In food packaging, low-density polyethylene (PE) coating is applied to paperboards to act as a functional barrier and to provide the smoothness required to enhance printability. These characteristics are related to the material’s surface roughness, the parameter monitored during the manufacturing process. Measurement of surface roughness using optical profilometry has gained importance in the paper industry. The optical instruments used to measure surface roughness are limited spatially by the relationship with the light wavelength at which they operate. A scanning electron microscope (SEM) is an alternative for overcoming the spatial resolution limitation, and the use of stereo-photogrammetry on SEM images can be seen as an alternative profilometry technique to measure surface roughness. In this investigation, the surface topography of industrially manufactured high-quality PE-coated paperboard was studied, comparing the SEM stereo-photogrammetry technique with a reference profilometry method, i. e., chromatic confocal microscopy (CCM). We found close agreement between the calculated surface roughness and the results of the techniques used and compared them according to the new ISO 25178 Geometric Product Specifications. We concluded that SEM stereo-photogrammetry provides comparable accurate alternative profilometry method for characterizing the surface roughness of PE-coated paperboard in the micrometer scale.

The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.