Spectrochim Acta A Mol. Biomol. Spectrosc. 2014, 118, 294-301. (42) Thomas, J.; Buzzini, P.; Massonnet, G.; Reedy, B.; Roux, C. Forensic Sci. Int. 2005, 152, 189-97. (43) Hodges, C.; Akhavan, J. Mol. Spectrosc. 1990, 46, 303307. (44) Rodger, C., Broughton, D. Analyst 1998, 123, 1823-1826. (45) Mazzella, W. D.; Buzzini, P. Forensic Sci. Int. 2005, 152, 241-7. (46) Suzuki, E. M.; Carrabba, M. J. Forensic Sci. 2001, 46, 1053-69. (47) Bueno, J.; Sikirzhytski, V.; Lednev, I. K. Anal. Chem. 2012, 84, 4334–4339 (48) Bueno, J.; Lednev, I. K. Anal. Bioanal. Chem. 2014, 406, 4595-9; (49) Abrego, Z.; Grijalba, N.; Unceta, N.; Maguregui, M.; Sanchez, A.; Fernandez-Isla, A.; Goicolea, M. A.; Barrio, R. J. Analyst 2014, 139, 6232-41; (50) Lopez-Lopez, M.; Fernandez de la Ossa, M. A.; GarciaRuiz, C. Appl. Spectrosc. 2015, 69, 889-93; (51) Zeng, J.; Qi, J.; Bai, F.; Yu, J. C.; Shih, W. C. Analyst 2014, 139, 4270-8. (52) McLaughlin, G.; Lednev, I. K. Anal. Bioanal. Chem. 2011, 401 , 2511-8; (53) McLaughlin, G.; Lednev, I. K. Am. J. Anal. Chem. 2012, 3, 161-167. (54) Coyle, T.; Anwar, N. Sci. Justice 2009, 49 , 32-40. (55) Virkler, K.; Lednev, I. K. Anal. Bioanal. Chem. 2010, 396, 525-34; (56) Virkler, K.; Lednev, I. K. Forensic Sci. Int. 2009, 193, 5662; (57) Virkler, K.; Lednev, I. K. Analyst 2010, 135, 512-7; (58) Virkler, K.; Lednev, I. K. Anal. Chem. 2009, 81, 7773-7; (59) Sikirzhytski, V.; Sikirzhytskaya, A.; Lednev, I. K. Anal. Chim. Acta. 2012, 718, 78-83; (60) Sikirzhytskaya, A.; Sikirzhytski, V.; Lednev, I. K. Forensic Sci. Int. 2012, 216, 44-8; (61) Sikirzhytskaya, A.; Sikirzhytski, V.; Lednev, I. K. J. Biophotonics 2012, 7, 59-67 (62) McLaughlin, G.; Doty, K. C.; Lednev, I. K. Forensic Sci. Int. 2014, 238, 91-5; (63) McLaughlin, G.; Doty, K. C.; Lednev, I. K. Anal. Chem. 2014, 86, 11628-33. (64) Sikirzhytski, V.; Sikirzhytskaya, A.; Lednev, I. K. Forensic Sci. Int. 2012, 222, 259-65. (65) McLaughlin, G.; Lednev, I. K. J. Forensic Sci. 2015, 60, 595-604; (66) McLaughlin, G.; Sikirzhytski, V.; Lednev, I. K. Forensic Sci. Int. 2013, 231, 157-66. (67) Sikirzhytskaya, A.; Sikirzhytski, V.; McLaughlin, G.; Lednev, I. K. J. Forensic Sci. 2013, 58, 1141-8. (68) Mistek, E.; Halamkova, L.; Doty, K. C.; Muro, C. K.; Lednev, I. K. Anal. Chem. 2016. 88, pp 7453–7456 (69) Zhang, Z. M.; Chen, S.; Liang, Y. Z. Analyst 2010, 135, 1138-46. (70) Niazi, A.; Leardi, R. J. Chemometrics 2012, 26, 345–351. (71) Ding, S.; Hui Li, H.; Su, C.; Yu, J.; Jin, F. Artificial Intelligence Review 2013, 39, 251-260; (72) Yao, X. Int. J. Neural. Syst. 1993, 4, 203-22. (73) Movasaghi, Z.; Rehman, S.; Rehman, I. U. Appl. Spectrosc. Rev. 2007, 42, 493-541; (74) Alfano, R.; Wang, W. B.; Doctor, A.Glucose Levels Using Excitation and Difference Raman Spectroscopy at the IUSL 2008; (75) Janko, M.; Stark, R. W.; Zink, A. J. R. Soc. Interface 2012; 9, 2581-90 (76) Aubrey, K. L.; Thomas, G. J. Jr. Biophys. J. 1991, 60, 1337-49;

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(77) Grasselli, J. Chemical Applications of Raman Spectroscopy. John Wiley & Sons: New York, 1981; (78) Johnson, C. R.; Ludwig, M.; Asher, S. A. J. Am. Chem. Soc. 1986, 108, 905-912; (79) Hu, X.; Spiro, T. G. Biochemistry 1997, 36, 15701-12; (80) Sato, H.; Chiba, H.; Tashiro, H.; Ozaki, Y. J. Biomed. Opt. 2001, 6, 366-70; (81) Premasiri, W. R.; Lee, J. C.; Ziegler, L. D. J. Phys. Chem. B 2012, 116, 9376-86.

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TOC figure 270x203mm (96 x 96 DPI)


Determining Gender by Raman Spectroscopy of a Bloodstain

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