Igor K. Lednev

	Crime scenes are the starting point of many investigations. This is where evidence is collected to provide investigative leads. Blood traces are commonly found at crime scenes and can provide substantial information about the event and individuals involved. Bloodstains are a primary source of DNA evidence and can be used to conclusively identify the donor of the blood. The pattern of a blood spatter can indicate the type of the violent action. Moreover, determining the time since deposition (TSD) of bloodstains is of significant value for two reasons. First, TSD could help to estimate the time of the crime. Second, if crime scenes contain multiple sets of bloodstains, the TSD should allow for the selection of bloodstains relevant to the crime, and therefore reduce the number of samples that should be collected, documented, and processed. The latter is of special importance because every crime laboratory in the United States has a significant backlog of DNA evidence.

	Raman spectroscopy has proven to be a rapid, nondestructive, and specific technique that is capable of identifying and analyzing bloodstains down to a single red blood cell (1). We have combined Raman spectroscopy with chemometrics to discriminate bloodstains from other body fluid traces (2–4), perform species identification (5–8), and determine phenotypic traits of human donors such as sex (9), race (10), age group (11). It is of special importance for practical forensic purposes that the Raman spectroscopic method for bloodstain identification is confirmatory and “immune” from environmental false positives (12). The availability of portable and handheld Raman instruments makes bloodstain analysis within the first minutes of the crime scene discovery a reality.

	The near infrared (NIR) Raman spectra of blood, when excited at 785 nm, are dominated by a contribution from hemoglobin. When blood exits the body, it is red in appearance, but as it dries, bloodstains darken. This red-to-brown color change occurs as a result of the autoxidation of hemoglobin into oxyhemoglobin (oxyHb). As the bloodstain aging process continues, oxyHb is converted into methemoglobin (metHb). Once in this state, the hemoglobin molecule is no longer able to bind oxygen. Finally, when blood is outside the body for an extended period of time, metHb will form aggregates of hemichrome. Raman spectroscopy detects these changes in hemoglobin over time and provides the basis for determining the TSD of bloodstains. In our initial study, we developed an accurate regression model for bloodstain aging for the first week since the deposition (13). Doty and associates were able to monitor the autoxidation process of hemoglobin by following the intensity changes of the Raman bands at 377 and 420 cm

. These bands are markers for metHb and oxyHb, respectively. Changes to these two bands began after one hour of deposition, with a relative increase in the 377 cm

 peaks. Using partial least squares regression (PLSR), the TSD of bloodstains was predicted with an accuracy rate of 0.99 and when cross-validated the accuracy rate was 0.97 for up to one week.

	To ascertain the full capabilities of the method, we continued to monitor the Raman spectra that allowed for building a regression model to determine the TSD for up to two years (Figure 1) (14). There were evident changes to Raman spectra even after two years of aging. As a result of heme aggregation during aging, the fluorescence interference continuously decreased the quality of the Raman spectra. This is because of the changes in the electronic structure of heme as it ages (15). The intensity of the Raman bands decreased, and the peaks broadened after a prolonged (about a year or longer) TSD. These changes were tentatively attributed to the advanced degradation of hemoglobin. Using the PLSR model, Doty and associates were able to predict the TSD of bloodstains up to two years with 70% accuracy. Some might speculate that this error is too large for practical application, but the method definitively differentiates between bloodstains that were present at crime scene for a few hours, a few days, a few weeks, a few months or over a year. Just as Raman spectroscopy has proven to be a valuable tool for discriminating between body fluids, this technique has the potential to be a one-step analysis method to both identify a biological stain as blood and approximate its TSD.	

	 Despite the promising progress made toward creating an easy-to-use method for determining the TSD of bloodstains, there is still more work that needs to be done. Doty and associates reported on bloodstains that were aged in a laboratory environment at ambient conditions. There are infinite varieties of potential conditions and environments in which forensic biological evidence can be discovered that can affect the blood aging process. Developing regression models for Raman spectroscopic signatures of bloodstains aging at various “extreme” environments appears to be the next logical step moving forward. The combination of the accumulated data into a single universal model applicable for all common environments would be ideal for practical application. The capability to create this universal model has yet to be demonstrated. This is problematic because of a combined complexity of 1) gathering more comprehensive spectral characteristics of bloodstains exposed to various “extreme” environments, and 2) the statistical analysis allowing for an accurate accounting of a specific environmental effect on the bloodstain aging. Overcoming these problems would allow for the building of a comprehensive (universal) model for predicting TSD of bloodstains found in any common environment. Such a development would be a great step forward in solving the perpetual problem of determining the TSD of a bloodstain for forensic purposes.

