NCJ Number
186078
Journal
International Journal of Forensic Document Examiners Volume: 5 Dated: December/January 1999 Pages: 123-129
Date Published
1999
Length
7 pages
Annotation
Product tampering and product counterfeiting are increasing the need for methods to quickly determine product authenticity; this study determined whether the application of pattern recognition to diffuse near-infrared reflectance spectral data for paper currency and good-quality paper stock could be used to separate specimens into different and distinct subsets.
Abstract
The sample population consisted of authentic currency, circulated and uncirculated, and cotton and rag paper stock as stand-ins for counterfeit currency. Reflectance spectra were obtained from a spot that was essentially void of printing on both sides of the currency specimens. Although the reflectance spectra for all of the samples were similar, principal component analysis separated the samples into distinct classes without there being any prior knowledge of their chemical or physical properties. Class separation was achieved even for currency bills that differed only in their past environment. Leave-One-Out procedures resulted in 100 percent correct classification of each member of the sample set. A K-Nearest-Neighbor test or a linear discriminate can be used to classify unknown samples. 6 tables, 10 figures, and 17 references