Skip to main content
padlock icon - secure page this page is secure

Statistical Discrimination of Liquid Gasoline Samples from Casework

Buy Article:

$52.00 + tax (Refund Policy)

Abstract: 

The intention of this study was to differentiate liquid gasoline samples from casework by utilizing multivariate pattern recognition procedures on data from gas chromatography-mass spectrometry. A supervised learning approach was undertaken to achieve this goal employing the methods of principal component analysis (PCA), canonical variate analysis (CVA), orthogonal canonical variate analysis (OCVA), and linear discriminant analysis. The study revealed that the variability in the sample population was sufficient enough to distinguish all the samples from one another knowing their groups a priori. CVA was able to differentiate all samples in the population using only three dimensions, while OCVA required four dimensions. PCA required 10 dimensions of data in order to predict the correct groupings. These results were all cross-validated using the “jackknife” method to confirm the classification functions and compute estimates of error rates. The results of this initial study have helped to develop procedures for the application of multivariate analysis to fire debris casework.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: chromatography; classification; forensic science; gasoline; multivariate; pattern recognition

Document Type: Research Article

Affiliations: 1: New York City Police Department Crime Laboratory, Fire Debris Analysis Unit, 150-14 Jamaica Avenue, Jamaica, NY 11432. 2: New York City Police Department Crime Laboratory, 150-14 Jamaica Avenue, Jamaica, NY 11432.

Publication date: September 1, 2008

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more