an official journal of: published by:
an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU


  • Giuseppe Bonifazi - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
  • Giuseppe Capobianco - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
  • Silvia Serranti - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
  • Sergio Malinconico - Department of new technologies for occupational safety of industrial plants, products and anthropic settlements, National Institute for Insurance against Accidents at Work, Italy
  • Federica Paglietti - Department of new technologies for occupational safety of industrial plants, products and anthropic settlements, National Institute for Insurance against Accidents at Work, Italy

Released under CC BY-NC-ND

Copyright: © 2021 CISA Publisher


Asbestos has been widely used in many applications for its technical properties (i.e. resistance to abrasion, heat and chemicals). Despite its properties, asbestos is recognized as a hazardous material to human health. In this paper a study, based on multivariate analysis, was carried out to verify the possibilities to utilize the hyperspectral imaging (HSI), working in the short-wave infrared range (SWIR: 1000-2500 nm), to detect the presence of asbestos-containing materials (ACM) in construction and demolition waste (CDW). Multivariate classification methods including classification and regression tree (CART), partial least squares-discriminant analysis (PLS-DA) and correcting output coding with support vector machines (ECOC-SVM), were adopted to perform the recognition/classification of ACM in respect of the other fibrous panels not containing asbestos, in order to verify and compare Efficiency and robustness of the classifiers. The correctness of classification results was confirmed by micro-X-ray fluorescence maps. The results demonstrate as SWIR technology, coupled with multivariate analysis modeling, is a quite promising approach to develop both “off-line” and “on-line” fast reliable and robust quality control strategies, finalized to perform a first evaluation of the presence of ACM.


Editorial History

  • Received: 17 Dec 2021
  • Revised: 17 May 2022
  • Accepted: 27 Jun 2022
  • Available online: 25 Aug 2022


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