	This project was supported by Award No. 2017-DN-BX-0135 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the U.S. Department of Justice.	

V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

A. Sikirzhytskaya, V. Sikirzhytski, and I.K. Lednev, 

G. McLaughlin, K.C. Doty, and I.K. Lednev, 

K.C. Doty, G. McLaughlin, and I.K. Lednev, 

 is a PhD student in the Chemistry Department at the University at Albany, State University of New York (SUNY). 	 

 is a professor of chemistry at the University at Albany, SUNY. Direct correspondence about this article to 

Articles

In celebration of Spectroscopy’s 35th Anniversary, leading experts discuss important issues and challenges in analytical spectroscopy.

Raman Spectroscopy is Solving the Perpetual Problem of CSI: The Time of a Crime

Near-infrared (NIR) Raman spectroscopy has become widespread in different areas of the analytical study of materials, including forensic science. The nondestructive character, high selectivity, and possibility to explore a variety of materials make NIR Raman a valuable tool for the identification of body-fluid traces. In this study, we combined regression and classification chemometrical algorithms to achieve effective discrimination of pure body fluids from their binary mixtures. Raman spectra of dried blood, semen, and their mixtures in different ratios, collected in an automatic mapping manner, were used as a model system. The established detection limit for minor contributors is as low as a few percent. The proposed methodology takes into account the intrinsic heterogeneity of blood and semen and their variations between donors, and it potentially can be applied on other mixtures, including those which are of interest to forensic specialists.

In this article, we outline a recently published study on differentiating semen and blood mixtures from individual body fluids (1). The ultimate goal of this project is to develop a nondestructive, confirmatory method for the identification of trace amounts of body fluids for forensic purposes (2,3). Modern forensic tools, such as DNA- and RNA-based methods, provide confirmation of body-fluid type, race, and gender. Even a person involved in a crime can be unambiguously identified by genetic analysis of biological evidence. However, in real life, forensic laboratories equipped with such labor-intensive and expensive techniques are significantly outnumbered by the quantity of biological "footprints" associated with crimes. As a result, there are thousands of body-fluid samples waiting to be analyzed. This type of analysis can be difficult or impossible to perform when the amount of evidence is diminutive or is presented as a complex mixture. 

Raman spectroscopy is a nondestructive, time-efficient, and easy-to-use technique suitable for the simultaneous characterization of multiple fluids. Our earliest studies demonstrated the exceptional potential of near-infrared (NIR) Raman spectroscopy for identifying pure body-fluid stains and encouraged us to address more complicated problems, such as differentiating pure body fluids from their mixtures (1,3–11). This practical task required a simplified formulation of the common statistical problem of spectroscopic signal demixing. A forensic expert is much more interested in determining the presence or absence of particular body fluids within a stain than their exact contributions (percentages), which are perfectly accidental. 

This scientific problem has been studied using mixtures of blood and semen as model systems because they are highly practical. Blood and semen mixtures are often found at crime scenes related to sexual assaults. The detection of even minor remnants of these types of forensic evidence could be crucial for investigating a crime. Several methods for body-fluid identification were recently proposed and have been used efficiently in forensic laboratories. It is possible to characterize such biological evidence by fluorescence (12–14), immunological tests (15), electrophoretic separation (16), RNA and DNA profiling (17), and several other methods (18–22).  

Despite the undeniable advantages of the methods listed above, a uniform approach that can save sample for further analysis and provide fast and accurate results is still a necessity. The Raman effect occurs due to the interaction of an incident photon with a vibrating molecule. The energies of the scattered photons with shifted frequencies can be graphically represented as Raman spectra that provide unique information about the biochemical composition of the analyzed samples (23). The volume of the analyzed matter can be as small as a few femtoliters or picograms of sample.  

Recent developments in analytical instrumentation have allowed for the comprehensive manipulation of samples using techniques such as automatic imaging and mapping. This technique automatically collects spectra when a sample under the laser beam moves by a specified amount until all of the areas are scanned. This approach yields large data sets that can be preprocessed and treated with a variety of statistical software packages. Combining Raman spectroscopy with multivariate statistics helps avoid the problem of heterogeneity of body-fluid stains; their composition can vary between donors and within the sample. The method we propose can process samples with various fluorescent profiles and overlapping spectral bands, which are common features for biological objects.  

