Comparative Forensic Soil Analysis of New Jersey State Parks Using a Combination of Simple Techniques with Multivariate Statistics^,

Abstract

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications.

X‐ray Diffraction and Rietveld Refinement in Deferrified Clays for Forensic Science

“…KEYWORDS: forensic science, rietveld method, multivariate analysis, dithionite-citrate-bicarbonate, kaolinite, gibbsite, crystallite size Soil vestiges might produce important information in criminal investigations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), and these vestiges might be transferred and remain impregnated in the victim's or criminal's accessories such as tools, shoe soles, wheels and tires, clothes (10). Using suitable techniques, these vestiges might provide accurate information about the crime scene, through comparison with previously mapped locations and analyzed soil samples (2).…”

Predictive Soil Provenancing (PSP): An Innovative Forensic Soil Provenance Analysis Tool

“…Once a database is selected, a number of statistical and visualization analysis tools are typically implemented, including univariate, bivariate and multivariate statistical analysis, exploratory data analysis, analysis of variance, compositional data analysis, spatial interpolation and smoothing, cluster analysis, supervised or unsupervised classification, and data mining (e.g., [19][20][21][22][23][24][25][26][27][28][29]). The next step in the ESP workflow is the comparison of the evidentiary soil sample's composition with the selected database.…”

Chemometrics

“…Bonetti and Quarino focused on multivariate statistical analysis of soil samples collected from various state parks in New Jersey, in both summer and fall (Bonetti and Quarino, 2014). Each soil sample was characterized based on particle size distribution, pH, and organic content and the data were analyzed using PCA and discriminant analysis.…”