Discrimination of color copier/laser printer toners by Raman spectroscopy and subsequent chemometric analysis

Abstract

Toner analysis has become an area of increased interest due to the wide availability of laser printers and photocopiers. Toner is most often encountered on paper in questioned document analysis. Because of this, it is important to develop methods that limit the interference of paper without damaging or destroying the document. Previous research using Fourier transform infrared spectroscopy (FTIR) has differentiated toners based on their polymer resin components. However, Raman spectroscopy and chemometric analysis are not typically used for the examination of this material. Raman spectroscopy is a popular tool for the chemical analysis of pigmented samples and was used to characterize cyan, yellow, and magenta toners. Analyses were performed using a dispersive micro-Raman spectrometer equipped with a 785nm diode laser, a CCD detector, and an objective at 20X magnification. One hundred samples of each color toner were collected. Three different and separate methods were developed for cyan, yellow, and magenta toners on paper to optimize results. Further analysis of the magenta toners was excluded due to a weak signal and significant paper interference. The data collected from the analyses of the blue and yellow toners was then processed using a combination of statistical procedures, including principal component analysis (PCA), agglomerative hierarchal clustering (AHC), and discriminative analysis (DA). Ninety-six blue toners were analyzed by PCA and three classes of spectra were suggested. Discriminant analysis showed that the three classes were well-differentiated with a cross-validation accuracy of 100% for the training set and 100% cross-validation accuracy for the external validation set. Eighty-eight yellow toners were analyzed by AHC and four classes of spectra were suggested. Discriminant analysis showed good differentiation between the classes with a cross-validation accuracy of 95.45% for the training set, but showed poor differentiation for the external validation set with a cross-validation accuracy of 72%. While these toners were able to be discriminated, no correlation could be made between the manufacturer, printer make and model, and the toner sample.