Our method is based on the effective combination of regression and classification analyses that require a multistep discrimination procedure. Support vector machine (SVM) regression was selected to separate the mixture's spectra that can be easily distinguished from those of pure fluids. The final classification model was built on selected data using support vector machine discriminant analysis (SVMDA). Then, the entire data set was subjected to the analysis. As a result, we were able to distinguish spectra with minor contributions of blood or semen. The lowest concentrations that we were able to detect were 5% of blood in semen and approximately 1% of semen in blood stains. Lower concentrations could be detected, but the accuracy of detection decreased significantly. The proposed approach potentially can be plugged into portable instruments as a discriminative algorithm, which would be beneficial for forensic laboratories as a valuable tool for investigation directly at a crime scene. 

Mixtures of blood and semen in different ratios (5:95, 10:90, 20:80, 30:70, 40:60, 50:50, 70:30, 75:25, 85:15, 85.5:12.5, 92.75:6.25, 96.9:3.1, and 98.4:1.6) were prepared. All samples were thoroughly blended to reconstruct the most difficult scenario, where two body fluids are presented as a highly homogeneous mixture. Each microscope slide was covered with a piece of aluminum foil, which has a small Raman and fluorescence profile, and a 10 μL sample was deposited on the foil. The entire data set was formed from spectra recorded by a Renishaw inVia confocal Raman spectrometer equipped with a Leica microscope with a 50× objective and a Prior Scientific automatic stage. A 785-nm laser beam was used for excitation. A mapping procedure was performed on a 3.5 × 2.5 mm area, and a total of 108 spectra were accumulated from each sample.  

Recorded spectra were preprocessed in GRAMS/AI 7.01 software (Thermo Scientific) and treated in MATLAB 7.4.0 (Mathworks). The preprocessing procedure included subtraction of the fluorescent background by the adaptive iteratively reweighted penalized least squares (airPLS) algorithm (25), removal of cosmic ray interference, and normalization of spectra by the total area to account for the total offset variation. SVM regression of dimensionally reduced data (principal component analysis, or PCA) was performed to select three characteristic groups of spectra: pure semen, pure blood, and their mixture. Those groups were analyzed using an SVMDA algorithm to find out the possibility of discrimination between mixture and pure fluid classes, and establish the detection limit. 

From our previous study, we know that the composition of all human body fluids may vary between donors and even within a sample (26). The Raman spectra of mixtures can be characterized by the presence of the most prominent peaks in blood at 754, 1003, 1226, and 1619 cm

  (9) and semen at 716, 830, 959, 1268, 1329, and 1671 cm

  (7) (Figure 1). In cases where body fluids are not well mixed and the surface of a dry spot has areas dominated by one of two fluids, the sample can be analyzed by multidimensional Raman spectroscopic signatures (7,9). The most difficult cases occur when the blood and semen are thoroughly mixed. Using the naked eye to observe the acquired data from a thoroughly mixed sample has revealed that the contribution from semen is less noticeable, which was especially true for mixtures with a blood concentration greater than 50%.  

Figure 1: Selected characteristic raw Raman spectra acquired from pure (a) semen and (i) blood along with the spectra of (bâh) bloodâsemen mixtures.			

A number of comprehensive chemometrical approaches, including classical least squares, inverse least squares, partial least squares, principal component regression, least-squares support vector machines, and artificial neural networks, were applied directly to the data to discriminate the components of the mixture (27–29). However, these attempts did not effectively separate the classes because of the high level of heterogeneity of blood and semen. Here, we present the results of the SVM classification and describe the weaknesses of its straightforward application. During the first stage of analysis, we assigned a total of 15 classes for all of the mixtures and pure body fluids (Figure 2a; the 

-axis corresponds to the number of samples, and the 

-axis corresponds to the predefined class). The calculations revealed that a significant part of the mixtures was misclassified as pure blood or semen. However, when we performed the same calculations using only three classes (pure semen, mixture, and pure blood), approximately 40% of the blood and semen samples were misclassified as a mixture (Figure 2b).  

Figure 2: Assignment of Raman spectra (colored symbols) of semen/blood mixtures according to different SVM classification models: (a) All 15 mixtures with compositions varying from pure semen (red symbols) to pure blood (black symbols) were treated as separate classes. (b) A similar analysis performed with the same experimental data set, but with a different classification scheme: class 1 = pure semen, class 2 = mixture, class 3 = pure blood.			

