In Situ Identification of Semen Stains on Common Substrates via Raman Spectroscopy,,

The spectroscopic identification of body fluids in situ is a major objective in forensic science. This approach offers the confirmatory, nondestructive, rapid, and on‐scene identification of various body fluids. Although Raman spectroscopy has shown tremendous promise toward this goal in prior proof‐of‐concept experiments, a significant challenge which still remains is substrate interference. Here, an approach for detecting semen stains in situ on various substrates using Raman spectroscopy is explored. Simulated semen evidence was prepared on skin, glass, and various fabrics. Raman data were accumulated from stains without any pretreatment using a common confocal mapping spectrometer using 785 nm laser excitation. The results demonstrate that the spectroscopic interferences encountered by substrates can be reduced and eliminated using a combination of existing subtraction techniques and chemometric models. Heterogeneous substrates proved most challenging, however, automatic subtraction treatment, and location of fluid hotspots was able to elucidate a clear spectroscopic signature of semen in every instance.     

Hierarchical Cluster Analysis of Ignitable Liquids Based on the Total Ion Spectrum

Gas chromatography–mass spectrometry (GC–MS) data of ignitable liquids in the Ignitable Liquids Reference Collection (ILRC) database were processed to obtain 445 total ion spectra (TIS), that is, average mass spectra across the chromatographic profile. Hierarchical cluster analysis, an unsupervised learning technique, was applied to find features useful for classification of ignitable liquids. A combination of the correlation distance and average linkage was utilized for grouping ignitable liquids with similar chemical composition. This study evaluated whether hierarchical cluster analysis of the TIS would cluster together ignitable liquids of the same ASTM class assignment, as designated in the ILRC database. The ignitable liquids clustered based on their chemical composition, and the ignitable liquids within each cluster were predominantly from one ASTM E1618‐11 class. These results reinforce use of the TIS as a tool to aid in forensic fire debris analysis.     

Rapid and nondestructive analysis of lipstick on different substrates using ATR-FTIR spectroscopy and chemometrics

Lipstick can be an important piece of evidence in crimes like murders, rapes, and suicides. Due to its prevalence, it can be an important corroborative evidence in crime reconstruction. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. We report the use of nondestructive ATR-FTIR spectroscopy combined with chemometrics for the classification of 10 brands of lipsticks with nine samples each. Chemometric method of partial least square-discriminant analysis (PLS-DA) has been employed to interpret the data and classify the samples into their respective classes. The PLS-DA model provides an AUC figure above 0.99 in all brands except one; for which it is slightly less at 0.94. We have also tested the traces of these lipstick samples on different substrates treating them as unknowns in the already trained PLS-DA model. 100% of the samples on nine substrates viz. a cotton, nylon, plastic, dry tissue, denim (blue jeans), wet tissue, nitrile gloves, white paper, and polyester were correctly attributed to their source brand. In conclusion, the results suggest that ATR-FTIR combined with the chemometrics is a rapid, nondestructive, and accurate method for the discrimination and source attribution of lipstick. This study has potential for use in actual forensic casework conditions.     

Using Cluster Analysis and ICP‐MS to Identify Groups of Ecstasy Tablets in Sao Paulo State,Brazil

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty‐five elements were determined by ICP‐MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K‐means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave‐one‐out cross‐validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples.     

Visualization of Latent Blood Stains Using Visible Reflectance Hyperspectral Imaging and Chemometrics

The detection of latent traces is an important aspect of crime scene investigation. Blood stains on black backgrounds can be visualized using chemiluminescence, which is invasive and requires a darkened room, or near-infrared photography, for which investigators need to change filters manually to optimize contrast. We demonstrated the performance of visible reflectance hyperspectral imaging (400–720 nm) for this purpose. Several processing methods were evaluated: single wavelength bands, ratio images, principal component analysis (PCA), and “SIMPLe-to-use Interactive Self-modeling Mixture Analysis” (SIMPLISMA). Using these methods, we were able to enhance the contrast between blood stains and 12 different fabrics. On black cotton, blood dilutions were visible with a minimal concentration of 25% of whole blood. The hyperspectral camera system used in this study is portable and wireless, which makes it suitable for crime scene use. The described technique is noncontact and nondestructive, so all traces are preserved for further analysis.     

