Asbestos containing materials detection and classification by the use of hyperspectral imaging

Our plain-English summary of Asbestos containing materials detection and classification by the use of hyperspectral imaging, by G. Bonifazi, G. Capobianco, S. Serranti (Journal of Hazardous Materials, 2018).

The study

• Authors: G. Bonifazi, G. Capobianco, S. Serranti
• Journal: Journal of Hazardous Materials
• Published: 2018;344:981-993
• Cited by: 26 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

In this work, hyperspectral imaging in the short wave infrared range (SWIR: 1000-2500nm) coupled with chemometric techniques was evaluated as an analytical tool to detect and classify different asbestos minerals, such as amosite ((Fe2+)2(Fe2+,Mg)5Si8O22(OH)2)), crocidolite (Na2(Mg,Fe)6Si8O22(OH)2) and chrysotile (Mg3(Si2O5)(OH)4), contained in cement matrices. Principal Component Analysis (PCA) was used for data exploration and Soft Independent Modeling of Class Analogies (SIMCA) for sample classification. The classification model was built using spectral characteristics of reference asbestos samples and then applied to the asbestos containing materials. Results showed that identification and classification of amosite, crocidolite and chrysotile was obtained based on their different spectral signatures, mainly related to absorptions detected in the hydroxyl combination bands, such as Mg-OH (2300nm) and Fe-OH (from 2280 to 2343nm). The developed SIMCA model showed very good specificity and sensitivity values (from 0.89 to 1.00). The correctness of classification results was confirmed by stereomicroscopic investigations, based on different color, morphological and morphometrical characteristics of asbestos minerals, and by micro X-ray fluorescence maps, through iron (Fe) and magnesium (Mg) distribution assessment on asbestos fibers. The developed innovative approach could represent an important step forward to detect asbestos in building materials and demolition waste.

Copyright © 2017 Elsevier B.V. All rights reserved.

In plain English

This was a laboratory study of a method for detecting asbestos in materials. It did not study people or health effects. The authors tested whether hyperspectral imaging in the short-wave infrared range (1000 to 2500 nm), combined with statistical classification, could detect and tell apart amosite, chrysotile and crocidolite. They built a classification model (called SIMCA) from one pure reference sample of each mineral, supplied by a company in Rome, Italy. When the model was checked by cross-validation on these reference samples, sensitivity and specificity were 0.90 and 0.91 for amosite, 1.00 and 0.93 for chrysotile, and 1.00 and 0.89 for crocidolite. The model was then applied to six certified asbestos-containing samples: a chrysotile rope, two asbestos-cement panels, an amosite fiber panel, amosite-containing plaster and a crocidolite water-tank fragment. The article reports that the fiber types mapped in each sample matched iron and magnesium maps made by micro X-ray fluorescence. No numerical agreement figures were given for these six samples. The authors concluded that these comparisons, together with microscope examination, confirmed the validity of the method.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Laboratory study developing and testing an analytical method (hyperspectral imaging with chemometric classification), checked against stereomicroscopy and micro X-ray fluorescence mapping
• Who was studied: Material samples, not people: three pure reference asbestos samples (one each of amosite, chrysotile and crocidolite) and six certified asbestos-containing material samples, all provided by VERAM srl, Rome, Italy. Dates of collection are not given.
• Exposure: No human exposure was studied. The materials analyzed were: chrysotile rope (F1); chrysotile and crocidolite asbestos-cement panel fragments (AM1, AM5); an amosite fiber-based panel fragment (AM2); amosite in plaster (AM3); and a crocidolite water-tank fragment (AM4).
• Main results:
  • In principal component analysis of the three reference samples, the spectra formed three separate groups, one for each mineral.
  • Chrysotile showed a strong absorption around 2300 nm linked to magnesium-hydroxyl (Mg-OH) bonds. Amosite showed an absorption from 2280 to 2343 nm linked to iron-hydroxyl (Fe-OH) bonds.
  • Sensitivity and specificity of the SIMCA model, checked by cross-validation on the reference sample spectra: amosite 0.90 and 0.91; chrysotile 1.00 and 0.93; crocidolite 1.00 and 0.89.
  • Applied to the chrysotile rope (F1), the model identified chrysotile fibers. Micro X-ray fluorescence magnesium maps of the same sample were reported as confirming this.
  • In the asbestos-cement panel sample AM1, the model identified both crocidolite and chrysotile. In the AM5 panel, it detected crocidolite in the larger fibers and chrysotile in the smaller fibers. In both cases this was reported as matching micro X-ray fluorescence maps.
  • The model identified amosite in samples AM2 and AM3 and crocidolite in sample AM4. In each case, iron distribution in micro X-ray fluorescence maps was reported as matching.
• The authors' conclusion: The authors concluded that comparison with micro X-ray fluorescence elemental maps and with stereomicroscopy confirmed the validity of the hyperspectral imaging-based model. They also stated that the approach could potentially be applied in situ; in this study it was tested only at laboratory scale.

Topics

Medical terms in this study, explained in our Lexicon: Asbestos, Amosite, Crocidolite, Chrysotile.

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