Forensic analysis of cigarette ash using ATR-FTIR spectroscopy and chemometric methods

Abstract

Cigarette ash is frequently found in crime scenes involving murder, suicide, rape, dacoity, or assault cases, but it is frequently overlooked as potential evidence by investigators. This ash as evidence can assist an investigator in figuring out the number of people who were there at the time of the crime as well as narrowing down the suspect pool. Because any type of ash, such as burnt paper, dhoop, agarbatti, cloth, tandoor, and so on, could be present at the crime scene, the first objective of this research study proposed a method for the characterization and discrimination of cigarette ash from other types using ATR-FTIR Spectroscopy in conjunction with statistical models PCA and PLSDA. In the second objective, this approach was used for the discrimination of cigarette and bidi ash. Finally, the same strategy is used to distinguish between ashes from three different cigarette brands because some people prefer one brand over another, which may be useful in certain situations. For this research study, we collected a total of 94 samples (47 unsmoked/fresh cigarette sticks of 20 brands from local vendors and 47 samples as other ash types from the surrounding area), 12 samples of bidi/hand-rolled cigarettes from different brands for cigarette ash discrimination, and 30 samples (10 sticks for a single cigarette brand) for brand discrimination. After smouldering the collected sample sticks in the laboratory, ash spectra were obtained for all of them for cigarette ash discrimination and brand determination. Differences in peak patterns, their absorbance, and the presence or absence of specific components in the fingerprint region were investigated for discriminatory purposes. Because FTIR spectra evaluation is time-consuming and prone to human error, the data was further analysed using multivariate techniques such as PCA (principal component analysis) and PLS-DA (Partial Least Square Discriminant Analysis) to improve sample discrimination. In the PCA plots, there was a degree of sample segregation. Therefore, for classifying the data into groups and predicting their outcomes, the PLS-DA method was employed, and a satisfactory categorization of samples was accomplished. The performance of the PLS-DA model is further validated using blind testing of unknown samples. As a result of the current research work, a non-destructive, efficient, and non-invasive approach for analysing cigarette ash has been established, which would aid in crime scene investigation.