These results illustrate that characterizing complicated systems, such as body-fluid mixtures, requires a more comprehensive approach. To improve discrimination, we developed a method that effectively combines the regression and classification analyses (Figure 3). During the first stage of the investigation, we prepared the data as described in the Experimental section (background subtraction, removal of cosmic rays, normalization, and assignment of classes). The pretreated data were subjected to SVM regression with leave-one-out cross-validation to determine the relationships between the measured and predicted classes (Figure 3a). This step revealed a smooth sigmoidal transition from pure fluid 1 to pure fluid 2, which was in agreement with the concentrations of the components. This calibration procedure helped us to select mixture spectra that could be easily distinguished from those of pure fluids (Figure 3b).  

Figure 3: (a) Blood contributions in a semen/blood mixture calculated using SVM regression. Blue crosses and red triangles represent the Raman spectra of pure semen and blood, respectively. Different types of symbols correspond to different mixtures. (b) The selection of Raman spectra for the following SVMDA classification. The first and second groups include the Raman spectra of pure fluids only, while Raman spectra of mixtures distinguishable from the first two groups compose the third group. (c) Cross-validated results of the SVMDA classification. (d) SVMDA analysis of the experimental Raman spectra, including those omitted during the classification model development stage (b). Each symbol corresponds to a single Raman spectrum.			

Semen, mixture, and blood classes were chosen and assigned as classes 1, 2, and 3, respectively. Only these spectra were subjected to the SVMDA classification with the help of PCA compression and cross-validation by 1000 splits. Data points outside of the range of 15–75% of blood were omitted because they overlapped with the classes of pure semen and blood (see Figure 3b). As expected, discriminant analysis of the selected spectra correctly distinguished (100%) the assigned classes. This model was used as the basis for further calculations and was applied to all of the data, including those previously omitted. The majority of the spectra were assigned to the correct class or mixture (Figure 3d). These procedures significantly improved the results achieved by directly applying the classification methods. 

In addition to the achieved results, we performed an external validation of our model using new donors (six for each body fluid). Different ratios of blood and semen were prepared, and their spectra were automatically classified. The results revealed that most of the spectra were assigned to the correct class. Only several spectra from the boundary classes were misclassified (the possibility of producing a false positive, false negative, true positive, or true negative classification is summarized in Table I).  

Table I: True positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) classification rates of pure blood, pure semen, and mixtures for different blood contributions. Cases in which TP, FP, TN, and FN terms are not applicable are marked as "n/a."			

This study examined the potential to use NIR Raman spectroscopy to characterize a mixture of blood and semen. The methodological aspects can be concisely summarized as follows: The spectroscopic data obtained from mixtures of blood and semen combined in different ratios were preprocessed and subjected to chemometrical analysis. During the first step, the spectroscopic data were subjected to a regression analysis, which allowed us to select three main classes that were distinguishable from each other and build an initial classification model. The spectra of the mixture samples with less than 15% and more than 75% blood were not considered during the preliminary discrimination procedure. After the model was developed, the SVMDA classification algorithm was applied to all of the data, including the previously omitted spectra. The obtained results were extensively cross-validated. The classification procedure revealed that the detection limits of both body fluids were as low as a few percent. Consequently, our recent studies, briefly delineated here, demonstrate the robustness of Raman spectroscopy coupled with advanced statistics and the validity of the initial studies. 

This project was supported by Award No. 2011-DN-BX-K551 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice (I.K.L.). The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice. We also would like to acknowledge Claire K. Luber for assistance with manuscript preparation. 

 (1) V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

 (3) V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

 (4) V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

 (5) V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

 (6) V. Sikirzhytski, A. Sikirzhytskaya, and I.K. Lednev, 

(12) N. Vandenberg and R.A.H. van Oorschot, 

(13) H.J. Kobus, E. Silenieks, and J. Scharnberg, 

(16) R.H. Mokashi, A.G. Malwankar, and M.S. Madiwale, 

(17) T.A. Brettell, J.M. Butler, and J.R. Almirall, 

(18) C. Haas, B. Klesser, C. Maake, W. Bar, and A. Kratzer, 

(23) J.M. Chalmers and P.R. Griffiths, Eds., 

Applications in Life, Pharmaceutical and Natural Sciences

 (John Wiley & Sons, Ltd, Chichester, UK, 2002). 

In this study, regression and classification chemometrical algorithms were combined to achieve effective discrimination of pure body fluids from their binary mixtures.

Igor K. Lednev

Articles

Raman Spectroscopy is Solving the Perpetual Problem of CSI: The Time of a Crime

Raman Spectroscopy Coupled with Advanced Chemometrics for Forensic Analysis of Semen and Blood Mixtures