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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 abstractGeochemical wolframite fingerprinting Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1007-9) contains supplementary material, which is available to authorized users.

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.

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.

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription The aim of the study was to investigate the applicability of the likelihood ratio (LR) approach 19 for verifying the authenticity of 178 samples of 3 Italian wine brands: Barolo, Barbera, and 20Grignolino described by 27 parameters describing their chemical compositions. Since the 21 problem of products authenticity may be of forensic interest, the likelihood ratio approach, 22 expressing the role of the forensic expert, was proposed for determining the true origin of 23 wines. It allows us to analyse the evidence in the context of two hypotheses, that the object 24 belongs to 1˚ or 2˚ wine brand. Various LR models were the subject of the research and their 25 correctness was evaluated by the Empirical Cross Entropy (ECE) approach. The rates of 26 *Manuscript Click here to view linked References

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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.

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