Institute of Forensic Research in Cracow

Kraków, Poland

Institute of Forensic Research in Cracow

Kraków, Poland

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Martyna A.,Jagiellonian University | Martyna A.,Institute of Forensic Research in Cracow | Zadora G.,Institute of Forensic Research in Cracow | Zadora G.,University of Silesia | And 3 more authors.
Analytica Chimica Acta | Year: 2016

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. © 2016 Elsevier B.V.


Martyna A.,Jagiellonian University | Michalska A.,Institute of Forensic Research in Cracow | Zadora G.,Institute of Forensic Research in Cracow | Zadora G.,University of Silesia
Analytical and Bioanalytical Chemistry | Year: 2015

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.[Figure not available: see fulltext.] © 2015 Springer-Verlag Berlin Heidelberg


PubMed | Institute of Forensic Research in Cracow, University of Silesia, Jagiellonian University and University of Glasgow
Type: | Journal: Analytica chimica acta | Year: 2016

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.


Soltyszewski I.,University of Warmia and Mazury | Pepinski W.,Medical University of Bialystok | Wolanska-Nowak P.,Institute of Forensic Research in Cracow | Maciejewska A.,Medical University of Gdańsk | And 7 more authors.
Forensic Science International: Genetics | Year: 2014

The objective of the research was to provide a comprehensive database of autosomal microsatellite loci included in AmpFlSTR NGM PCR kit for a population of Poland considering possible genetic differentiation of a forensic interest. Fifteen STR markers were analyzed in 2041 unrelated individuals residing in eight geographically different regions. All the loci were found to be in Hardy-Weinberg equilibrium. The combined probability of match is 3.52 × 10-19 and the combined Power of Exclusion is 0.9999998. The F ST estimate over all 15 STRs is 0.0051 for the Polish population. We established that a combined NGM database may be employed for a Polish population. © 2013 Elsevier Ireland Ltd.


PubMed | University of Warmia and Mazury, Medical University of Bialystok, Institute of Forensic Research in Cracow, Poznan University of Medical Sciences and 4 more.
Type: | Journal: Forensic science international. Genetics | Year: 2014

The objective of the research was to provide a comprehensive database of autosomal microsatellite loci included in AmpFlSTR NGM PCR kit for a population of Poland considering possible genetic differentiation of a forensic interest. Fifteen STR markers were analyzed in 2041 unrelated individuals residing in eight geographically different regions. All the loci were found to be in Hardy-Weinberg equilibrium. The combined probability of match is 3.52 10(-19) and the combined Power of Exclusion is 0.9999998. The F(ST) estimate over all 15 STRs is 0.0051 for the Polish population. We established that a combined NGM database may be employed for a Polish population.


Ossowski A.,Pomeranian Medical University | Kus M.,Pomeranian Medical University | Kupiec T.,Institute of Forensic Research in Cracow | Bykowska M.,Polish Academy of Sciences | And 3 more authors.
Forensic Science International | Year: 2016

This paper describes the creation of the Polish Genetic Database of Victims of Totalitarianism and the first research conducted under this project. On September 28th 2012, the Pomeranian Medical University in Szczecin and the Institute of National Remembrance-Commission for Prosecution of Crimes against the Polish Nation agreed to support the creation of the Polish Genetic Database of Victims of Totalitarianism (PBGOT, www.pbgot.pl). The purpose was to employ state-of-the-art methods of forensic genetics to identify the remains of unidentified victims of Communist and Nazi totalitarian regimes. The database was designed to serve as a central repository of genetic information of the victim's DNA and that of the victim's nearest living relatives, with the goal of making a positive identification of the victim. Along the way, PGBOT encountered several challenges. First, extracting useable DNA samples from the remains of individuals who had been buried for over half a century required forensic geneticists to create special procedures and protocols. Second, obtaining genetic reference material and historical information from the victim's closest relatives was both problematic and urgent. The victim's nearest living relatives were part of a dying generation, and the opportunity to obtain the best genetic and historical information about the victims would soon die with them.For this undertaking, PGBOT assembled a team of historians, archaeologists, forensic anthropologists, and forensic geneticists from several European research institutions. The field work was divided into five broad categories: (1) exhumation of victim remains and storing their biological material for later genetic testing; (2) researching archives and historical data for a more complete profile of those killed or missing and the families that lost them; (3) locating the victim's nearest relatives to obtain genetic reference samples (swabs), (4) entering the genetic data from both victims and family members into a common database; (5) making a conclusive, final identification of the victim.PGBOT's first project was to identify victims of the Communist regime buried in hidden mass graves in the Powazki Military Cemetery in Warsaw. Throughout 2012 and 2013, PGBOT carried out archaeological exhumations in the Powazki Military Cemetery that resulted in the recovery of the skeletal remains of 194 victims in several mass graves. Of the 194 sets of remains, more than 50 victims have been successfully matched and identified through genetic evidence. © 2015 Elsevier Ireland Ltd.


