scholarly journals Recycling-Oriented Characterization of Post-Earthquake Building Waste by Different Sensing Techniques

2021 ◽  
Vol 7 (9) ◽  
pp. 182
Author(s):  
Oriana Trotta ◽  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco ◽  
Silvia Serranti
Keyword(s):  
Cement Mortar ◽  
Methodological Approach ◽  
Principal Component ◽  
Short Wave ◽  
Infrared Range ◽  
Classification Models ◽  
Chemometric Methods ◽  
X Ray ◽  
Non Destructive

In this paper, a methodological approach based on hyperspectral imaging (HSI) working in the short-wave infrared range (1000–2500 nm) was developed and applied for the recycling-oriented characterization of post-earthquake building waste. In more detail, the presence of residual cement mortar on the surface of tile fragments that can be recycled as aggregates was estimated. The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squares-discriminant analysis (PLS-DA) to build classification models. Micro-X-ray fluorescence (micro-XRF) maps were also obtained on the same samples in order to validate the HSI classification results. Results showed that it is possible to identify cement mortar on the surface of the recycled tile, evaluating its degree of liberation. The recognition is automatic and non-destructive and can be applied for recycling-oriented purposes at recycling plants.

scholarly journals Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

2020 ◽  
Author(s):  
Vitor J Bianchini ◽  
Gabriel M Mascarin ◽  
Lúcia CAS Silva ◽  
Valter Arthur ◽  
Jean M Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Seed Quality ◽  
Alternative Methods ◽  
Quality Analysis ◽  
Classification Models ◽  
X Ray ◽  
Imaging Technologies ◽  
X Ray Imaging ◽  
Non Destructive

Abstract Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

scholarly journals Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

2020 ◽  
Author(s):  
Vitor J Bianchini ◽  
Gabriel M Mascarin ◽  
Lúcia CAS Silva ◽  
Valter Arthur ◽  
Jean M Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Seed Quality ◽  
Alternative Methods ◽  
Quality Analysis ◽  
Classification Models ◽  
X Ray ◽  
Imaging Technologies ◽  
X Ray Imaging ◽  
Non Destructive

Abstract Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

scholarly journals Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Vitor de Jesus Martins Bianchini ◽  
Gabriel Moura Mascarin ◽  
Lúcia Cristina Aparecida Santos Silva ◽  
Valter Arthur ◽  
Jens Michael Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Seed Quality ◽  
Alternative Methods ◽  
Quality Analysis ◽  
Classification Models ◽  
X Ray ◽  
Imaging Technologies ◽  
X Ray Imaging ◽  
Non Destructive

Abstract Background The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

scholarly journals Biodegradable and Compostable Shopping Bags under Investigation by FTIR Spectroscopy

2021 ◽  
Vol 11 (2) ◽  
pp. 621
Author(s):  
Silvana Alfei ◽  
Anna Maria Schito ◽  
Guendalina Zuccari
Keyword(s):  
Principal Component Analysis ◽  
Ftir Spectroscopy ◽  
Principal Component ◽  
Poly Lactic Acid ◽  
Chemometric Methods ◽  
Industrial Sectors ◽  
Fresh Food ◽  
Extruded Films ◽  
Productive Process ◽  
Non Destructive

In the recent years, plastic-based shopping bags have become irregular and progressively replaced by compostable ones. To be marketed, these “new plastics” must possess suitable requirements verified by specific bodies, which grant the conformity mark, and the approved physicochemical properties are periodically verified. The fast, inexpensive, non-destructive, easy to use, and reproducible Fourier-Transform infrared (FTIR) spectroscopy is a technique routinely applied to perform analysis in various industrial sectors. To get reliable information from spectral data, chemometric methods, such as Principal Component Analysis (PCA), are commonly suggested. In this context, PCA was herein performed on 4, 5, and 21 × 3251 matrices, collecting the FTIR data from regular and irregular shopping bags, including three freshly extruded films from the Italian industry MecPlast, to predict their compliance with legislation. The results allowed us to unequivocally achieve such information and to classify the bags as suitable for containing fresh food in bulk or only for transport. A self-validated linear model was developed capable to estimate, by acquiring a single FTIR spectrum if, after the productive process, the content of renewable poly-lactic-acid (PLA) in a new produced film respect the expectations. Surprisingly, our findings established that among the grocery bags available on the market, irregular plastic-based shopping bags continue to survive.

