Brazilian forensic casework analysis through MPS applications: Statistical weight-of-evidence and biological nature of criminal samples as an influence factor in quality metrics.

Real forensic casework biological evidence can be found in a myriad of different conditions and presenting very distinct features, including key elements such as degradation levels, the nature of biological evidence, mixture presence, and surface or substrate deposition, among others. Technical protocols employed by forensic DNA analysts must consider such characteristics in order to improve the chances of successfully genotyping these materials. MPS has been used as a very useful tool for forensic sample processing and genetic profile generation. However, it is not completely clear how different features encountered with real forensic samples impact sequencing quality and, consequently, profile accuracy and reliability. In this context, the present study analyzes a set of 47 real forensic casework samples, obtained from semen, saliva, blood and epithelial evidence, as well as reference oral swabs, aiming to evaluate the impact of a sample's biological nature in profiling success. All DNA extracts from samples were standardized according to sample conditions, as assessed by traditional forensic profiling methods (real-time PCR quantitation and capillary electrophoresis-coupled STR fragment analysis). Samples were separated into groups according to their biological nature, and the resultant sequencing quality was evaluated through a series of well-established statistical tests, applied specifically to six different MPS quality metrics. The results showed that certain groups of samples, especially epithelial and (to a lesser extent) saliva samples, exhibited significantly lower quality in terms of some of the evaluated metrics. A number of reasons for such unexpected behavior are discussed. In addition, a series of calculations was performed to assess the weight of genetic evidence in Brazilian samples, and reflexes in data analysis and national allele frequency database construction are discussed. Overall, the results indicate that a unified national allele frequency database can be used nationwide. Besides this, MPS genetic profiles obtained from samples with particular biological origins may benefit from meticulous manual review, and visual inspection could be important as an additional step to avoid genotyping errors or misinterpretation, leading to more trustworthy and reliable results in real criminal forensic casework analysis.

Full mtDNA genome sequencing of Brazilian admixed populations: A forensic-focused evaluation of a MPS application as an alternative to Sanger sequencing methods.

The use of Massive Parallel Sequencing (MPS) techniques have been proposed by the forensic community as an alternative to Sanger sequencing methods in routine forensic casework analysis regarding mitochondrial DNA (mtDNA). Interesting features of MPS include high throughput, ability to simultaneously genotype a significant number of samples by barcoding techniques, processing automation, reduced time and costs, among others. Advantages include the capability of generating full mtDNA genome sequences versus usual techniques, usually limited to hypervariable or control regions exclusively. In this work, 96 reference single-source samples from three different Brazilian cities were subjected to full mtDNA genome sequencing by MPS techniques using an early-access version of Precision ID mtDNA Whole Genome Panel on an Ion Torrent PGM platform (Thermo Fisher Scientific, Waltham, MA, USA). Complete, high-quality sequences were obtained and sequencing performance was evaluated via four different metrics. As a subset of evaluated samples have been previously submitted for Sanger sequencing of the control region, a comparative analysis of both methods' results was conducted in order to compare technique adequacy within a forensic context. Even though this study is one of the first to report full mtDNA genome sequences for Brazilian admixed populations, the observed haplotypes exhibit a predominance of Native American and African maternal lineages in the studied sample set, reproducing results described in the literature for control regions only. Interpopulation analysis among Brazilian and 26 worldwide populations was also carried out. The results indicate that MPS-generated full mtDNA genome sequences may have great utility in forensic real casework applications, with a pronounced gain of genetic information and discrimination power provided by coding region evaluation and the enhanced capacity of heteroplasmies determination. Database construction and other relevant factors concerning implementation of such techniques in Brazilian forensic laboratories are also discussed.

Interval importance index to select relevant ATR-FTIR wavenumber Intervals for falsified drug classification.

The commerce of falsified drugs has substantially grown in recent years due to facilitated access to technologies needed for copying authentic pharmaceutical products. Attenuated Total Reflectance coupled with Fourier Transform Infrared (ATR-FTIR) spectroscopy has been successfully employed as an analytical tool to identify falsified products and support legal agents in interrupting illegal operations. ATR-FTIR spectroscopy typically yields datasets comprised of hundreds of highly correlated wavenumbers, which may compromise the performance of classical multivariate techniques used for sample classification. In this paper we propose a new wavenumber interval selection method aimed at selecting regions of spectra that best discriminate samples of seized drugs into two classes, authentic or falsified. The discriminative power of spectra regions is represented by an Interval Importance Index (III) based on the Two-Sample Kolmogorov-Smirnov test statistic, which is a novel proposition of this paper. The III guides an iterative forward approach for wavenumber selection; different data mining techniques are used for sample classification. In 100 replications using the best combination of classification technique and wavenumber intervals, we obtained average 99.87% accurate classifications on a Cialis® dataset, while retaining 12.5% of the authentic wavenumbers, and average 99.43% accurate classifications on a Viagra® dataset, while retaining 23.75% of the authentic wavenumbers. Our proposition was compared with alternative approaches for individual and interval wavenumber selection available in the literature, always leading to more consistent and easier to interpret results.

Wavenumber selection method to determine the concentration of cocaine and adulterants in cocaine samples.

Street cocaine is typically altered with several compounds that increase its harmful health-related side effects, most notably depression, convulsions, and severe damages to the cardiovascular system, lungs, and brain. Thus, determining the concentration of cocaine and adulterants in seized drug samples is important from both health and forensic perspectives. Although FTIR has been widely used to identify the fingerprint and concentration of chemical compounds, spectroscopy datasets are usually comprised of thousands of highly correlated wavenumbers which, when used as predictors in regression models, tend to undermine the predictive performance of multivariate techniques. In this paper, we propose an FTIR wavenumber selection method aimed at identifying FTIR spectra intervals that best predict the concentration of cocaine and adulterants (e.g. caffeine, phenacetin, levamisole, and lidocaine) in cocaine samples. For that matter, the Mutual Information measure is integrated into a Quadratic Programming problem with the objective of minimizing the probability of retaining redundant wavenumbers, while maximizing the relationship between retained wavenumbers and compounds' concentrations. Optimization outputs guide the order of inclusion of wavenumbers in a predictive model, using a forward-based wavenumber selection method. After the inclusion of each wavenumber, parameters of three alternative regression models are estimated, and each model's prediction error is assessed through the Mean Average Error (MAE) measure; the recommended subset of retained wavenumbers is the one that minimizes the prediction error with maximum parsimony. Using our propositions in a dataset of 115 cocaine samples we obtained a best prediction model with average MAE of 0.0502 while retaining only 2.29% of the original wavenumbers, increasing the predictive precision by 0.0359 when compared to a model using the complete set of wavenumbers as predictors.

Multicriteria wavenumber selection in cocaine classification.

Cocaine ATR-FTIR spectra consist of a large number of wavenumbers that typically decreases the performance of exploratory and predictive multivariate techniques. This paper proposes a framework for selecting the most relevant wavenumbers to classify cocaine samples into two categories regarding chemical composition, i.e. salt and base. The proposed framework builds a wavenumber importance index based on the Bhattacharyya distance (BD) followed by a procedure that removes wavenumbers from the spectra according to the order suggested by the BD index. The recommended wavenumber subset is chosen based on multiple criteria assessing classification performance, which are recalculated after each wavenumber is eliminated. The method was applied to ATR-FTIR spectra from 513 samples of cocaine, remarkably reducing the percent of retained wavenumbers and yielding near to perfect classifications in the testing set. In addition, we compared our propositions with other methods tailored to wavenumber selection; we found that the proposed framework, which relies on simple mathematical fundamentals, yielded competitive results.