Progress Toward the Determination of Correct Classification Rates in Fire Debris Analysis,,

Principal components analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to develop a multistep classification procedure for determining the presence of ignitable liquid residue in fire debris and assigning any ignitable liquid residue present into the classes defined under the American Society for Testing and Materials (ASTM) E 1618‐10 standard method. A multistep classification procedure was tested by cross‐validation based on model data sets comprised of the time‐averaged mass spectra (also referred to as total ion spectra) of commercial ignitable liquids and pyrolysis products from common building materials and household furnishings (referred to simply as substrates). Fire debris samples from laboratory‐scale and field test burns were also used to test the model. The optimal model's true‐positive rate was 81.3% for cross‐validation samples and 70.9% for fire debris samples. The false‐positive rate was 9.9% for cross‐validation samples and 8.9% for fire debris samples.     

Rapid Localization of Bone Fragments on Surfaces using Back‐Projection and Hyperspectral Imaging

Manual localization of bone fragments on the ground or on complex surfaces in relation to accidents or criminal activity may be time‐consuming and challenging. It is here investigated whether combining a near‐infrared hyperspectral camera and chemometric modeling with false color back‐projection can be used for rapid localization of bone fragments. The approach is noninvasive and highlights the spatial distribution of various compounds/properties to facilitate manual inspection of surfaces. Discriminant partial least squares regression is used to classify between bone and nonbone spectra from the hyperspectral camera. A predictive model (>95% prediction ability) is constructed from raw chicken bones mixed with stone, sand, leaves, moss, and wood. The model uses features in the near‐infrared spectrum which may be selective for bones in general and is able to identify a wide variety of bones from different animals and contexts, including aged and weathered bone.     

Detection and Classification of Ignitable Liquid Residues in the Presence of Matrix Interferences by Using Direct Analysis in Real Time Mass Spectrometry,

Conventional Gas Chromatography‐Mass Spectrometry (GC‐MS) methods for the analysis of ignitable liquids (ILs) are usually time‐consuming, and the data produced are difficult to interpret. A fast IL screening method using direct analysis in real time mass spectrometry (DART‐MS) is proposed in this study. GC‐MS, QuickStrip DART‐MS, and thermal desorption DART‐MS methods were used to analyze neat ILs and thermal desorption DART‐MS without extraction was used to analyze ILs on five substrates (e.g., carpet, wood, cloth, sand, and paper). Compared to GC‐MS, DART‐MS methods generated different spectral profiles for neat ILs with more peaks in the higher mass range and also provided better detection of less volatile compounds. ILs on substrates were successfully classified (98 ± 1%) using partial least squares discriminant analysis (PLS‐DA) models based on thermal desorption DART‐MS data. This study shows that DART‐MS has great potential for the high‐throughput screening of ILs on substrates.     

Total Ion Spectra versus Segmented Total Ion Spectra as Preprocessing Tools for Gas Chromatography – Mass Spectrometry Data

Alignment of fire debris data from GC‐MS for chemometric analysis is challenged by highly variable, uncontrolled sample and matrix composition. The total ion spectrum (TIS) obviates the need for alignment but loses all separation information. We introduce the segmented total ion spectrum (STIS), which retains the advantages of TIS while retaining some retention information. We compare the performance of STIS with TIS for the classification of casework fire debris samples. TIS and STIS achieve good model prediction accuracies of 96% and 98%, respectively. Baseline removal improved model prediction accuracies for both TIS and STIS to 97% and 99%, respectively. The importance of maintaining some chromatographic information to aid in deciphering the underlying chemistry of the results and reasons for false positive/negative results was also examined.     

Provenance Establishment of Stingless Bee Honey Using Multi‐element Analysis in Combination with Chemometrics Techniques

As consumption of stingless bee honey has been gaining popularity in many countries including Malaysia, ability to identify accurately its geographical origin proves pertinent for investigating fraudulent activities for consumer protection. Because a chemical signature can be location‐specific, multi‐element distribution patterns may prove useful for provenancing such product. Using the inductively coupled‐plasma optical emission spectrometer as well as principal component analysis (PCA) and linear discriminant analysis (LDA), the distributions of multi‐elements in stingless bee honey collected at four different geographical locations (North, West, East, and South) in Johor, Malaysia, were investigated. While cross‐validation using PCA demonstrated 87.0% correct classification rate, the same was improved (96.2%) with the use of LDA, indicating that discrimination was possible for the different geographical regions. Therefore, utilization of multi‐element analysis coupled with chemometrics techniques for assigning the provenance of stingless bee honeys for forensic applications is supported.