Presented at the 64th Annual Meeting of the American Academy of Forensic Sciences, February 20-25, 2012, in Atlanta, GA.
Article first published online: 6 FEB 2014
© 2014 American Academy of Forensic Sciences
Journal of Forensic Sciences
Volume 59, Issue 3, pages 627–636, May 2014
How to Cite
Bonetti, J. and Quarino, L. (2014), Comparative Forensic Soil Analysis of New Jersey State Parks Using a Combination of Simple Techniques with Multivariate Statistics,. Journal of Forensic Sciences, 59: 627–636. doi: 10.1111/1556-4029.12375
Supported by the National Institute of Justice Office of Justice Programs, U.S. Department of Justice/Forensic Science Foundation under Award No. 2008-DN-BX-K216.
- Issue published online: 21 APR 2014
- Article first published online: 6 FEB 2014
- Manuscript Accepted: 16 FEB 2013
- Manuscript Revised: 22 DEC 2012
- Manuscript Received: 11 JUL 2012
- forensic science;
- soil analysis;
- multivariate statistics;
- principal component analysis;
- canonical discriminant analysis;
- particle-size distribution;
- pH ;
- loss on ignition
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.