Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques

Giuseppe Bonifazi (), Sergio Bellagamba, Giuseppe Capobianco, Riccardo Gasbarrone (), Ivano Lonigro, Sergio Malinconico, Federica Paglietti and Silvia Serranti
Additional contact information
Giuseppe Bonifazi: Department of Chemical Engineering, Materials and Environment (DICMA), Sapienza University of Rome, Via Eudossiana 18, 00185 Roma, Italy
Sergio Bellagamba: Department of New Technologies for Occupational Safety of Industrial Plants, Products and Human Settlements (Dit), Inail (Italian Workers’ Compensation Authority-Research Division), Via R. Ferruzzi 38/40, 00143 Roma, Italy
Giuseppe Capobianco: Department of Chemical Engineering, Materials and Environment (DICMA), Sapienza University of Rome, Via Eudossiana 18, 00185 Roma, Italy
Riccardo Gasbarrone: Research and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza University of Rome, Via XXIV Maggio 7, 04100 Latina, Italy
Ivano Lonigro: Research and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza University of Rome, Via XXIV Maggio 7, 04100 Latina, Italy
Sergio Malinconico: Department of New Technologies for Occupational Safety of Industrial Plants, Products and Human Settlements (Dit), Inail (Italian Workers’ Compensation Authority-Research Division), Via R. Ferruzzi 38/40, 00143 Roma, Italy
Federica Paglietti: Department of New Technologies for Occupational Safety of Industrial Plants, Products and Human Settlements (Dit), Inail (Italian Workers’ Compensation Authority-Research Division), Via R. Ferruzzi 38/40, 00143 Roma, Italy
Silvia Serranti: Department of Chemical Engineering, Materials and Environment (DICMA), Sapienza University of Rome, Via Eudossiana 18, 00185 Roma, Italy

Sustainability, 2025, vol. 17, issue 3, 1-31

Abstract: Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best ( R e c a l l M and A c c u r a c y M equal to 1.00), followed by PCA-KNN ( R e c a l l M of 0.98–1.00 and A c c u r a c y M equal to 1.00). Poorer performances were obtained by PLS-DA ( R e c a l l M of 0.68–0.72 and A c c u r a c y M equal to 0.95) and PCA-DA ( R e c a l l M of 0.66–0.70 and A c c u r a c y M equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies.

Keywords: asbestos; naturally occurring asbestos; asbestos-containing materials; contaminated soil; NIR spectroscopy; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/3/972/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/3/972/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:3:p:972-:d:1576648

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().