Handheld Portable Energy-Dispersive X-Ray Fluorescence Spectrometry (pXRF)

2016 ◽  
pp. 362-381 ◽  
Author(s):  
Elisabeth Holmqvist
Keyword(s):  
Production Systems ◽  
Matrix Effects ◽  
Chemical Characterization ◽  
High Sensitivity ◽  
Limited Range ◽  
Energy Dispersive ◽  
Analytical Sensitivity ◽  
X Ray ◽  
Non Destructive

Handheld portable energy-dispersive X-ray fluorescence (pXRF) spectrometry is used for non-destructive chemical characterization of archaeological ceramics. Portable XRF can provide adequate analytical sensitivity to discriminate geochemically distinct ceramic pastes, and to identify compositional clusters that correlate with data patterns acquired by NAA or other high sensitivity techniques. However, successful non-destructive analysis of unprepared inhomogeneous ceramic samples requires matrix-defined scientific protocols to control matrix effects which reduce the sensitivity and precision of the instrumentation. Quantification of the measured fluorescence intensities into absolute concentration values and detection of light elements is encumbered by the lack of matrix matched calibration and proper vacuum facilities. Nevertheless, semi-quantitative values for a limited range of high Z elements can be generated. Unstandardized results are difficult to validate by others, and decreased analytical resolution of non-destructive surface analysis may disadvantage site-specific sourcing, jeopardize correct group assignments, and lead to under-interpretation of ceramic craft and production systems.

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

2019 ◽  
Vol 9 (21) ◽  
pp. 4587 ◽  
Author(s):  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco ◽  
Silvia Serranti
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Principal Component ◽  
Short Wave ◽  
Partial Least Square ◽  
Least Square ◽  
Construction And Demolition Waste ◽  
Infrared Range ◽  
Demolition Waste ◽  
Control Approach

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.

Non-destructive characterization of lithium deposition at the Li/separator and Li/carbon matrix interregion by synchrotron X-ray tomography

Nano Energy ◽  
2019 ◽  
Vol 62 ◽  
pp. 11-19 ◽  
Author(s):  
Kang Dong ◽  
Markus Osenberg ◽  
Fu Sun ◽  
Henning Markötter ◽  
Charl J. Jafta ◽  
...  
Keyword(s):  
Carbon Matrix ◽  
X Ray ◽  
Lithium Deposition ◽  
Non Destructive ◽  
Non Destructive Characterization

scholarly journals Parameters based on X-ray images to assess the physical and physiological quality of Leucaena leucocephala seeds

2018 ◽  
Vol 42 (6) ◽  
pp. 643-652 ◽  
Author(s):  
André Dantas de Medeiros ◽  
Joyce de Oliveira Araújo ◽  
Manuel Jesús Zavala León ◽  
Laércio Junio da Silva ◽  
Denise Cunha Fernandes dos Santos Dias
Keyword(s):  
Leucaena Leucocephala ◽  
High Performance ◽  
Principal Component ◽  
Automated Analysis ◽  
Radiographic Images ◽  
X Ray ◽  
Physiological Quality ◽  
Non Destructive ◽  
Do So

ABSTRACT Non-destructive and high performance analyses are highly desirable and important for assessing the quality of forest seeds. The aim of this study was to relate parameters obtained from semi-automated analysis of radiographs of Leucaena leucocephala seeds to their physiological potential by means of multivariate analysis. To do so, seeds from five lots collected from parent trees from the region of Viçosa, MG, Brazil, were used. The study was carried out through analysis of radiographic images of seeds, from which the percentage of damaged seeds (predation and fungi), and measurements of area, perimeter, circularity, relative density, and integrated density of the seeds were obtained. After the X-ray test, the seeds were tested for germination in order to assess variables related to seed physiological quality. Multivariate statistics were applied to the data generated, with use of principal component analysis (PCA). X-ray testing allowed visualization of details of the internal structure of seeds and differences regarding density of seed tissues. Semi-automated analysis of radiographic images of Leucaena leucocephala seeds provides information on seed physical characteristics and generates parameters related to seed physiological quality in a simple, fast, and inexpensive manner.

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