Agnieszka Martyna

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.

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.

Analysis of lead isotopic ratios of glass objects with the aim of comparing them for forensic purposes

This paper presents the possibilities of applying the likelihood ratio (LR) approach for the comparison problem to the data collected as a result of the Isotope Ratio Mass Spectrometry (IRMS) analysis targeted at lead (Pb)-isotope ratios. The assessment of the applied LR models performance was conducted by an Empirical Cross Entropy (ECE) approach. 35 glass samples were subjected to IRMS analysis and were described by Pb-isotope ratios: (208)Pb/(204)Pb, (207)Pb/(204)Pb, (206)Pb/(204)Pb, (208)Pb/(206)Pb, and (207)Pb/(206)Pb. Univariate and bivariate LR computations were performed, assuming normally distributed data subjected or not to a logarithmic transformation. Principal Component Analysis (PCA) was employed for creating orthogonal variables to propose an alternative LR model. It was found that the application of variable (208)Pb/(204)Pb seems to be promising as it delivers one of the lowest percentages of false positive and false negative rates as well as being the only variable for which an ECE plot gave satisfactory results.

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.

The evidential value of microspectrophotometry measurements made for pen inks

Three colour systems, defined by the International Commission on Illumination (CIE), have been used to parametrise spectra from microspectrophotometry in the visible range for ten replicates of each of forty inks. The parametrised spectra were used to calculate a likelihood ratio (LR) for pairwise comparisons under the propositions implying that any ink from some suspect document came from the same pen, as that from a control document, versus, the converse proposition which implies the ink from the suspect document came from some other pen. Both univariate and bivariate likelihood ratios for each colour system were calculated. Empirical cross-entropy was selected as an appropriate measure of performance for each system. The bivariate combinations of the CIE-xyz colour system achieve the best results as well as a bivariate combination of a and b variables within the CIE-Lab colour system.

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].

Optimized Sample Preparation for Glass Fragments by Scanning Electron Microscopy–Energy Dispersive X-ray Spectrometry

ABSTRACT Quantitative analysis by scanning electron microscopy–energy dispersive X-ray spectrometer (SEM–EDX) requires a flat and smooth sample surface. To fulfill these requirements, an embedding procedure is generally used for sample preparation. This approach is impractical for small glass fragments, which are frequently the subject of forensic examination. The authors report the use of optical microscopy for selecting glass fragments that are smooth and flat as possible and directly placing them on a scanning electron microscopy stub. The results using two SEM–EDXs were compared for embedded and nonembedded glass standards. No significant differences in accuracy, precision, reproducibility, and false answer rates were observed using likelihood ratio models suggesting that the reported method of sample preparation is suitable for forensic analysis.

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.