Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

In polyethylene terephthalate's (PET)'s recycling processes, separation from polyvinyl chloride (PVC) is of prior relevance due to its toxicity, which degrades the final quality of recycled PET. Moreover, the potential presence of some polymers in mixed plastics (such as PVC in PET) is a key aspect for the use of recycled plastic in products such as medical equipment, toys, or food packaging. Many works have dealt with plastic classification by hyperspectral imaging, although only some of them have been directly focused on PET sorting and very few on its separation from PVC. These works use different classification models and preprocessing techniques and show their performance for the problem at hand. However, still, there is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments‐based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable‐based and/or artificial intelligence classification method, when using NIR hyperspectral images. There is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments‐based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable–based and/or artificial intelligence classification method when using near‐infrared hyperspectral images.