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10 May 2019 Hyperspectral imaging applied to asbestos containing materials detection: specimen preparation and handling
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Abstract
Asbestos recognition, inside different matrices (i.e. Asbestos Containing Materials: ACMs), is of great importance both “in situ” and in the further analysis at lab scale. Among the industrial sectors utilizing asbestos, the building and construction sector is the most important, especially with reference to all the constructions built before the ‘90s. The large utilization of asbestos is mainly linked to its technical properties (i.e. resistance to abrasion, heat and chemicals). Despite its properties, asbestos is recognized as a hazardous material to human health and starting from the ‘80s its use was banned in many countries. Asbestos, in fact, is potentially dangerous due to the potential release in air of fibers that can be inhaled or ingested as a consequence of degradation/alteration phenomena and manipulation/handling activities. Fast and reliable recognition of ACMs, as well as ACMs degradation characteristics, represent two important targets to be reached. ACMs sample collection and their proper preparation and handling are two fundamental aspects in order to correctly perform the analyses, taking into account at the same time operators’ safety. In this paper these latter aspects, specifically investigated with reference to the utilization of an emerging and powerful analytical technique (i.e. hyperspectral imaging: HSI), were analyzed and discussed. Different preparation and sample handling procedures were set up and tested in order to reach the optimal conditions to perform all the analyses in safety, but at the same time not altering the optically acquired information at the base of the ACMs recognition/classification.
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Silvia Serranti, Giuseppe Bonifazi, Giuseppe Capobianco, Sergio Malinconico, and Federica Paglietti "Hyperspectral imaging applied to asbestos containing materials detection: specimen preparation and handling", Proc. SPIE 11007, Advanced Environmental, Chemical, and Biological Sensing Technologies XV, 110070S (10 May 2019); https://doi.org/10.1117/12.2517070
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