Near infrared hyperspectral imaging-based approach for end-of-life flat monitors recycling

Bonifazi, Giuseppe; Gasbarrone, Riccardo; Palmieri, Roberta; Serranti, Silvia

doi:10.1515/auto-2019-0058

Cited by 15 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visible and NIR spectroscopy techniques are actually utilized to perform both qualitative and quantitative analysis in different fields: i.e. in primary/secondary raw materials sector [4] , [5] , in cultural heritage [6] , [7] , in the agricultural and food industry [8] , [9] , [10] , [11] , [12] , [13] , in the pharmaceutical and chemical industry [14] , [15] , in clinical application [16] , [17] , [18] and more generally in analytical science [19] to perform systematic environmental remote and proximal sensing. In this data descriptor, reflectance Visible (Vis) and Short Wave Infrared (SWIR) spectra of pre-cooked pasta products are reported.…”

Section: Data Descriptionmentioning

confidence: 99%

A dataset of visible – Short wave InfraRed reflectance spectra collected on pre-cooked pasta products

et al. 2021

Self Cite

View full text Add to dashboard Cite

Reflectance Visible (Vis) and Short Wave Infrared (SWIR) spectra of pre-cooked pasta products were collected in a non-invasive way by using an ASD FieldSpec® 4 Standard–Res portable spectrophotoradiometer (350–2500 nm). Vis-SWIR data were collected on 6 samples of Pennette 72 and 6 samples of Mezze Penne with different salting levels with the aid of a contact probe in two different physical conditions: i) frozen and ii) thawed. Fifty Vis-SWIR spectra were collected per measurement time from each sample resulting in 1200 raw spectra. The dataset presented in this descriptor can be used to explore the possibilities to develop automated methods to perform pre-cooked pasta analysis. Vis-SWIR data have potential reuse for follow-up studies finalized to develop pre-cooked pasta quality control applications by using similar devices or to test the ability of different chemometric algorithms.

show abstract

Section: Data Descriptionmentioning

confidence: 99%

A dataset of visible – Short wave InfraRed reflectance spectra collected on pre-cooked pasta products

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…This study aimed to define a method based on Hyperspectral Imaging (HSI) [7][8][9][10][11] to automatically detect and identify contaminants like plastic, wood and drywall in a post-earthquake building waste flow stream. To reach this goal HSI *silvia.serranti@uniroma1.it; phone 39 6 44585 360; fax 39 06 44585 618; www.uniroma1.it data were used in the Short-Wave InfraRed (SWIR) range (1000-2500 nm), applying different chemometric methods, like principal component analysis for data exploration and partial least-square-discriminant analysis to build classification models.…”

Section: Introductionmentioning

confidence: 99%

An innovative approach based on hyperspectral imaging for an automatic characterization of post-earthquake building waste

Bonifazi¹,

Capobianco²,

Serranti³

et al. 2023

Photonic Instrumentation Engineering X

View full text Add to dashboard Cite

The construction sector produces more than one-third of the world's solid waste. Construction and demolition waste (CDWs) are generated from the construction, renovation and demolition of buildings, roads, bridges and other structures. Moreover, CDW include the materials that may suddenly be generated by natural disasters, such as earthquakes and floods. Post-earthquake building waste (PBW) is typically composed of a mixture of different materials, such as concrete, bricks, tiles, ceramics, wood, glass, gypsum and plastic. These materials represent, if properly separated, a high potential for recycling and reuse particularly the inert fraction, representing about 70% of the total. From this perspective, this work aims to develop an innovative strategy based on optical sensing in order to identify and classify different types of PBW coming from a post-earthquake site (Amatrice, Italy). A strategy based on hyperspectral imaging (HSI) working in the SWIR range (1000-2500 nm) was developed. The acquired hyperspectral images were analyzed using different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squarediscriminant analysis (PLSDA) to build a classification model. Results showed that the proposed approach allows to recognize and classify inert fractions from contaminants (i.e., wood, plastics and drywall). The obtained results show how HSI could be particularly suitable to perform classification in complex scenarios as produced by earthquakes.

show abstract

“…HSI is a technology allowing the collection of both spatial and spectral information at the same time, thus providing several physical and chemical characteristics of the analyzed material [27]. In literature, several studies related to NIR-his-based applications for WEEE material detection [28][29][30][31][32] and polymer identification and/or characterization [33][34][35][36][37][38][39][40] are reported.…”

Section: Introductionmentioning

confidence: 99%

Detection of Brominated Plastics from E-Waste by Short-Wave Infrared Spectroscopy

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this work, the application of Short-Wave Infrared (SWIR: 1000–2500 nm) spectroscopy was evaluated to identify plastic waste containing brominated flame retardants (BFRs) using two different technologies: a portable spectroradiometer, providing spectra of single spots, and a hyperspectral imaging (HSI) platform, acquiring spectral images. X-ray Fluorescence (XRF) analysis was preliminarily performed on plastic scraps to analyze their bromine content. Chemometric methods were then applied to identify brominated plastics and polymer types. Principal Component Analysis (PCA) was carried out to explore collected data and define the best preprocessing strategies, followed by Partial Least Squares—Discriminant Analysis (PLS-DA), used as a classification method. Plastic fragments were classified into “High Br content” (Br > 2000 mg/kg) and “Low Br content” (Br < 2000 mg/kg). The identified polymers were acrylonitrile butadiene styrene (ABS) and polystyrene (PS). Correct recognition of 89–90%, independently from the applied technique, was achieved for brominated plastics, whereas a correct recognition ranging from 81 to 89% for polymer type was reached. The study demonstrated as a systematic utilization of both the approaches at the industrial level and/or at laboratory scale for quality control can be envisaged especially considering their ease of use and the short detection response.

show abstract

Near infrared hyperspectral imaging-based approach for end-of-life flat monitors recycling

Cited by 15 publications

References 29 publications

A dataset of visible – Short wave InfraRed reflectance spectra collected on pre-cooked pasta products

A dataset of visible – Short wave InfraRed reflectance spectra collected on pre-cooked pasta products

An innovative approach based on hyperspectral imaging for an automatic characterization of post-earthquake building waste

Detection of Brominated Plastics from E-Waste by Short-Wave Infrared Spectroscopy

Contact Info

Product

Resources

About