The ability to analyze traces of body fluids is crucial to determining the key details of a crime. Nowadays, the combination of advanced statistical methods and multidimensional Raman spectral features shows the potential to solve the shortcomings of standard methods and to minimize false negatives and positives. And there are other potential uses for this approach.
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Via Vitali Sikirzhytski, Aliaksandra Sikirzhytskaya, and Igor K. Lednev
Editor's note: This article is based on V. Sikirzhytski, A. Sikirzhytskaya and IK Lednev, Appl. Spectroscopy. , 65, 11, 1223-1232 (2011).
Analyzing fluid traces during a forensic investigation is crucial to determining the key details of a crime. Ideally, analytical methods are deterministic and non-destructive and apply to multiple fluids. But none of the currently used methods meet these criteria: Standard biochemical tests are often destructive, require harmful chemicals and expertise, and do not have any tools to analyze all body fluids. 1,2
However, Raman spectroscopy shows the potential for rapid, non-destructive and confirmatory identification of as few as few milliliters of samples that have not been prepared. 3 It is the foundation of a novel and universal method of analyzing forensic evidence.
Providing spatial resolution of about 1 micron or less - about 10 times the mid-infrared (mid-infrared) spectroscopy - Raman microspectroscopy imaging and imaging is relatively easy with the help of the automation phase. In addition, portable instruments allow on-site analysis of drugs, bone, fingerprints and body fluids. 1,2,5-18
Reversing a mature standard
The standard method for identifying unknown samples is generally based on detecting a characteristic Raman spectrum in the recorded experimental spectrum or comparing the experimental spectrum as a whole with the spectra from the reference database. Although these methods have their place (for example, they are widely used in the process industry), our research shows the inherent heterogeneity of body fluid stains, multiple overlap of Raman bands and variable fluorescence backgrounds (contamination and substrate types It can also interfere with the analysis of unknown samples). A new method we developed combines Raman spectroscopy with advanced statistical methods and uses multidimensional Raman spectral features to overcome the complexity of standard methods and minimize false negatives and positives. 12-15,18
Multidimensional spectral signatures represent a set of spectra that represent the key features of a sample. 1,16 The experimental spectrum can be expressed as a linear combination of these components, ie as a spatial point whose coordinates are equal to the contribution of the corresponding spectrum. Identifying unknown objects includes searching for the best match between available multi-dimensional signatures and experimental data.
Each signature is designed to cover the inherent variability of the heterogeneous sample and the variability between the samples. In this way, the likelihood of false negative results is minimized. The probability of false positives can be controlled using statistical parameters from the signature fit: If the residuals for this fit are too large then the sample will be interpreted as not yet identified.
Raman methods and biological samples
Raman spectroscopy is based on inelastic scattering of laser light through the interaction with vibrating molecules. 19 Finally, Raman spectroscopy provides information on the interactions between the chemical structure of a molecule, the conformation of the molecule, the molecules and the surrounding environment, and the conditions of the physical state and matter.
Raman spectroscopy can be very complex and includes a wide range of superimposed Raman bands. This is especially true for biological samples, which typically include spectral features of multiple components: proteins, lipids, DNA, RNA, single amino acids, biochromosins (heme, carotenoids and melanin), and other metabolites . In this case, tremendous effort is required to extract useful information, such as applying a comprehensive chemometric approach.
Raman spectroscopy uses a variety of laser sources to provide excitation over a wide spectral range covering the UV, VIS and NIR (UV-VIS-NIR). This method is not limited by physical conditions and can be performed on gas, solid, liquid, gel, opaque and non-uniform samples with complex chemical composition. Recent studies have shown that under certain conditions (resonance and surface enhancement) even at the single molecule level. twenty four
Two main disadvantages of Raman spectroscopy are 1) the weakness of the Raman effect in the absence of resonance and surface enhancement, and 2) the possibility of fluorescence interference. The Raman effect is only 10 -6 -10 -9 per incident photon, which requires a sensitive and optimized photodetector. Fluorescence contribution can be minimized using temporal or spatially resolved Raman spectroscopy or by eliminating the fluorescence background from previously recorded Raman spectra using numerical methods.
