Vitali Sikirzhytski

Raman spectroscopic analysis of gunshot residue offering great potential for caliber differentiation.

Near-infrared (NIR) Raman microspectroscopy combined with advanced statistics was used to differentiate gunshot residue (GSR) particles originating from different caliber ammunition. The firearm discharge process is analogous to a complex chemical reaction. The reagents of this process are represented by the chemical composition of the ammunition, firearm, and cartridge case. The specific firearm parameters determine the conditions of the reaction and thus the subsequent product, GSR. We found that Raman spectra collected from these products are characteristic for different caliber ammunition. GSR particles from 9 mm and 0.38 caliber ammunition, collected under identical discharge conditions, were used to demonstrate the capability of confocal Raman microspectroscopy for the discrimination and identification of GSR particles. The caliber differentiation algorithm is based on support vector machines (SVM) and partial least squares (PLS) discriminant analyses, validated by a leave-one-out cross-validation method. This study demonstrates for the first time that NIR Raman microspectroscopy has the potential for the reagentless differentiation of GSR based upon forensically relevant parameters, such as caliber size. When fully developed, this method should have a significant impact on the efficiency of crime scene investigations.

Multidimensional Raman Spectroscopic Signatures as a Tool for Forensic Identification of Body Fluid Traces: A Review

The analysis of body fluid traces during forensic investigations is a critical step in determining the key details of a crime. Several confirmatory and presumptive biochemical tests are currently utilized. However, these tests are all destructive, and no single method can be used to analyze all body fluids. This review outlines recent progress in the development of a novel universal approach for the nondestructive, confirmatory identification of body fluid traces using Raman spectroscopy. The method is based on the use of multidimensional spectroscopic signatures of body fluids and accounts for the intrinsic heterogeneity of dry traces and donor variation. The results presented here demonstrate that Raman spectroscopy has potential for identifying traces of semen, blood, saliva, sweat, and vaginal fluid with high confidence.

Raman spectroscopic signature of vaginal fluid and its potential application in forensic body fluid identification

Traces of human body fluids, such as blood, saliva, sweat, semen and vaginal fluid, play an increasingly important role in forensic investigations. However, a nondestructive, easy and rapid identification of body fluid traces at the scene of a crime has not yet been developed. The obstacles have recently been addressed in our studies, which demonstrated the considerable potential of Raman spectroscopy. In this study, we continued to build a full library of body fluid spectroscopic signatures. The problems concerning vaginal fluid stain identification were addressed using Raman spectroscopy coupled with advanced statistical analysis. Calculated characteristic Raman and fluorescent spectral components were used to build a multidimensional spectroscopic signature of vaginal fluid, which demonstrated good specificity and was able to handle heterogeneous samples from different donors.

Quantitative methods for structural characterization of proteins based on deep UV resonance Raman spectroscopy.

Here we report on novel quantitative approaches for protein structural characterization using deep UV resonance Raman (DUVRR) spectroscopy. Specifically, we propose a new method combining hydrogen-deuterium (HD) exchange and Bayesian source separation for extracting the DUVRR signatures of various structural elements of aggregated proteins including the cross-beta core and unordered parts of amyloid fibrils. The proposed method is demonstrated using the set of DUVRR spectra of hen egg white lysozyme acquired at various stages of HD exchange. Prior information about the concentration matrix and the spectral features of the individual components was incorporated into the Bayesian equation to eliminate the ill-conditioning of the problem caused by 100% correlation of the concentration profiles of protonated and deuterated species. Secondary structure fractions obtained by partial least squares (PLS) and least squares support vector machines (LS-SVMs) were used as the initial guess for the Bayessian source separation. Advantages of the PLS and LS-SVMs methods over the classical least squares calibration (CLSC) are discussed and illustrated using the DUVRR data of the prion protein in its native and aggregated forms.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: Semen and blood mixtures

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids.

Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood

Body fluids are a common and important type of forensic evidence. In particular, the identification of menstrual blood stains is often a key step during the investigation of rape cases. Here, we report on the application of near-infrared Raman microspectroscopy for differentiating menstrual blood from peripheral blood. We observed that the menstrual and peripheral blood samples have similar but distinct Raman spectra. Advanced statistical analysis of the multiple Raman spectra that were automatically (Raman mapping) acquired from the 40 dried blood stains (20 donors for each group) allowed us to build classification model with maximum (100%) sensitivity and specificity. We also demonstrated that despite certain common constituents, menstrual blood can be readily distinguished from vaginal fluid. All of the classification models were verified using cross-validation methods. The proposed method overcomes the problems associated with currently used biochemical methods, which are destructive, time consuming and expensive.

Multidimensional Raman spectroscopic signature of sweat and its potential application to forensic body fluid identification.

This proof-of-concept study demonstrated the potential of Raman microspectroscopy for nondestructive identification of traces of sweat for forensic purposes. Advanced statistical analysis of Raman spectra revealed that dry sweat was intrinsically heterogeneous, and its biochemical composition varies significantly with the donor. As a result, no single Raman spectrum could adequately represent sweat traces. Instead, a multidimensional spectroscopic signature of sweat was built that allowed for the presentation of any single experimental spectrum as a linear combination of two fluorescent backgrounds and three Raman spectral components dominated by the contribution from lactate, lactic acid, urea and single amino acids.

Circumventing substrate interference in the Raman spectroscopic identification of blood stains

Raman spectroscopy has demonstrated remarkable capabilities in identifying blood in controlled laboratory conditions. However, substrate interference presents a significant challenge toward characterizing body fluid traces with Raman spectroscopy at a crime scene. Here, several possible solutions are explored, including the selection of laser excitation, isolating the signal of blood using spectral subtraction and using a favorable substrate for collection which minimizes interference. Simulated blood stain evidence was prepared and analyzed using a Raman microscope with variable laser capabilities. It is shown that the best approach for detecting blood depends on the nature of the substrate and the type of interference encountered.

Forensic Identification of Blood in the Presence of Contaminations Using Raman Microspectroscopy Coupled with Advanced Statistics: Effect of Sand, Dust, and Soil

Body fluid traces recovered at crime scenes are among the most common and important types of forensic evidence. However, the ability to characterize a biological stain at a crime scene nondestructively has not yet been demonstrated. Here, we expand the Raman spectroscopic approach for the identification of dry traces of pure body fluids to address the problem of heterogeneous contamination, which can impair the performance of conventional methods. The concept of multidimensional Raman signatures was utilized for the identification of blood in dry traces contaminated with sand, dust, and soil. Multiple Raman spectra were acquired from the samples via automatic scanning, and the contribution of blood was evaluated through the fitting quality using spectroscopic signature components. The spatial mapping technique allowed for detection of “hot spots” dominated by blood contribution. The proposed method has great potential for blood identification in highly contaminated samples.

Attenuated total reflectance-FT-IR spectroscopy for gunshot residue analysis: potential for ammunition determination.

The ability to link a suspect to a particular shooting incident is a principal task for many forensic investigators. Here, we attempt to achieve this goal by analysis of gunshot residue (GSR) through the use of attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FT-IR) combined with statistical analysis. The firearm discharge process is analogous to a complex chemical process. Therefore, the products of this process (GSR) will vary based upon numerous factors, including the specific combination of the firearm and ammunition which was discharged. Differentiation of FT-IR data, collected from GSR particles originating from three different firearm-ammunition combinations (0.38 in., 0.40 in., and 9 mm calibers), was achieved using projection to latent structures discriminant analysis (PLS-DA). The technique was cross (leave-one-out), both internally and externally, validated. External validation was achieved via assignment (caliber identification) of unknown FT-IR spectra from unknown GSR particles. The results demonstrate great potential for ATR-FT-IR spectroscopic analysis of GSR for forensic purposes.