Recovered Paperboard Samples Identification by Means of Mid-Infrared Sensors

Abstract

Paperboard is widely and increasingly applied as a packaging material, and in many applications 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 is applied to acquire the mid-infrared spectra of the paperboard samples. Next, statistical multivariate feature extraction and classification methods are 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.