Determination of time since deposition of bloodstains through NIR and UV-Vis spectroscopy - A critical comparison.

Time elapsed since bloodstain deposition is a crucial aspect in forensic investigations, where non-destructive spectroscopic methods play a pivotal role. While extensive research has been conducted by UV-Vis spectroscopy, showcasing its utility in specific cases, there is still a paucity of studies based on NIR spectroscopy, which has the potential to overcome the limitations of the UV-Vis-based methods. To compensate for this disequilibrium, the present study aimed to evaluate the NIR applicability for estimating the age of forensic bloodstains and develop a performance comparison with UV-Vis spectroscopy methods. Capillary blood was sampled and subjected to a 16-day aging, during which it was repeatedly analyzed using both spectroscopic methods. Subsequently, chemometric analysis was applied to process the spectral data and independently assess the methods' performance. Classical preprocessing transforms (i.e., Savitzky-Golay derivatives and SNV transform) were used together with more targeted strategies, such as class centering, whose benefit was highlighted by PCA. Lastly, PLS regression models were computed to evaluate the effectiveness of both spectroscopic methods in estimating the time elapsed since blood trace deposition. Comparable root mean square errors in prediction (RMSEP) - 40 and 55 h for UV-Vis and NIR spectroscopy, respectively - were observed for both techniques, featuring an improvement with respect to the existing literature for NIR spectroscopy. Data fusion strategies for a multi-instrumental platform were also explored, evaluating advantages and disadvantages of low-level and mid-level approaches. The results indicated that NIR spectroscopy integrated with adequate chemometric strategies deserves increased appreciation in forensic bloodstain dating.

Breaking with trends in forensic dating: A likelihood ratio-based comparison approach.

Further steps toward understanding the time-related information contained within bloodstains found at the crime scene are rightly considered a top priority in forensic science. Contrary to widely held assumptions, the reason for the delayed exploitation of bloodstains dating methods in practice is not the lack of suitable analytical techniques for monitoring degradation processes. The problem lies in the variability of the environmental and circumstantial conditions, playing a vital role in the degradation kinetics of blood deposits. The present article demonstrates the possibility of breaking with current approaches based on absolute age estimations to finally answer time-centered questions in real forensic scenarios. The proposed novel framework for situating forensic traces in time is based on the likelihood ratio assessment of the (dis)similarity between the evidence decomposition and sets of reference materials obtained through supervised aging. In such a strategy, every dating procedure is constructed on a case-by-case basis to fit examined blood traces, thereby limiting the adverse influence of external factors on the validity of age estimations and providing a way for future crime scene implementation.

Differentiation of oleoresin capsicum sprays based on their capsaicinoid profiles.

Oleoresin capsicum (OC) sprays, often referred to as 'pepper sprays', contain a solution of active compounds, exerting an irritating effect on the human body. The active component of OC sprays are capsaicinoids, obtained by extraction from peppers. The profiles (quantitative relations) of natural capsaicinoids depend on the plant material, they were extracted from. Pepper spray is a non-lethal weapon that should only be used for self-defense but is often used by criminals to attack and incapacitate victims. Evidence related to these types of incidents, such as containers, clothes of victims or suspects, as well as traces of substances found at the scene, are submitted to the forensic laboratory. The purpose of the analysis is to identify the ingredients of the preparation (especially active components) and compare the traces found on objects from the victim or the scene of the incident with the preparation from the can or traces found on objects related to the suspect. The study aimed to investigate the possibility of differentiating OC gases based on capsaicinoid profiles recorded in GC-MS analyses. Sixty-four gases from 12 different manufacturers were purchased and tested. The likelihood ratio (LR) approach was applied to the data expressing the relative capsaicinoids contents computed by integrating GC-MS signals. Two hypotheses were assumed that stated either common or different origins of the samples. Several LR models have been developed, and their performance has been controlled by the number of false positives and false negatives as well as empirical cross entropy. The research results showed that differentiation was very successful, with more than 90% of correct responses. The results obtained show that OC sprays may be distinguished, even if they were produced by the same producer presumably if produced using different batches of pepper extract.

