Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame retardants

TY - JOUR

T1 - Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame retardants

AU - Caballero, Daniel

AU - Bevilacqua, Marta

AU - Amigo, José Manuel

PY - 2019

Y1 - 2019

N2 - Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type and the same additive content can be recycled together. Three models based on different chemometrics techniques applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis, decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management industries.

AB - Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type and the same additive content can be recycled together. Three models based on different chemometrics techniques applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis, decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management industries.

KW - Decision tree

KW - Flame retardants

KW - Hierarchical classification

KW - NIR hyperspectral imaging

KW - Partial least square-discrimination analysis

KW - Plastics recycling

KW - Polymer

KW - Waste recycling

U2 - 10.1255/jsi.2019.a1

DO - 10.1255/jsi.2019.a1

M3 - Review

AN - SCOPUS:85070597134

SN - 2040-4565

VL - 8

JO - Journal of Spectral Imaging

JF - Journal of Spectral Imaging

M1 - a1

ER -