Geneviève Massonnet

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm(-1) and 2730-3600 cm(-1), provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

A market study of green spray paints by Fourier transform infrared (FTIR) and Raman spectroscopy.

A market study of 40 different green spray paints was carried out using infrared (FTIR) and Raman spectroscopy. The infrared technique distinguished between the 12 main groups based on their binder and extender composition. After visual comparison of the spectra 22 subgroups were observed. Raman spectroscopy was also carried out on the 40 reference paints in order to determine the pigment content. Analyses were undertaken using two different excitation sources: Argon ion (514.5 nm) and Helium-Neon (632.8 nm). The first generated strong fluorescence for most of the samples and created eight groups. Using the red laser, 15 classes were observed. Finally, using an analytical sequence starting with infrared spectroscopy followed by Raman Helium-Neon and then by Raman Argon laser, most of the paints were differentiated. In this study infrared and Raman spectroscopy complemented each other. FTIR supplied information about the binder and some extenders, and Raman provided information on the main organic pigments present.

Evaluation of Raman Spectroscopy for the Analysis of Colored Fibers: A Collaborative Study

A collaborative study on Raman spectroscopy was carried out by members of the ENFSI (European Network of Forensic Science Institutes) European Fibres Group (EFG) on three dyed fibers: two red acrylics and one red wool. Raman instruments from six different manufacturers were tested as well as nine different laser wavelengths ranging from blue (lambda = 458 nm) to near infrared-NIR (lambda = 1064 nm). This represents the largest comparison study of Raman analytical parameters carried out on identical fiber samples. For the chosen fiber and dye samples, red lasers (lambda = 633 and 685 nm) gave the poorest spectral quality whereas blue (458 nm), green (514 nm) and near infrared lasers (785, 830 and 1064 nm) provided average results. Blue (488 nm) and green lasers (532 nm) globally gave the best quality spectra. Fluorescence problems were often encountered with some of the excitation wavelengths and therefore a flexible Raman instrument equipped with different lasers can be recommended to measure forensic fiber samples. The instrument should also be equipped with a Raman microscope in order to be able to focus on a single fiber. This study shows that Raman spectroscopy usually enables the identification of the main dye present in a colored fiber; however, minor dye components are much more difficult to detect. SERRS (Surface Enhanced Resonance Raman Scattering) techniques give an improvement of the dyes spectral intensity but no spectral improvement was observed for the two red acrylic and red wool fibers tested.

Applications of a transportable Raman spectrometer for the in situ detection of controlled substances at border controls

A transportable Raman spectrometer was tested for the detection of illicit drugs seized during border controls. In a first step, the analysis methodology was optimized using reference substances such as diacetylmorphine (heroin), cocaine and amphetamine (as powder or liquid forms). Adequate focalisation distance and times of analysis, influence of daylight and artificial light sources, repeatability and limits of detection were studied. In a second step, the applications and limitations of the technique to detect the illicit substances in different mixtures and containers were evaluated. Transportable Raman spectroscopy was found to be adequate for a rapid screen of liquids and powders for the detection and identification of controlled substances. Additionally, it had the advantage over other portable techniques, such as ion mobility spectrometry, of being non-destructive and capable of rapid analysis of large quantities of substances through containers such as plastic bags and glass bottles.

The discrimination of colored acrylic, cotton, and wool textile fibers using micro-Raman spectroscopy. Part 1: in situ detection and characterization of dyes.

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633‐nm laser did not provide Raman information. The 514‐nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830‐nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.

Influence of the shaking time on the forensic analysis of FTIR and Raman spectra of spray paints

In order to decide if replicated measurements of a trace fall within the intra-variability expected for reference paint samples, a forensic scientist has to understand and integrate all reasonable sources of variation. The origins of such variation in spectra can be various, but mainly include differences in components distribution (homogeneity of spraying) or differences originating from the manufacturing process (production batches). Instrumental variation can also be problematic for non-successive measurements. Infrared and Raman spectra were collected to study the homogeneity of the paint distribution after shaking a spray can for times of 0, 1, 2, 3, 4 and 5min. The results confirm that differences arise in both the spectroscopic techniques used in this study. Mainly, this survey shows that the problematic of shaking is particularly important when the pigment content can be detected from spray paint samples within the infrared domain. In these situations, the signal from the pigment might produce strong absorptions that vary with shaking time, leading to differences in relative intensities with respect to those attributed to the binder. For Raman spectroscopy, it has been shown that a gradient of pigment concentration is observable in some samples depending on the shaking time. The proportion of the signal due to the pigment increases with shaking times from 0 to 1min and diminishes afterwards, to finally reach stabilization around 3min of shaking. Not all samples are affected by these differences and it should always be evaluated on a case-by-case basis. From a statistical point-of-view, principal component analyses of the replicates show that the spectra are reproducible after 3min of shaking.