Toward a novel framework for bloodstains dating by Raman spectroscopy: How to avoid sample photodamage and subsampling errors.

Answers to questions about the time of bloodstains formation are often essential to unravel the sequence of events behind criminal acts. Unfortunately, the relevance of preserved evidence to the committed offence usually cannot be verified, because forensic experts are still incapable of providing an accurate estimate of the bloodstains' age. An antidote to this impediment might be substituting the classical dating approach - founded on the application of calibration models - by the comparison problem addressed using likelihood ratio tests. The key aspect of this concept involves comparing the evidential data with results characterizing reference bloodstains, formed during the process of supervised ageing so as to reproduce the evidence. Since this comparison requires data that conveys information inherent to changes accompanying the process of blood decomposition, this study provided a Raman-based procedure, designated for probing into the chemistry of ageing bloodstains. To circumvent limitations experienced with single-point measurements - the risk of laser-induced degradation of hemoglobin and subsampling errors - the rotating mode of spectral acquisition was introduced. In order to verify the performance of this novel sampling method, obtained spectra were confronted with those acquired during conventional static measurements. The visual comparison was followed by analysis of data structure using regularized MANOVA, which boosted the variance between differently-aged samples while minimizing the variance observed for bloodstains deposited at the same time. Studies of relation between these variances demonstrated the superiority of novel procedure, as it provided Raman signatures that enabled a better distinction between differently-aged bloodstains.

The evaluation of evidence for microspectrophotometry data using functional data analysis.

Microspectrophotometry data arise in the study of many forensically applicable situations. The situations here are those of ink and fibres. In a criminal investigation, data associated with a crime scene are compared with data associated with a person of interest. Methods based on the likelihood ratio are often used to evaluate such evidence. A technique known as functional data analysis for determining likelihood ratios using the full spectrum is described. It provides support comparing a proposition of common source with a proposition of different sources for data from the crime scene and from the person of interest. Data are available from ink, woollen and cotton fibres. The effectiveness of the method is assessed using false positive and false negative rates and Tippett plots in the comparison of data from known sources.

Evidence of seasonal variation of ethyl glucuronide in hair: Modeling a seven-year data series.

The assessment of chronic excessive alcohol consumption by ethyl glucuronide (EtG) determination in hair is generally based on a cut-off value of 30 pg/mg recognized by regulatory authorities and scientific societies that guide the decision process. The ongoing debate about the risks connected with the straightforward application of this cut-off refers to the factors that may influence the detected EtG concentration. The present contribution to this debate evaluates the seasonal variation of the averaged EtG values along a seven-year period. Over 65 000 data points have been statistically analyzed to provide a mathematical model that interprets the data, gives insight into several influencing factors, and forecasts progressive data-points of the time series. This model shows that there is an annual pattern in the data exhibiting lower EtG concentrations during warm seasons and higher values in cold seasons. The estimated EtG cycles are characterized by the seasonal variation of ±2.78 pg/mg above and below the overall mean (with 5.56 pg/mg absolute difference overall). This seasonal factor associated with EtG quantification might result in a potential source of bias, at least in the regional/climatic conditions observed in the samples' collection area. Moreover, the EtG time series reveals that the change in the sample pre-treatment procedure has an effect on the modeled pattern as an abrupt increment (+38%) in the mean value of the EtG concentration. This change corresponds to the time when the former protocol of cutting hair into small segments before extraction was substituted by pulverization with a ball mill.

Development and evaluation of semi-destructive, ultrasound assisted extraction method followed by gas chromatography coupled to mass spectrometry enabling discrimination of red lipstick samples.

