The increasing use of secondary fiber in papermaking has led to the production of paper containing a wide range of contaminants. Wastepaper mills need to develop quality control methods for evaluating the incoming wastepaper stock as well as testing the specifications of the final product. The goal of this work is to present a fast and successful methodology for identifying different paper types. In this way, undesirable paper types can be refused, thus improving the runnability of the paper machine and the quality of the paper manufactured. In this work we examine various types of paper using information obtained by an appropriate chemometric treatment of infrared spectral data. For this purpose, we studied a large number of paper sheets of three different types (namely coated, offset and cast-coated) supplied by several paper manufacturers. We recorded Fourier transform infrared (FTIR) spectra with the aid of an attenuated total reflectance (ATR) module and near-infrared (NIR) reflectance spectra by means of fiber optics. Both techniques proved expeditious and required no sample pretreatment. The primary objective of this work was to develop a methodology for the accurate identification of samples of different paper types. For this purpose, we used the chemometric discrimination technique extended canonical variate analysis (ECVA) in combination with the k nearest neighbor (kNN) method to classify samples in the prediction set. Use of the NIR and FTIR techniques under these conditions allowed paper types to be identified with 100% success in prediction samples.

Peer Reviewed