ABSTRACT
In the forensic examination of automotive paint, each layer of paint is analyzed individually by infrared (IR) spectroscopy. Laboratories in North America typically hand section each layer and present each separated layer to the spectrometer for analysis, which is time consuming. In addition, sampling too close to the boundary between adjacent layers can pose a problem as it produces an IR spectrum that is a mixture of the two layers. Not having a "pure" spectrum of each layer will prevent a meaningful comparison between each paint layer or in the situation of searching an automotive database will prevent the forensic paint examiner from developing an accurate hit list of potential suspects. These two problems can be addressed by collecting concatenated IR data from all paint layers in a single analysis by scanning across the cross sectioned layers of the paint sample using a FTIR imaging microscope. Decatenation of the IR data is achieved by multivariate curve resolution using a Varimax extended rotation to select the starting point (i.e., initial estimates of the reconstructed IR spectra of each layer) for the alternating least squares algorithm to obtain a pure IR spectrum of each automotive paint layer. Comparing the reconstructed IR spectrum of each layer against the IR spectral library of the PDQ database demonstrated that it is possible to identify the correct model of the vehicle from these reconstructed spectra. This imaging approach to IR analysis of automotive paint, not only saves time and eliminates the need to analyze each layer separately, but also ensures that the final spectrum of each layer is "pure" and not a mixture.
ABSTRACT
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.
ABSTRACT
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%.
ABSTRACT
PURPOSE: The purpose of this study was to examine the relationship between sleep patterns and adiposity in young adult women. DESIGN: Cross-sectional. SETTING: The study took place at two Mountain West region universities and surrounding communities. SUBJECTS: Subjects were 330 young adult women (20.2 ± 1.5 years). MEASURES: Sleep and physical activity were monitored for 7 consecutive days and nights using actigraphy. Height and weight were measured directly. Adiposity was assessed using the BOD POD. ANALYSIS: Regression analysis, between subjects analysis of variance, and structural equation modeling were used. RESULTS: Bivariate regression analysis demonstrated that sleep efficiency was negatively related to adiposity and that the 7-day standard deviations of bedtime, wake time, and sleep duration were positively related to adiposity (p < .05). Controlling for objectively measured physical activity strengthened the relationship between sleep duration and adiposity by 84% but had a statistically negligible impact on all other relationships that were analyzed. However, multivariate structural equation modeling indicated that a model including sleep efficiency, sleep pattern inconsistency (latent variable consisting of the 7-day standard deviations of bedtime, wake time, and sleep duration), and physical activity was the best for predicting percent body fat. CONCLUSION: Inconsistent sleep patterns and poor sleep efficiency are related to adiposity. Consistent sleep patterns that include sufficient sleep may be important in modifying risk of excess body fat in young adult women.