In this study, the effective and robust semi-destructive ultrasound assisted extraction (UAE) method followed by gas chromatography coupled to mass spectrometry (GC-MS) method used for analysis of red lipsticks samples was developed, optimized and evaluated. 43 red lipsticks of a very similar hue representing of 21 different manufacturers were investigated. The red lipsticks (approximately 0.05 mg) were applied on a disc (of ø 2 mm) placed on specially designed stand printed with a 3D printer. In order to optimize the main factors affecting extraction process, Doehlert experimental design with response surface methodology was applied. The optimal for all analysed lipsticks UAE extraction conditions were: 21 min - time, 35 °C - temperature of the ultrasonic bath, and the 100 µL of extraction mixture of acetonitrile, methanol and acetone (50:30:20,  v/v/v). The developed, qualitative UAE/GC-MS method was evaluated and then successfully used for the differentiation of 43 red lipsticks. In this case, the two approaches were utilized: the visual inspection of chromatograms and the likelihood ratio model. The results confirmed that the proposed method has a great potential in lipsticks differentiation and after adaptation to real samples it seems to be a good alternative to the methods routinely used in forensic science investigations.

Geochemical wolframite fingerprinting - the likelihood ratio approach for laser ablation ICP-MS data.

Wolframite has been specified as a 'conflict mineral' by a U.S. Government Act, which obliges companies that use these minerals to report their origin. Minerals originating from conflict regions in the Democratic Republic of the Congo shall be excluded from the market as their illegal mining, trading, and taxation are supposed to fuel ongoing violent conflicts. The German Federal Institute for Geosciences and Natural Resources (BGR) developed a geochemical fingerprinting method for wolframite based on laser ablation inductively coupled plasma-mass spectrometry. Concentrations of 46 elements in about 5300 wolframite grains from 64 mines were determined. The issue of verifying the declared origins of the wolframite samples may be framed as a forensic problem by considering two contrasting hypotheses: the examined sample and a sample collected from the declared mine originate from the same mine (H1), and the two samples come from different mines (H2). The solution is found using the likelihood ratio (LR) theory. On account of the multidimensionality, the lack of normal distribution of data within each sample, and the huge within-sample dispersion in relation to the dispersion between samples, the classic LR models had to be modified. Robust principal component analysis and linear discriminant analysis were used to characterize samples. The similarity of two samples was expressed by Kolmogorov-Smirnov distances, which were interpreted in view of H1 and H2 hypotheses within the LR framework. The performance of the models, controlled by the levels of incorrect responses and the empirical cross entropy, demonstrated that the proposed LR models are successful in verifying the authenticity of the wolframite samples. Graphical abstract Geochemical wolframite fingerprinting.

Investigation of various factors influencing Raman spectra interpretation with the use of likelihood ratio approach.

The main aim of this study was to verify whether selected analytical parameters may affect solving the comparison problem of Raman spectra with the use of the likelihood ratio (LR) approach. Firstly the LR methodologies developed for Raman spectra of blue automotive paints obtained with the use of 785nm laser source (results published by the authors previously) were implemented for good quality spectra recorded for these paints with the use of 514.5nm laser source. For LR models construction two types of variables were used i.e. areas under selected pigments bands and coefficients derived from discrete wavelet transform procedure (DWT). Few experiments were designed for 785nm and 514.5nm Raman spectra databases after constructing well performing LR models (low rates of false positive and false negative answers and acceptable results of empirical cross entropy approach). In order to verify whether objective magnification described by its numerical aperture affects spectra interpretation, three objective magnifications -20×(N.A.=0.4.), 50×(N.A.=0.75) and 100×(N.A.=0.85) within each of the applied laser sources (514.5nm and 785nm) were tested for a group of blue solid and metallic automotive paints having the same sets of pigments depending on the applied laser source. The findings obtained by two types of LR models indicate the importance of this parameter for solving the comparison problem of both solid and metallic automotive paints regardless of the laser source used for measuring Raman signal. Hence, the same objective magnification, preferably 50× (established based on the analysis of within- and between-samples variability and F-factor value), should be used when focusing the laser on samples during Raman measurements. Then the influence of parameters (laser power and time of irradiation) of one of the recommended fluorescence suppression techniques, namely photobleaching, was under investigation. Analysis performed on a group of solid automotive paint samples showed that time of irradiation upon established laser power does not affect solving the comparison problem with the use of LR test. Likewise upon established time of irradiation 5% or 10% laser power could be used interchangeably without changing conclusions within this problem. However, upon the established time of irradiation changes in laser power between control and recovered sample from 5% or 10% to 50% may cause erroneous conclusions. Additionally it was also proved that prolonged irradiation of paint does not quantitatively affect pigments bands areas revealed after such a pre-treatment.

