Identification and Comparison of Electrical Tapes Using Instrumental and Statistical Techniques: I. Microscopic Surface Texture and Elemental Composition^*

Abstract

Comparisons of polyvinyl chloride electrical tape typically rely upon evaluating class characteristics such as physical dimensions, surface texture, and chemical composition. Given the various techniques that are available for this purpose, a comprehensive study has been undertaken to establish an optimal analytical scheme for electrical tape comparisons. Of equal importance is the development of a quantitative means for sample discrimination. In this study, 67 rolls of black electrical tape representing 34 different nominal brands were analyzed via scanning electron microscopy and energy dispersive spectroscopy. Differences in surface roughness, calendering marks, and filler particle size were readily apparent, including between some rolls of the same nominal brand. The relative amounts of magnesium, aluminum, silicon, sulfur, lead, chlorine, antimony, calcium, titanium, and zinc varied greatly between brands and, in some cases, could be linked to the year of manufacture. For the first time, quantitative differentiation of electrical tapes was achieved through multivariate statistical techniques, with 36 classes identified within the sample population. A single-blind study was also completed where questioned tape samples were correctly associated with known exemplars. Finally, two case studies are presented where tape recovered from an improvised explosive device is compared with tape recovered from a suspect.

Forensic Discrimination of Dyed Hair Color: I. UV‐Visible Microspectrophotometry*†

“…The summation of the squares of all peak areas was calculated, and the square root of this sum was then applied. The normalized value was derived as the quotient of each peak area divided by the square root of the sum of squares (19). Finally, the average of all 25 scans was calculated to represent each dye sample, while the average of five scans was also tabulated to represent each hair individually.…”

Analysis and Discrimination of Electrical Tapes: Part II. Backings*,†

“…Although a range of widths are available, approximately 19 mm is common. Similarly, thicknesses can vary and has been cited as the only difference between different products from the same manufacturer (2). …”

Forensic Discrimination of Dyed Hair Color: II. Multivariate Statistical Analysis*,†

“…The statistically derived mean of each class is called the centroid, while the input data closest to the mean is referred to as the central object. AHC utilized Euclidean distances; linking and clustering was achieved using Ward’s aggregation criterion (5). The number of classes established in AHC was based exclusively on the dye or substrate average of 25 spectra (five hairs per dye or substrate, five scans per hair) and strict interpretation of the automatic truncation in the dendrogram.…”

“…Factor scores are tabulated that depict the locations of each observation in the data set with respect to each of the principal component axes. Factor loadings plots illustrate the contributions of the original variables to the variance described by each principal component (5). The weights associated with each original variable are termed “loadings,” while the principal components are sometimes referred to as “factors.” Exploration of this plot aids in the discerning chemical trends potentially associated with each component axis (1,2).…”

“…In this work, UV–visible spectra of dyed hair samples will be analyzed using chemometric methods to assess the discriminating capabilities of the method (4). The statistical analysis scheme chosen for this research, consistent with existing literature, includes agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA) (5). …”