PubMed | Pomeranian Medical University, Polish Academy of Sciences, Institute of Forensic Research in Cracow, Norwegian University of Science and Technology and Texas Rio Grande Legal Aid
Type: | Journal: Forensic science international | Year: 2016

This paper describes the creation of the Polish Genetic Database of Victims of Totalitarianism and the first research conducted under this project. On September 28th 2012, the Pomeranian Medical University in Szczecin and the Institute of National Remembrance-Commission for Prosecution of Crimes against the Polish Nation agreed to support the creation of the Polish Genetic Database of Victims of Totalitarianism (PBGOT, www.pbgot.pl). The purpose was to employ state-of-the-art methods of forensic genetics to identify the remains of unidentified victims of Communist and Nazi totalitarian regimes. The database was designed to serve as a central repository of genetic information of the victims DNA and that of the victims nearest living relatives, with the goal of making a positive identification of the victim. Along the way, PGBOT encountered several challenges. First, extracting useable DNA samples from the remains of individuals who had been buried for over half a century required forensic geneticists to create special procedures and protocols. Second, obtaining genetic reference material and historical information from the victims closest relatives was both problematic and urgent. The victims nearest living relatives were part of a dying generation, and the opportunity to obtain the best genetic and historical information about the victims would soon die with them. For this undertaking, PGBOT assembled a team of historians, archaeologists, forensic anthropologists, and forensic geneticists from several European research institutions. The field work was divided into five broad categories: (1) exhumation of victim remains and storing their biological material for later genetic testing; (2) researching archives and historical data for a more complete profile of those killed or missing and the families that lost them; (3) locating the victims nearest relatives to obtain genetic reference samples (swabs), (4) entering the genetic data from both victims and family members into a common database; (5) making a conclusive, final identification of the victim. PGBOTs first project was to identify victims of the Communist regime buried in hidden mass graves in the Powzki Military Cemetery in Warsaw. Throughout 2012 and 2013, PGBOT carried out archaeological exhumations in the Powzki Military Cemetery that resulted in the recovery of the skeletal remains of 194 victims in several mass graves. Of the 194 sets of remains, more than 50 victims have been successfully matched and identified through genetic evidence.


Lachowicz T.,Jagiellonian University | Zieba-Palus J.,Institute of Forensic Research in Cracow | Koscielniak P.,Jagiellonian University | Koscielniak P.,Institute of Forensic Research in Cracow
Analytical Letters | Year: 2013

A pyrolysis gas chromatography-mass spectrometry (GC-MS) method was applied for the comparative analysis of 42 samples of rubber collected from passenger car tires. It was found that rubber samples originating from different tires can, in most cases, be effectively differentiated. The rubber samples were first assigned to one of three classes on the basis of the main components present in chromatograms (styrene and limonene). Then peaks obtained from trace constituents of the rubber were taken into account. In most cases, the differences between the analyzed samples were sufficient to distinguish them. In this study, an on-line derivatization technique (using tetramethylammonium hydroxide) was applied. In some cases, this technique made it possible to demonstrate differences that were invisible in normal analysis. The study showed that the pyrolysis GC-MS method was an effective tool to differentiate between samples of tire rubber. The estimated discrimination power without derivatization ranged from 90% (styrene-butadiene rubber based) to 100% (natural/styrene-butadiene rubber samples). Derivatization increased the former discrimination power to 99%. © 2013 Copyright Taylor and Francis Group, LLC.

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