Direct and indirect alcohol biomarkers data collected in hair samples - multivariate data analysis and likelihood ratio interpretation perspectives.

The concentration values of direct and indirect biomarkers of ethanol consumption were detected in blood (indirect) or hair (direct) samples from a pool of 125 individuals classified as either chronic (i.e. positive) and non-chronic (i.e. negative) alcohol drinkers. These experimental values formed the dataset under examination (Table 1). Indirect biomarkers included: aspartate transferase (AST), alanine transferase (ALT), gamma-glutamyl transferase (GGT), mean corpuscular volume of the erythrocytes (MCV), carbohydrate-deficient-transferrin (CDT). The following direct biomarkers were also detected in hair: ethyl myristate (E14:0), ethyl palmitate (E16:0), ethyl stearate (E18:1), ethyl oleate (E18:0), the sum of their four concentrations (FAEEs, i.e. Fatty Acid Ethyl Esters) and ethyl glucuronide (EtG; pg/mg). Body mass index (BMI) was also collected as a potential influencing factor. Likelihood ratio (LR) approaches have been used to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers, as described in "Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes" (E. Alladio, A. Martyna, A. Salomone, V. Pirro, M. Vincenti, G. Zadora, 2017) [1].

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption and provide a suitable gradation to support the judgment.

A comment on the PCAST report: Skip the "match"/"non-match" stage.

This letter comments on the report "Forensic science in criminal courts: Ensuring scientific validity of feature-comparison methods" recently released by the President's Council of Advisors on Science and Technology (PCAST). The report advocates a procedure for evaluation of forensic evidence that is a two-stage procedure in which the first stage is "match"/"non-match" and the second stage is empirical assessment of sensitivity (correct acceptance) and false alarm (false acceptance) rates. Almost always, quantitative data from feature-comparison methods are continuously-valued and have within-source variability. We explain why a two-stage procedure is not appropriate for this type of data, and recommend use of statistical procedures which are appropriate.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure.

Interpretation of FTIR spectra of polymers and Raman spectra of car paints by means of likelihood ratio approach supported by wavelet transform for reducing data dimensionality.

The problem of interpretation of common provenance of the samples within the infrared spectra database of polypropylene samples from car body parts and plastic containers as well as Raman spectra databases of blue solid and metallic automotive paints was under investigation. The research involved statistical tools such as likelihood ratio (LR) approach for expressing the evidential value of observed similarities and differences in the recorded spectra. Since the LR models can be easily proposed for databases described by a few variables, research focused on the problem of spectra dimensionality reduction characterised by more than a thousand variables. The objective of the studies was to combine the chemometric tools easily dealing with multidimensionality with an LR approach. The final variables used for LR models' construction were derived from the discrete wavelet transform (DWT) as a data dimensionality reduction technique supported by methods for variance analysis and corresponded with chemical information, i.e. typical absorption bands for polypropylene and peaks associated with pigments present in the car paints. Univariate and multivariate LR models were proposed, aiming at obtaining more information about the chemical structure of the samples. Their performance was controlled by estimating the levels of false positive and false negative answers and using the empirical cross entropy approach. The results for most of the LR models were satisfactory and enabled solving the stated comparison problems. The results prove that the variables generated from DWT preserve signal characteristic, being a sparse representation of the original signal by keeping its shape and relevant chemical information.

A likelihood ratio model for the determination of the geographical origin of olive oil.

