Automotive paint analysis: How far has science advanced in the last ten years?

Analysis of automotive paint layers on plastic substrates using chemical imaging μ-FTIR and O-PTIR microspectroscopy

Automobile paint chips are a crucial piece of trace evidence for forensic investigators. This is because automotive paints are composed of multiple layers, including the primer, basecoat, and clearcoat, each of which has its own chemical composition that can vary by vehicle make, model, year, and manufacturing plant. Thus, Fourier-transform infrared (FTIR) spectral databases for automobile paint systems have been established to aid law enforcement in, for example, narrowing search parameters for a suspect's vehicle. Recently, car manufacturers have implemented primers on plastic substrates that are much thinner (~5 μm) than those on metal substrates, making it more difficult to manually separate for analyses. Here, we evaluated FTIR microspectroscopy (μ-FTIR) and optical photothermal infrared spectroscopy (O-PTIR) to chemically image cross sections of paint chips without manually separating the layers. For μ-FTIR, transmission and transflection modes provided the highest quality spectra compared to reflection and μ-ATR analyses. Point analysis was preferable to chemical imaging, as peaks were identified in the point (MCT) detector's lower spectral range that was below the imaging (FPA) detector's cutoff, such as those associated with titanium dioxide. Reduced spectral range can lead to a similar issue in O-PTIR analyses depending on instrument configuration. However, its complementary Raman spectra showed strong titanium dioxide peaks, providing an alternate means of identification. Both techniques are likely to become more relevant as they are non-destructive and avoid manual separation of the layers. O-PTIR is particularly well-suited for analysis of the thin primer layer due to its superior spatial resolution.

A Novel LIBS Sensor for Sample Examinations on a Crime Scene

In this work, we present a compact LIBS sensor developed for characterization of samples on a crime scene following requirements of law enforcement agencies involved in the project. The sensor operates both in a tabletop mode, for aside measurements of swabbed materials or taken fragments, and in handheld mode where the sensor head is pointed directly on targets at the scene. The sensor head is connected via an umbilical to an instrument box that could be battery-powered and contains also a color camera for sample visualization, illumination LEDs, and pointing system for placing the target in focus. Here we describe the sensor's architecture and functionalities, the optimization of the acquisition parameters, and the results of some LIBS measurements. On nano-plotted traces at silica wafer and in optimized conditions, for most of the elements the detection limits, in term of the absolute element masses, were found to be below 10 picograms. We also show results obtained on some representative materials, like fingerprints, swabbed soil and gunshot residue, varnishes on metal, and coated plastics. The last, solid samples were used to evaluate the depth profiling capabilities of the instrument, where the recognition of all four car paint layers was achieved.

A novel approach for forensic identification of automotive paints using optical coherence tomography and multivariate statistical methods

Automotive paint is one of the most important evidence in solving vehicle-related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral-domain optical coherence tomography system (SD-OCT). Two-dimensional cross-sectional OCT images and three-dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A-scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross-sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A-scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi-layer perceptron (MLP), k-nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.

Surface Analysis Techniques in Forensic Science: Successes, Challenges, and Opportunities for Operational Deployment

Surface analysis techniques have rapidly evolved in the last decade. Some of these are already routinely used in forensics, such as for the detection of gunshot residue or for glass analysis. Some surface analysis approaches are attractive for their portability to the crime scene. Others can be very helpful in forensic laboratories owing to their high spatial resolution, analyte coverage, speed, and specificity. Despite this, many proposed applications of the techniques have not yet led to operational deployment. Here, we explore the application of these techniques to the most important traces commonly found in forensic casework. We highlight where there is potential to add value and outline the progress that is needed to achieve operational deployment. We consider within the scope of this review surface mass spectrometry, surface spectroscopy, and surface X-ray spectrometry. We show how these tools show great promise for the analysis of fingerprints, hair, drugs, explosives, and microtraces.

Cross-section measurements of multilayer automotive paint samples using combined Raman spectroscopy and LIBS

The analysis of multilayer automotive paints is a challenging task due to their inherent complexity. Combined LIBS and Raman spectroscopy allows a comprehensive chemical analysis of each individual layer in one step with minimal sample preparation.

Discrimination of white automotive paint samples using ATR-FTIR and PLS-DA for forensic purposes

The consequences of a hit-and-run car crash are significant and may include serious injuries to the victims, health system overload and even victim's death. The vehicle and driver identification are often challenging for local law enforcement. The aim of this study was to develop a methodology to discriminate between automotive paint samples according to the make of the vehicle and its color shade. 143 white samples (collected at traffic accident scenes) were analyzed in situ by Fourier transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR) and coupled microscopy. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed for data analysis. The samples were split into three groups: calibration set, validation set and external test set. The figures of merit were calculated to assess the quality of the model. Sensitivity, specificity, and efficiency rates were, respectively, 98,9%, 98.4% and 98.6%, for the calibration set. For the validation group, the classification accuracy was 100%. Correct classification rates for the internal validation set and external test set were 100% and 79.1% respectively. The technique is clean, fast, relatively low-cost, and non-destructive. Damaged regions of the samples were avoided by using the attached microscope. Limiting the age of the samples to a maximum of 10 years was enough to avoid misclassifications due to the natural degradation and weathering of the sample. Since the external test group is formed by underrepresented classes, its correct classification rate (79.1%) can be potentially improved at any time, by including and analyzing more samples.