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Abstract-Paperboard is widely and increasingly applied as a packaging material and in many applications it is in direct contact with foodstuff. The increasing use of recovered paperboard has led to the production of paperboard containing several types of contaminants. In the case of using recovered paperboard some of these contaminants may migrate into the food in concentrations considered harmful to human health. To prevent this problem, a very fast and nondestructive method to identify recovered paperboard samples from those produced mainly from virgin fibers is developed in this paper. Therefore, recovered samples may be identified, so a special consideration may be given to these samples. To this end, Fourier transform mid-infrared spectroscopy was applied to acquire the midinfrared spectra of the paperboard samples. Next, statistical multivariate feature extraction and classification methods were applied to identify incoming samples produced from recovered fibers. Experimental results presented here prove that the proposed scheme allows obtaining high classification accuracy with a very fast response.

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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.

Ethylene propylene diene monomer (EPDM) rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovery processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FT-IR) spectroscopy and suitable multivariate statistical methods. An expedited and nondestructive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples, and microwave treated samples. First the FT-IR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis and canonical variate analysis to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k nearest neighbor algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FT-IR spectroscopy in this area, since the classification accuracy can be as high as 100%.