Andrew Weakley

Evidential significance of automotive paint trace evidence using a pattern recognition based infrared library search engine for the Paint Data Query Forensic Database

A prototype library search engine has been further developed to search the infrared spectral libraries of the paint data query database to identify the line and model of a vehicle from the clear coat, surfacer-primer, and e-coat layers of an intact paint chip. For this study, search prefilters were developed from 1181 automotive paint systems spanning 3 manufacturers: General Motors, Chrysler, and Ford. The best match between each unknown and the spectra in the hit list generated by the search prefilters was identified using a cross-correlation library search algorithm that performed both a forward and backward search. In the forward search, spectra were divided into intervals and further subdivided into windows (which corresponds to the time lag for the comparison) within those intervals. The top five hits identified in each search window were compiled; a histogram was computed that summarized the frequency of occurrence for each library sample, with the IR spectra most similar to the unknown flagged. The backward search computed the frequency and occurrence of each line and model without regard to the identity of the individual spectra. Only those lines and models with a frequency of occurrence greater than or equal to 20% were included in the final hit list. If there was agreement between the forward and backward search results, the specific line and model common to both hit lists was always the correct assignment. Samples assigned to the same line and model by both searches are always well represented in the library and correlate well on an individual basis to specific library samples. For these samples, one can have confidence in the accuracy of the match. This was not the case for the results obtained using commercial library search algorithms, as the hit quality index scores for the top twenty hits were always greater than 99%.

Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database to Enhance Lead Information from Automotive Paint Trace Evidence

Multilayered automotive paint fragments, which are one of the most complex materials encountered in the forensic science laboratory, provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, forensic automotive paint examiners have turned to the paint data query (PDQ) database, which allows the forensic examiner to compare the layer sequence and color, texture, and composition of the sample to paint systems of the original equipment manufacturer (OEM). However, modern automotive paints have a thin color coat and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database in an effort to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach employing pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts.

On the Widths of Bands in the Infrared Spectra of Oxyanions

It is well known that the antisymmetric stretching (ν3) band in the mid-infrared spectra of oxyanion salts is usually very broad, whereas all the other fundamental bands are narrow. In this paper, we propose that the underlying cause of the increased width is the effect of the very high absorption index of this band for samples prepared with a range of particle sizes. When oxyanion salts are ground, the diameter of the resulting particles usually varies from less than 100 nm to about 2 µm. While the peak absorbance of the ν3 band of the smaller particles (diameter < 200 nm) is less than 1, that of the larger particles can be as high as 6. We show that the average transmittance of these particles leads to a significant band broadening, especially when there are small voids in the resulting sample. Although the effect is always seen in the spectra of alkali halide disks and mineral oil mulls, it is also seen in diffuse reflection and attenuated total reflection (ATR) spectra. Because the depth of penetration of infrared radiation below 1500 cm−1 is less than 1 µm for ATR spectra measured with a germanium internal reflection element (IRE), the width of the ν3 band is lower than that of ATR spectra measured with an IRE of lower refractive index such as diamond on zinc selenide.

Ambient aerosol composition by infrared spectroscopy and partial least squares in the chemical speciation network: Multilevel modeling for elemental carbon

ABSTRACT Fourier transform infrared spectroscopy (FT-IR) has been used to predict elemental carbon (EC) on polytetrafluoroethylene (PTFE) filter samples from the United States Environmental Protection Agencys Chemical Speciation Network (CSN). This study provides a proof-of-principle demonstration of using multilevel modeling to determine thermal/optical reflectance (TOR) equivalent EC (a.k.a., FT-IR EC) on PTFE samples collected in the CSN. Initially, spectra from nine geographically disperse sites were pooled and calibrated directly to collocated TOR EC measurements. The FT-IR EC quantified in test samples was deemed substandard when judged against an earlier study, e.g., R2 = 0.760 and median absolute deviation (MAD) = 26.7%. Upon scrutinizing each samples absolute prediction error and squared Mahalanobis distance, Elizabeth, NJ predictions were found to exhibit atypical systematic errors, motivating the development of a multilevel classification and calibration procedure. Atypical Elizabeth spectra were distinguished from the (typical) CSN spectra by training a partial least-square discriminant analysis. Predicting EC using calibrations dedicated to either atypical or typical samples produced a satisfactory improvement in overall performance (R2 = 0.886, MAD = 19.8%). Analysis of the atypical FT-IR spectra and select TOR thermal fractions suggested that Elizabeth samples contained elevated levels of diesel particulate matter as evidenced by the use of organic nitrogen functional groups for prediction, very low average OC/EC, and minimal charring during TOR speciation. FT-IR EC from the other eight sites was predominately determined by aliphatic C-H, C = C aromatic, and functional groups associated with oxidation. This study provides preliminary confirmation that FT-IR EC may be accurately determined from source-oriented calibrations under a combined classification and calibration methodology. Copyright