Discrimination and classification of FTIR spectra of red, blue and green spray paints using a multivariate statistical approach

The use of multivariate techniques was investigated in a forensic paint analysis context. The data set consisted of the infrared spectra of 74 spray paint cans, corresponding to three colors code, respectively red, green and blue. Two aspects of the forensic procedure are studied, respectively, the discrimination of paints coming from a market study through exploratory techniques, and the source prediction of unknown samples in database using classifiers. The exploratory discrimination capabilities of principal component analysis (PCA) and hierarchical clusters analysis (HCA) were compared to a visual comparison of the spectra. Iterative PCA was found to be the most adapted solution for exploratory analysis of the samples. Very few differences were found compared to a visual comparison of the samples and the statistical foundations behind the method ensure that no errors are due to a misclassification of the samples. Market studies and joint PCA also represent a significant gain of time. Following that, classification and prediction of future samples were evaluated by means of supervised techniques of classification such as linear/quadratic discriminant analysis (LDA/QDA), support vector machines (SVM), soft independent modeling of classes analogies (SIMCA) and partial least squares discriminant analysis (PLS-DA). SIMCA was the preferred method, as it provided the smallest false negative rates together with a correct classification rate of about 95%. From an investigative point-of-view the presence of false positives was considered acceptable, as it is preferable to have a longer list of possible sources but have confidence that the true source belongs to it.

The Analysis of Colored Acrylic, Cotton, and Wool Textile Fibers Using Micro‐Raman Spectroscopy. Part 2: Comparison with the Traditional Methods of Fiber Examination

In the second part of this survey, the ability of micro‐Raman spectroscopy to discriminate 180 fiber samples of blue, black, and red cottons, wools, and acrylics was compared to that gathered with the traditional methods for the examination of textile fibers in a forensic context (including light microscopy methods, UV‐vis microspectrophotometry and thin‐layer chromatography). This study shows that the Raman technique plays a complementary and useful role to obtain further discriminations after the application of light microscopy methods and UV‐vis microspectrophotometry and assure the nondestructive nature of the analytical sequence. These additional discriminations were observed despite the lower discriminating powers of Raman data considered individually, compared to those of light microscopy and UV‐vis MSP. This study also confirms that an instrument equipped with several laser lines is necessary for an efficient use as applied to the examination of textile fibers in a forensic setting.

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 Wards 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.

Survey on batch-to-batch variation in spray paints: A collaborative study

This study represents the most extensive analysis of batch-to-batch variations in spray paint samples to date. The survey was performed as a collaborative project of the ENFSI (European Network of Forensic Science Institutes) Paint and Glass Working Group (EPG) and involved 11 laboratories. Several studies have already shown that paint samples of similar color but from different manufacturers can usually be differentiated using an appropriate analytical sequence. The discrimination of paints from the same manufacturer and color (batch-to-batch variations) is of great interest and these data are seldom found in the literature. This survey concerns the analysis of batches from different color groups (white, papaya (special shade of orange), red and black) with a wide range of analytical techniques and leads to the following conclusions. Colored batch samples are more likely to be differentiated since their pigment composition is more complex (pigment mixtures, added pigments) and therefore subject to variations. These variations may occur during the paint production but may also occur when checking the paint shade in quality control processes. For these samples, techniques aimed at color/pigment(s) characterization (optical microscopy, microspectrophotometry (MSP), Raman spectroscopy) provide better discrimination than techniques aimed at the organic (binder) or inorganic composition (fourier transform infrared spectroscopy (FTIR) or elemental analysis (SEM - scanning electron microscopy and XRF - X-ray fluorescence)). White samples contain mainly titanium dioxide as a pigment and the main differentiation is based on the binder composition (CH stretches) detected either by FTIR or Raman. The inorganic composition (elemental analysis) also provides some discrimination. Black samples contain mainly carbon black as a pigment and are problematic with most of the spectroscopic techniques. In this case, pyrolysis-GC/MS represents the best technique to detect differences. Globally, Py-GC/MS may show a high potential of discrimination on all samples but the results are highly dependent on the specific instrumental conditions used. Finally, the discrimination of samples when data was interpreted visually as compared to statistically using principal component analysis (PCA) yielded very similar results. PCA increases sensitivity and could perform better on specific samples, but one first has to ensure that all non-informative variation (baseline deviation) is eliminated by applying correct pre-treatments. Statistical treatments can be used on a large data set and, when combined with an experts opinion, will provide more objective criteria for decision making.