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Automobile glass forensics using suitable chemical analysis techniques is an important field in forensic science. In this respect, major elements help to classify the glass samples, whereas trace elements in conjunction with chemometrics are very important to help in grouping for source/origin finding. Five different radioanalytical techniques namely scanning electron microscopy energy dispersive X-ray spectroscopy (SEM-EDX), energy dispersive X-ray fluorescence (ED-XRF), particle induced gamma-ray/X-ray emission (PIGE/PIXE), and instrumental neutron activation analysis (INAA) were utilized for chemical characterization of automobile (car) windshield glasses and the results have been discussed for possible forensic applications. Methods were validated by analyzing geological and glass-certified/standard reference materials. The R&D work was carried out for 48 real case car windshield glass samples from 10 different manufacturers, whereas the method was standardized for 5 glasses, then applied to 20, and finally to 48 glass samples. From the results of all techniques, it was observed that trace elements like Sc, Cr, Co, Mn, Hf, Ta, Ba La, Ce, Sm, Eu, Yb, and Th obtained by INAA are very helpful for the grouping. Preliminary grouping of the first five samples was carried out utilizing concentration ratios with respect to Sc, La/Ce, and total rare earth elements (REEs) as well as from biplots and ternary plots. External (in air) PIGE and INAA methods were optimized and applied to "as received" glass fragments for confirming the class of glass from major elements and grouping study from trace elements, respectively. Different clustering and grouping methods of chemometrics like cluster analysis (CA) and principal component analysis (PCA) were employed to get the best grouping of similar glasses. PCA using trace elemental concentration was found to be the best approach. Final grouping confirmation of 48 automobile glasses was carried out by PCA, which indicated a clear nine groups from nine branded manufacturers, whereas one set of local glasses did not show any group. The study showed the efficacy of external PIGE and INAA for the classification and grouping study of these soda lime glasses and trace elements in conjunction with PCA for confirmation of grouping needed for forensic applications. [ABSTRACT FROM AUTHOR]

The detection of computer-generated document forgeries has always been a challenging task for forensic document examiners (FDE). With the aim to support the examination processes, Schottky field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FE-SEM-EDS) is explored as a recent tool to analyze black toners obtained from laser printers and photocopier machines. Forty samples each from the laser printer and photocopier machines are procured and studied for morphological features, elemental profile, and multivariate analysis. The acquired SEM images and spectra are evaluated to discriminate and classify the toners having a different source of origin. Multivariate analysis is applied to develop a model of classification to successfully classify the printed documents on the basis of the similarities and differences in their composition. Hierarchical cluster analysis (HCA) discriminates the printouts in the forms of groups based on their chemical composition. The laser printer and the photocopier printed documents are grouped into 11 and eight clusters, respectively, based on their elemental composition. Cross-validation is further conducted to assess the capabilities of developed principal component analysis (PCA) and linear discriminant analysis (LDA) models for the examination of printouts from unknown origin. [ABSTRACT FROM AUTHOR]

Abstract In forensic science, lipsticks are considered as crucial trace evidence because it helps in the linking of the criminal with the crime scene. In the present work, twenty-five lipstick samples are characterized and discriminated by using ATR-FTIR spectroscopy coupled with multivariate statistical methods. The utilized approach is non-destructive, fast, and provides reproducible results. It is observed from the FTIR spectra that lipstick contains various aliphatic and aromatic compounds e.g. Propyl ester of Hexanoic acid, Silicates, etc. Further, the discrimination power is calculated by using three approaches i.e. visual examination, cluster analysis (HCA and k-means) and factor analysis method. The multivariate method combined with t-statistics delivered a higher value of discriminating power i.e. 100% which is an improvement on the 99.00% discrimination power of visual comparison method. The developed method is validated by analyzing five duplicate samples and predicted them to their respective brands significantly. This study establishes a method which provides proof of concept discrimination of the lipstick samples. In the future, it can be quite possible to create an FTIR database of more lipstick samples for the identification of unknown/suspected lipstick samples. Graphical abstract Unlabelled Image Highlights • Lipstick samples are characterized and discriminated via FTIR and chemometric methods. • Lipstick shows the presence of Hexanoic acid, Aliphatic/aromatic compounds and Silicates. • Effects of heating are observed in the form of shifting and evaporation of spectral peaks. • Highest discrimination is achieved by multivariate and t-statistics for lipstick samples. [ABSTRACT FROM AUTHOR]

In this research, we characterized and discriminated 24 different kinds of writing/printing papers that may or may not have similar compositions by using Thermogravimetric Analysis (TGA). All paper samples were obtained from the local market in and around Chandigarh (Northwest India). Large differences were observed in the estimation of the kinetic parameters of cellulose and hemicellulose. The calculated percentage weight loss and activation energy for Reaction I and II was further used to determine the thermal stability of each sample. Although few differences were observed on visual examination of the samples, significant differences were obtained on the basis of their kinetic parameters. Multivariate analysis was further applied to the results and considerable enhancement of the results was observed. The current study provides 99.28% discrimination among all the possible pairs of paper samples. The technique is cost-effective, involves no sample preparation, consumes less time and provides reproducible results. [ABSTRACT FROM AUTHOR]