EXPRESS: Long Term Strategy for Assessing Carbonaceous Particulate Matter Concentrations from Multiple Fourier Transform Infrared (FT-IR) Instruments: Influence of Spectral Dissimilarities on Multivariate Calibration Performance

Matching the spectral response between multiple spectrometers is a mandatory procedure when developing robust calibrations whose prediction is independent of instrument-related signal variations. A viable alternative to complex calibration transfer methods consists of matching the instrument spectral response by controlling a set of key instrumental and environmental parameters. This paper discusses the applicability of such an approach to three Fourier transform infrared (FT-IR) spectrometers used for the routine assessment of carbonaceous particulate matter concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) speciation network. The effectiveness of the proposed matching procedure is evaluated by comparing the spectral response for each individual instrument in order to characterize the extent, and nature, of the remaining inter-instrument spectral dissimilarities. Instrument-related contributions to the signal were determined to be small compared with the spectral variability induced by the filter type used for sample collection. The impact of spectral differences on prediction was addressed through the comparison of model performance derived from multiple calibration scenarios. A hybrid model yielding accurate and homogeneous prediction regardless of the instrument was proposed for organic carbon (OC) and elemental carbon (EC), two major constituents of atmospheric particulate matter. Coefficients of determination of 0.98 (OC) and 0.90 (EC) with median biases not exceeding 0.20 µg (OC) and 0.07 µg (EC) are reported. The long-term stability, assessed from weekly measurements of reference samples, shows a deviation in predicted concentrations of less than ±5% over a 2.5-year period for most of the data collected. Extending OC and EC hybrid models to the prediction of ambient samples collected during the two subsequent years provides satisfactory performance. The proposed instrument matching procedure coupled with the relative simplicity of the hybrid model is an alternative to computationally advanced calibration transfer methodologies for the characterization of carbonaceous particulate matter using multiple FT-IR instruments.

Thermal/optical reflectance equivalent organic and elemental carbon determined from federal reference and equivalent method fine particulate matter samples using Fourier transform infrared spectrometry

Abstract A fine particulate matter (PM2.5) monitoring network of filter-based federal reference methods and federal equivalent methods (FRM/FEMs) is used to assess local ambient air quality by comparison to National Ambient Air Quality Standards (NAAQS) at about 750 sites across the continental United States. Currently, FRM samplers utilize polytetrafluoroethylene (PTFE) filters to gravimetrically determine PM2.5 mass concentrations. At most of these sites, sample composition is unavailable. In this study, we present the proof-of-principle estimation of the carbonaceous fraction of fine aerosols on FRM filters using a nondestructive Fourier transform infrared (FT-IR) method. Previously, a quantitative FT-IR method accurately determined thermal/optical reflectance equivalent organic and elemental carbon (a.k.a., FT-IR organic carbon [OC] and elemental carbon [EC]) on filters collected from the chemical speciation network (CSN). Given the similar configuration of FRM and CSN aerosol samplers, OC and EC were directly determined on FRM filters on a mass-per-filter-area basis using CSN calibrations developed from nine sites during 2013 that have collocated CSN and FRM samplers. FRM OC and EC predictions were found to be comparable to those of the CSN on most figures of merit (e.g., R2) when the type of PTFE filter used for aerosol collection was the same in both networks. Although prediction accuracy remained unaffected, FT-IR OC and EC determined on filters produced by a different manufacturer show marginally increased prediction errors suggesting that PTFE filter type influences extending CSN calibrations to FRM samples. Overall, these findings suggest that quantifying FT-IR OC and EC on FRM samples appears feasible.

Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression

In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administrations P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in coal mine dusts, using both OLS and PLS analyses, when kaolinite was present.