The Analysis of; Tires and Tire Traces using FTIR and Py-GC/MS

Advancing automobile identification and brand discrimination from tyre rubber through Machine learning algorithms for forensic investigations

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.

Comparison of polymeric components and tire wear particle contents in particulate matter collected at bus stop and college campus

Particulate matter (PM_2.5) samples were collected at two different places of a college campus (CC) and a bus stop (BS) nearby the college campus. The traffic volume of college campus was very low due to untact classes. Polymeric components and tire wear particle (TWP) contents in the PM_2.5 samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Various polymeric components such as natural rubber (NR), bitumen, saturated hydrocarbons, poly(ethylene terephthalate) (PET), and plant-related particles (PRPs) were observed. NR and bitumen are key components of TWP of bus tire tread and asphalt pavement wear particle (APWP), respectively. The TWP contents in the PM_2.5 samples collected at the bus stop were larger than those collected at the college campus. For the same sampling site, the TWP content in the PM_2.5 sample collected for higher fine dust concentration in the air was greater than that for lower one. The TWP_2.5 concentration in the air for the BS sampling was higher than those for the CC sampling, even when the PM_2.5 concentration in the air for the former was lower than those for the latter. It can be concluded that the TWPs and APWPs in the PM_2.5 samples collected at the college campus should be transferred mostly from the outside road.

Tire Classification by Elemental Signatures Using Laser-Induced Breakdown Spectroscopy

Tire evidence is a form of trace evidence that is often overlooked in today's forensics, while frequently found at crime or accident scenes, usually in the form of skid marks. The pattern of the tire skid mark has been used before to link a tire or car to a scene, but the widespread use of anti-lock braking systems makes this an almost impossible and abandoned route of analysis. With this in mind, using the chemical profile of a tire has potential to link a car or tire back to a scene in which its trace material is found. This study shows the successful use of the elemental profile of tire rubber to classify 32 different samples using laser-induced breakdown spectroscopy, analyzed by principal component analysis combined with linear discriminant analysis. A classification accuracy close to 99% shows the ever-growing use of laser-induced breakdown spectroscopy as a technique of choice for forensic analysis of tire rubber, opening the path for its use as a forensic evidence.

Tire traces - discrimination and classification of pyrolysis-GC/MS profiles

Tire traces can be observed on several crime scenes as vehicles are often used by criminals. The tread abrasion on the road, while braking or skidding, leads to the production of small rubber particles which can be collected for comparison purposes. This research focused on the statistical comparison of Py-GC/MS profiles of tire traces and tire treads. The optimisation of the analytical method was carried out using experimental designs. The aim was to determine the best pyrolysis parameters regarding the repeatability of the results. Thus, the pyrolysis factor effect could also be calculated. The pyrolysis temperature was found to be five time more important than time. Finally, a pyrolysis at 650°C during 15s was selected. Ten tires of different manufacturers and models were used for this study. Several samples were collected on each tire, and several replicates were carried out to study the variability within each tire (intravariability). More than eighty compounds were integrated for each analysis and the variability study showed that more than 75% presented a relative standard deviation (RSD) below 5% for the ten tires, thus supporting a low intravariability. The variability between the ten tires (intervariability) presented higher values and the ten most variant compounds had a RSD value above 13%, supporting their high potential of discrimination between the tires tested. Principal Component Analysis (PCA) was able to fully discriminate the ten tires with the help of the first three principal components. The ten tires were finally used to perform braking tests on a racetrack with a vehicle equipped with an anti-lock braking system. The resulting tire traces were adequately collected using sheets of white gelatine. As for tires, the intravariability for the traces was found to be lower than the intervariability. Clustering methods were carried out and the Ward's method based on the squared Euclidean distance was able to correctly group all of the tire traces replicates in the same cluster than the replicates of their corresponding tire. Blind tests on traces were performed and were correctly assigned to their tire source. These results support the hypothesis that the tested tires, of different manufacturers and models, can be discriminated by a statistical comparison of their chemical profiles. The traces were found to be not differentiable from their source but differentiable from all the other tires present in the subset. The results are promising and will be extended on a larger sample set.

The Analysis of Tire Rubber Traces Collected After Braking Incidents Using Pyrolysis-Gas Chromatography/Mass Spectrometry

Automobile tire marks can routinely be found at the scenes of crime, particularly hit-and-run accidents and are left on road surfaces because of sudden braking or the wheels spinning. The tire marks are left due to the friction between the tire rubber and the solid road surface, and do not always demonstrate the tire tread pattern. However, the tire mark will contain traces of the tire. In this study, Pyrolysis Gas Chromatography/Mass Spectrometry was used to analyze 12 tires from different manufacturer's and their traces collected after braking incidents. Tire marks were left on a conglomerate road surface with sudden braking. The samples were pyrolysed without removal of contaminant in a micro-furnace type pyrolyser. Quantitative and qualitative analysis were performed on all the samples. All 12 samples were distinguished from each other. Each of the tire traces were identified as coming from there original source.