Abstract The potential to ascertain the origin and aging of bloodstains have various important applications in the forensic serological investigation. The individualization of bloodstain is necessary as both human and animals can be involved in the crimes. The foremost advantages of these types of investigation are: the expert significantly differentiates between human and animal blood which ultimately helps in the decipherment of criminal associated with the crimes; the aging of bloodstain assists in the determination of approximate time and date of incidence of crime. In recent time, significant advancement in analytical methods has been done to investigate the complex evidence collected from crime scene such as bloodstain. Along with modern analytical methods, the multivariate methods are also obtaining popularity in forensic science, especially in the bloodstain investigations. This review emphasizes the importance of analytical and chemometric models that have been utilized for the identification and aging of bloodstains. The reliability and sensitivity of these models are compared. Highlights • Advanced analytical and chemometric methods for bloodstain investigation are summarized. • Most of the studies utilized spectroscopy methods especially Raman and FTIR. • PLS-DA and regression-based modeling have been widely used for discrimination and dating studies. • New models like SVM and ANN provide better results than PLS-DA in discrimination as well as aging. [ABSTRACT FROM AUTHOR]

This review represents a detailed discussion of the multivariate methods used in the examination of forensic exhibits; their advantages, disadvantages, and efficiency are compared. The last decade has seen the application of the chemometric methods combined with analytical techniques for characterization and discrimination of samples, which leads to the informative and representative examinations of the samples. Many research articles with reference to the use of chemometrics in forensic science have been published. This review has been divided into various sections which include chemometrics, its history, multivariate methods, and the application of chemometrics in various disciplines of forensic science. It is suggested that these new techniques and mathematical/statistical methods should be utilized in forensic science casework to get statistical confidence in the results. [ABSTRACT FROM AUTHOR]

The dating of questioned document is a firmest problem for forensic experts. The current work is focused on the study of the dating of ink samples by using spectrophotometry technique and multiple regression analysis. A multiple linear regression (MLR) model has been proposed for the estimation of blue ballpoint pen ink dating by measuring the fading of ink with respect to time using UV–Vis spectrophotometer. Before developing the model, the factors like the best solvent for ink extraction and the conditions through which maximum ink is extracted has been determined. All the samples have been treated with the same method to avoid differences caused by the extraction procedure. Cross check of the predicted model has been done by analyzing the known writings. Validation of the equation has been done by calculating the aging of already known values of the same samples. The differences between expected and estimated dating were used to determine the average errors in the dating method. The standard errors have been minimized in case of MLR model significantly. [ABSTRACT FROM AUTHOR]

The present study offers a rapid and non-destructive approach for wood identification utilizing Attenuated total reflection Fourier transform infrared (ATR-FTIR) analytical technique with chemometric methods. This will estimate the source of origin of unknown wood samples or matching of two or more than two wood samples, for its applications in the wood and forensic science. The discrimination of wood samples is achieved by using multivariate statistical analysis including Hierarchical cluster analysis (HCA), and principal component analysis (PCA). The linear discrimination analysis (LDA) is also used for classifying the unknown wood samples into the respective class of wood sample. The presented methodology provides a proof-of-concept, fast, non-destructive, discrimination and classification of wood samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Forensic investigations favour quick and non-destructive identification and discrimination. • Machine learning techniques combined with ATR-FTIR become more prevalent as a way to enhance the precision of the measure. • 220 samples of rubber residues from automobile tyres from different brands had been distinguished. • 15 machine learning approaches have been explored for tyre brand discrimination and type of vehicle identification from rubber residues. • The Extra Tree Classifier outperforms other ML classifiers, in accordance with the outcome of this research. Criminal instances involving collision accidents, hit-and-run incidents, abduction, hostage-taking, and the unauthorised transit of forbidden items generally include evidence involving rubber traces from automobile tyres. These traces can be located on the road surface, in clothing, on the victim(s) themselves, or on items as skid marks following sudden stopping and spinning around. These traces serve as crucial evidence by reducing the range of suspects by revealing linkages between the getaway vehicle, the site of the crime, and the perpetrator through the tyre's brand, producer, or origin. This study offered a way for classifying 220 tyre rubber samples from different brands using various machine learning algorithms in PyCaret in conjunction with rapid and non-destructive ATR-FTIR spectroscopy equipped with diamond crystal. On spectral information from ATR-FTIR, pre-processing tools such as baseline correction, smoothing, derivatization, and normalisation were also implemented prior to machine learning. This approach has the potential to be advantageous for efficiently and non-destructively identifying rubber traces as forensic evidence and for facilitating brand recognition of automobile tyres. [ABSTRACT FROM AUTHOR]

