Hyperspectral imaging applied to asbestos containing materials detection: specimen preparation and handling

Serranti, Silvia; Bonifazi, Giuseppe; Capobianco, Giuseppe; Malinconico, Sergio; Paglietti, Federica

doi:10.1117/12.2517070

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Increasing the number of the acquired samples, the noise effect will be reduced, thanks to the increase of spectral data set representative of each class. result does not negatively affect ACM identification, they also highlights how low spectral resolution generates more noise in prediction than that produced according to different set up adopted in our previous studies [20,36,39]. Increasing the number of the acquired samples, the noise effect will be reduced, thanks to the increase of spectral data set representative of each class.…”

Section: Classificationmentioning

confidence: 53%

“…The analysis of Figure 8c clearly shows as the unclassified pixels are due to the "border effect" present in all the analyzed samples and to the class NC (i.e., not classified individuals), that a class of material not containing asbestos (i.e., drywall) but, at the same time, not belonging, for their detected spectral attributes, to any of the previously defined classes, that is: AGGREGATES MORTAR AND BRICK, FIBER GLASS and ORGANIC. Although the obtained result does not negatively affect ACM identification, they also highlights how low spectral resolution generates more noise in prediction than that produced according to different set up adopted in our previous studies [20,36,39]. Increasing the number of the acquired samples, the noise effect will be reduced, thanks to the increase of spectral data set representative of each class.…”

Section: Classificationmentioning

confidence: 54%

“…Multivariate approach, HIS based, was largely adopted in many research fields [29][30][31][32] to manage the huge amount of data and to utilize the information to identify, to characterize and to sort ACM. The aim of the chemometric approach was to obtain a data dimensionality reduction for a better data spectral evaluation and to develop classification algorithm for an efficient handling of multiple classes when hyperspectral imaging [33][34][35][36][37][38][39][40][41] sorting strategies have to be set up. Furthermore, the proposed approach also represents a step forward with respect to safety, being applied with a minor exposure risk for workers.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019

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Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.

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Section: Classificationmentioning

confidence: 53%

Section: Classificationmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

2019

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“…Four pure asbestos samples (Chrysotile standard intermediate 031G, Chrysotile standard NIEHS plastibest 20, Amosite Standard 312M, Amosite standard NIEHS, Crocidolite standard 5174, Crocidolite from Balangero industrial plant, and 'matrix without asbestos'), provided by National Institute for Insurance against Accidents at Work (INAIL) (Rome, Italy), were analyzed in order to create a calibration and validation set (Figure 1). The samples were sealed up in borosilicate glass Petri cups as showed in Figure 1 (Serranti et al, 2019). Furthermore, three asbestos certified samples constituted by amosite, chrysotile and crocidolite fibers were acquired.…”

Section: Methodsmentioning

confidence: 99%

Asbestos Detection in Construction and Demolition Waste Adopting Different Classification Approaches Based on Short Wave Infrared Hyperspectral Imaging

et al. 2022

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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.

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“…Post-earthquake Building Waste (PBW) belong to the category of Construction and Demolition Waste (CDW) are mainly composed of materials like concrete, glass, asphalt, wood and also some hazardous materials like asbestos, still present in old buildings built before its ban in 1975 (Tabata et al, 2022). Asbestos is a fibrous mineral widely used in a variety of building materials due to its extraordinary tensile strength and resistance to heat and corrosion (Gualtieri, 2017;Paglietti et al, 2019). However, it causes serious diseases such as lung cancer, mesothelioma (Azuma et al, 2009) and asbestosis (EPA, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Detection of Asbestos Containing Material in Post-Earthquake Building Waste Through Hyperspectral Imaging and Micro-X-Ray Fluorescence

et al. 2022

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During an earthquake, a large amount of waste was generated, and many Asbestos-Containing Materials (ACM) were unintentionally destroyed. ACM is a mixture of cement matrix and asbestos fiber, widely used in construction materials, that causes serious diseases such as lung cancer, mesothelioma and asbestosis, as a consequence of inhalation of the asbestos fiber. In order to reuse and recycle Post-earthquake Building Waste (PBW) as secondary raw material, ACM must be separately collected and deposited from other wastes during the recycling process. The work aimed to develop a non-destructive, accurate and rapid method to detect ACM and recognize different types of PBW to obtain the best method to correctly identify and separate different types of material. The proposed approach is based on Hyperspectral Imaging (HSI) working in the short-wave infrared range (SWIR, 1000-2500 nm), followed by the implementation of a classification model based on hierarchical Partial Least Square Discriminant Analysis (hierarchical-PLS-DA). Micro-X-ray fluorescence (micro-XRF) analyses were carried out on the same samples in order to evaluate the reliability, robustness and analytical correctness of the proposed HSI approach. The results showed that the applied technology is a valid solution that can be implemented at the industrial level.

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Hyperspectral imaging applied to asbestos containing materials detection: specimen preparation and handling

Cited by 5 publications

References 17 publications

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

Asbestos Detection in Construction and Demolition Waste Adopting Different Classification Approaches Based on Short Wave Infrared Hyperspectral Imaging

Detection of Asbestos Containing Material in Post-Earthquake Building Waste Through Hyperspectral Imaging and Micro-X-Ray Fluorescence

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