Two-dimensional and / or three-dimensional spectral mapping allows data to be efficiently collected from essentially non-uniform samples. In general, acquiring Raman spectroscopy involves moving the sample in a stepwise fashion until the entire region of interest is characterized. Modern devices provide settings for plotting measurements, such as spectral acquisition from rectangular or circular areas (fill or focus check out), line, point or depth slices. Therefore, each spectrum represents a specific area of the sample.
There are several ways to work with spectral datasets: You can create various types of maps by analyzing data directly, curve fitting, or preprocessing; and developing topologies, components, principal component analysis (PCA), and multivariate graphs. 4,25,26
Unknown sample challenge
Many statistical methods can help solve common problems identifying unknown species. The application of multidimensional signatures described here has some similarities to the problem of mixed analytics. The local composition of the uneven stain can be treated as a mixture of several basic components. The relative concentrations of these ingredients in the sample may vary widely, but the list of ingredients under consideration is limited. Multidimensional spectral features of body fluids must account for the heterogeneity of dry trace and donor variation. The identification process needs to be simple to determine which set of pre-defined spectra (markers) is best suited for unknown experimental data. It is important to set the standard for "the most suitable".
Where ÿ I, ŷ I, and ȳ represent the actual, assembly and average. The large number of signatures developed and cross-fit support the validity of this approach.
Body fluid characteristics
The drying traces of body fluids are not only heterogeneous in nature, but also vary in chemical composition between their donors. For these reasons, no single Raman spectrum can adequately represent a particular body fluid. The method we propose is based on the fluid's unique multi-dimensional Raman feature.
In general, Raman spectra of biological samples have significant fluorescence contributions. Two strategies based on the concept of spectral features can be used to identify unknown stains. The first involves the expansion of multidimensional features with fluorescent background and horizontal lines - the latter accounting for the change in offset. 14 In this case, the raw experimental spectrum, which is not processed, can be fit by the signature component. The second strategy involves baseline correction of the original spectra prior to the statistical analysis and determination of the multi-dimensional signature containing only Raman components. 18
Both strategies have advantages. Previous baseline corrections left only highly informative Raman signals, whereas incorporation of a less informative but still characteristic fluorescent background may increase the specificity of multidimensional features. The spectral components of the multidimensional features of human blood, semen and saliva present only Raman contributions, whereas the contribution of the broader fluorescence signature is precluded by previous baseline corrections. Full multidimensional signatures of blood, saliva and semen include an average fluorescence background other than Raman spectral components, whereas both Raman and fluorescent components are stoichiometry to obtain multidimensional signatures of sweat and vaginal fluids. We found that these bodily fluids have different fluorescence backgrounds and should appear as a linear combination of several spectral distributions (two for sweat and three for vaginal fluid). The fact that all bodily fluids have different fluorescence components allows us to speculate that fluorescence should not be considered unpopular as it can serve as an important tool for characterizing specific bodily fluids. Use fluorescence components to test multidimensional signatures with sweat and vaginal fluids. The fact that all bodily fluids have different fluorescence components allows us to speculate that fluorescence should not be considered unpopular as it can serve as an important tool for characterizing specific bodily fluids. Use fluorescence components to test multidimensional signatures with sweat and vaginal fluids. The fact that all bodily fluids have different fluorescence components allows us to speculate that fluorescence should not be considered unpopular as it can serve as an important tool for characterizing specific bodily fluids. Use fluorescence components to test multidimensional signatures with sweat and vaginal fluids.
All calculated Raman features have been successfully applied to experimental data collected from multiple donors of different races, genders and ages. The high agreement of the statistical results confirms the high specificity of the developed features (see table).
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