Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

G Bonifazi; G Capobianco; S Serranti; O Trotta; S Bellagamba; S Malinconico; F Paglietti

doi:10.1016/j.saa.2023.123672

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Feb 15:307:123672. doi: 10.1016/j.saa.2023.123672. Epub 2023 Nov 20.

Authors

G Bonifazi¹, G Capobianco², S Serranti¹, O Trotta¹, S Bellagamba³, S Malinconico³, F Paglietti³

Affiliations

¹ Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy.
² Department of Chemical Engineering Materials & Environment, Sapienza University of Rome, Rome, Italy. Electronic address: giuseppe.capobianco@uniroma1.it.
³ Italian Workers Compensation Authority (INAIL), Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements, Rome, Italy.

PMID: 37995651
DOI: 10.1016/j.saa.2023.123672

Abstract

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.

Keywords: Asbestos; Construction and demolition waste; ECOC-SVM; Hi-PLSDA; Hyperspectral imaging; Micro-XRF; PCA-kNN.