ABSTRACT
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%.
ABSTRACT
Paperboard is widely used in different applications, such as packaging and graphic printing, among others. Consumption of recycled paper is growing, which has led the paper-mill packaging industry to apply strict quality controls. This means that it is very important to develop methods to test the quality of recycled products. In this article, we focus on determining the recovered-fiber content of paperboard samples by applying Fourier transform mid-infrared (FT-MIR) spectroscopy in combination with multivariate statistical methods. To this end, two very fast, nondestructive approaches were applied: classification and quantification. The first approach is based on classifying unknown paperboard samples into two groups: high and low recovered-fiber content. Conversely, under the quantification approach, the content of recovered fiber in the incoming paperboard samples is determined. The experimental results presented in this article show that the classification approach, which classifies unknown incoming paperboard samples, is highly accurate and that the quantification approach has a root mean square error of prediction of about 4.1.
