PRINCIPAL COMPONENT ANALYSIS OF DIFFUSE NEAR-INFRARED REFLECTANCE DATA FROM PAPER CURRENCY

One of the concepts that the authors are investigating for the detection of counterfeit objects involves the use of pattern recognition techniques to analyze multivariant data acquired from properties intrinsic to the object. The sample population consisted of authentic currency, circulated and uncirculated, and cotton and rag paper stock as stand-ins for counterfeit currency. Although the reflectance spectra for all of the samples were very 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.