Background: Variation in the speech of speakers is a crucial issue for the forensic system. The main reason behind incorrect speaker identification is greater intra-speaker fluctuation. In the forensic state of play, a lot of research has been carried out on speaker identification. However inter variations and intra fluctuations in speakers for the Punjabi language is still a grey area. Aims and Objectives: Our aim is to study acoustic analysis of fluctuations for inter and intra speaker variability in speech sounds. In our study, we will consider Punjabi vowel with consonants. The Statistical methods will be applied to analyze the data; firstly, the Shapiro-Wilk test will be checked for normality and then Levene's Test to assess the equality of variances. Materials and Method: Five vowels were selected with different consonants. They were combined to make meaningful words. Then these meaningful words were embedded in sentences. Ten speakers participated voluntarily. All are students of A.S College at Khanna in Punjab. The individuals were aged between 20-22 years with no hearing or speech disorder. The voice samples were recorded with help of good quality microphone and by Goldwave software in the sound proof lab. Samples were introduced directly into PRAAT software by the use of a Sony microphone and with sampling rate of 44100 Hz frequency. Acoustic Analysis has been done with help of Goldwave software in form of spectrograms. Results and Conclusion: Each formant shows a different value for inter variations and inter speaker fluctuations. F1 and F2 shows lesser speaker variation than the high-frequency region in F3 and F4, so we can say that in comparison with the lower part, high-frequency regions are more valuable. The assumptions for TWO-WAY ANOVA is violated and hence, we have used the non-parametric Friedman Test and performed its Post hoc analysis. From Posthoc analysis, we can say that F1 and F2 (p >0.05) and F2 and F3 (p>0.05) gave the same type of results. Hence, from the results of these statistical tests, we can conclude that F1 is recommended over F2, F3, and F4. As the frequency of F1 is high as well as in line with the results of statistical tests. Because we prefer more variation among frequencies so that we can easily distinguish different speakers and it would be more beneficial for inter variations and intra fluctuations. [ABSTRACT FROM AUTHOR]

In the present study, we have utilized attenuated total reflectance Fourier transform infrared spectroscopy for the examination of raw, pasteurized and adulterated milk samples. Principal component analysis and discriminant analysis have been applied for discrimination and classification purposes. According to the observations and the model of "goodness of fit", the obtained results explain 100% of the original classification of the dataset and leave one out cross-validation provides 93.74% of accurate classification. [ABSTRACT FROM AUTHOR]

Cosmetic products such as sunscreens may often be encountered in forensic investigations as traces left on tissue paper, apparels, drinkware, painted exterior, or various alternative surfaces. The pilot study aimed at efficiently using Infrared spectroscopy coupled with chemometrics to cater to unbiased, rapid, and non-destructive identification of sunscreens which will aid various forensic investigations soon. In the present research, a total of 109 sunscreen samples were analyzed using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy. The physicochemical data from the FTIR instrument was then subjected to principal component analysis (PCA), which successfully distinguished most of the samples based on their spectral information. The trained model resulted in clear segregation of unknown sunscreen samples. This could provide an outstanding level of confidence during the conduction of 'questioned versus known' comparisons of similar sunscreens and will give the details of the manufacturer, thereby, helping in rounding off the suspects. Unlabelled Image • 109 sunscreen samples were analyzed using ATR-FTIR & Chemometric methods. • No prior sample preparation needed for the devised method of characterization. • The trained model was validated using unknown samples. • The trained model resulted in discrimination accuracies to 100%. • First such study on sunscreen stains identification utilizing IR and Chemometric methods. [ABSTRACT FROM AUTHOR]

Kajal, also known as Kohl (Arabic name) a cosmetic, is used for the beautificationof eyes and face as a whole. It can serve as an important corroborative evidence in various crimes against women and can also be used as a writing instrument to write the anonymous letters, threatening notes, suicide letters on a variety of substrates such as cotton cloth, tissue paper, tissue wipes, skin etc. 40 Kajal samples from 33 different brands were collected and analyzed using Attenuated Total Reflectance Fourier Transform Infrared (ATR FT-IR) Spectroscopy with advance statistical and computational tools. The present research investigated the application of this technique for the differentiation of locally manufactured Kajal based on the differences in the peak patterns, frequencies, and strengths of peak exhibited within the fingerprint region. The blind test was conducted with four unknown samples which were pretended to be unknown to the analyst and it was observed that all the unknown samples were predicted correctly with their respective classes. Effect of substrates and ageing were also studied under similar set of experimental conditions. This technique has been proven to be of high significance with complete non-destructive nature requiring minimal or no sample preparation and high repeatability. [ABSTRACT FROM AUTHOR]