Food fraud or food adulteration may be of forensic interest for instance in the case of suspected deliberate mislabeling. On account of its potential health benefits and nutritional qualities, geographical origin determination of olive oil might be of special interest. The use of a likelihood ratio (LR) model has certain advantages in contrast to typical chemometric methods because the LR model takes into account the information about the sample rarity in a relevant population. Such properties are of particular interest to forensic scientists and therefore it has been the aim of this study to examine the issue of olive oil classification with the use of different LR models and their pertinence under selected data pre-processing methods (logarithm based data transformations) and feature selection technique. This was carried out on data describing 572 Italian olive oil samples characterised by the content of 8 fatty acids in the lipid fraction. Three classification problems related to three regions of Italy (South, North and Sardinia) have been considered with the use of LR models. The correct classification rate and empirical cross entropy were taken into account as a measure of performance of each model. The application of LR models in determining the geographical origin of olive oil has proven to be satisfactorily useful for the considered issues analysed in terms of many variants of data pre-processing since the rates of correct classifications were close to 100% and considerable reduction of information loss was observed. The work also presents a comparative study of the performance of the linear discriminant analysis in considered classification problems. An approach to the choice of the value of the smoothing parameter is highlighted for the kernel density estimation based LR models as well.

Wine authenticity verification as a forensic problem: an application of likelihood ratio test to label verification.

The aim of the study was to investigate the applicability of the likelihood ratio (LR) approach for verifying the authenticity of 178 samples of 3 Italian wine brands: Barolo, Barbera, and Grignolino described by 27 parameters describing their chemical compositions. Since the problem of products authenticity may be of forensic interest, the likelihood ratio approach, expressing the role of the forensic expert, was proposed for determining the true origin of wines. It allows us to analyse the evidence in the context of two hypotheses, that the object belongs to one or another wine brand. Various LR models were the subject of the research and their accuracy was evaluated by the Empirical cross entropy (ECE) approach. The rates of correct classifications for the proposed models were higher than 90% and their performance evaluated by ECE was satisfactory.

Chemical analysis of post explosion samples obtained as a result of model field experiments.

Five different explosives were detonated in a series of field experiments. Each experiment (detonation of the charge of each specific explosive) was repeated three times. The experiments were conducted under controlled conditions, exceeding those of research published so far. Detonated charges were uniform in size and, as far as possible, in shape. The explosives used originated from the same batch. Additionally, the same kind of electric detonators were used. Witness plates (sheets of galvanised steel 100 cm × 90 cm × 0.5 mm) were used to collect post-blast residues in a reproducible way. They were placed relatively close to the charge to minimise the influence of the wind. Samples were collected by systematic swabbing of the surface of the plate by acetone moistened cotton swabs. Samples were packed tight, transferred to the laboratory, and extracted with methanol. Extracts were concentrated by solvent evaporation, cleaned by centrifugation, and analysed using HPLC-DAD. Each extract was analysed three times and the mean value of the amount of the given explosive within the extract was calculated. For each of the explosive materials used the results of the repetition of the experiments proved them to be irreproducible. After each detonation of a specific charge different amounts of given explosives were found in post-blast samples. Also, the intuitively expected relationship between the distance from the charge and amount of post-blast residues were not observed. These results are consistent with previously published results of field experiments. The lack of reproducibility may be explained by differences in efficiency of detonation. The efficiency of a detonation may be influenced even by small differences in the shape of the charge as well as by the position and properties of the detonator. The lack of dependency between the amount of the explosive in the post-blast samples and the distance from the charge may be explained by the fact that during detonation, particles of unreacted explosives are not uniformly dispersed in all directions.

Information-theoretical assessment of the performance of likelihood ratio computation methods.

Performance of likelihood ratio (LR) methods for evidence evaluation has been represented in the past using, for example, Tippett plots. We propose empirical cross-entropy (ECE) plots as a metric of accuracy based on the statistical theory of proper scoring rules, interpretable as information given by the evidence according to information theory, which quantify calibration of LR values. We present results with a case example using a glass database from real casework, comparing performance with both Tippett and ECE plots. We conclude that ECE plots allow clearer comparisons of LR methods than previous metrics, allowing a theoretical criterion to determine whether a given method should be used for evidence evaluation or not, which is an improvement over Tippett plots. A set of recommendations for the use of the proposed methodology by practitioners is also given.