Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.

Methodological approach for the automatic discrimination of pictorial materials using fused hyperspectral imaging data from the visible to mid-infrared range coupled with machine learning methods

Hyperspectral imaging represents a powerful tool for the study of artwork's materials since it permits to obtain simultaneously information about the spectral behavior of the materials and their spatial distribution. By combining hyperspectral images performed on several spectral intervals (visible, near infrared and mid-infrared ranges) through chemometric methods it is possible to clearly identify most of the materials used in painting (i.e., pigments, dyes, varnishes, and binders). Moreover, in the last decade, the development of machine learning algorithms coupled with comprehensive and continuously updated databases opens new perspective on the automatic recognition of pictorial materials. In this work, we propose a novel procedure to support the automatic discrimination of pictorial materials consisting in a mid-level data fusion on imaging datasets coming from two commercial hyperspectral cameras, in the 400-1000 nm and 1000-2500 nm spectral ranges, respectively, and a MAcroscopic Fourier Transform InfRared scanning in reflection mode (MA-rFTIR), in the 7000 to 350 cm-1 (1428 nm - 28 μm) spectral range. The automatic recognition of 102 pictorial mock-ups from the fused data is performed by testing the performance of ECOC-SVM (error-correcting output coding and support vector machine) model obtaining a good predictive result with only few pixels that are confused with other classes. The methodology described in this paper demonstrates that an accurate paint layer multiclass recognition is feasible, and the use of chemometric approaches solves some challenges involving the study of materials.

Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process

In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy.

Arsenic accumulation in Pteris vittata: Time course, distribution, and arsenic-related gene expression in fronds and whole plantlets

In this work, arsenic (As) accumulation and distribution over time in Pteris vittata young fronds from adult plants and in whole plantlets, grown on a highly contaminated As-soil, was determined by μ-XRF. A linear increase in As content up to 60 days was found in young fronds at different times, and a progressive distribution from the apex to the base of the fronds was observed. In whole plantlets, As signal was detectable from 9 to 20 days in the apex of a few fronds and fiddleheads. Later, up to 60 days, As was localized in all fronds, in the rhizome and in basal part of the roots. The dynamics of expression of As-related genes revealed a good correlation between As content and the level of the As (III)-antiporter PvACR3 transcript in plantlets roots and fronds and in young fronds. Moreover, the transcription of As (V)-related gametophytic genes PvGAPC1, PvOCT4 increases over time during As accumulation while PvGSTF1 is expressed only in roots. Here, we demonstrate the suitability of the μ-XRF technique to monitor As accumulation, which allowed us to propose that As is initially directly transported to fiddleheads and apex of fronds, is later distributed to the whole fronds and simultaneously accumulated in the rhizome and roots. We also provide indications on the expression of candidate genes possibly involved in As (hyper)accumulation.

Asbestos in soil and water: A review of analytical techniques and methods

In this review the main standard and novel analytical techniques and methods for sampling, sample preparation, detection and quantification of asbestos in soil and water are described, compared and discussed in terms of advantages and limitations. An overview of common analytical methods applied for identification and quantification of airborne asbestos is preliminary provided, as they have been widely studied, due to the well-known human pathologies related to fibers inhalation. Despite the presence of asbestos in soil and water may also constitute a health risk, it has been less investigated and regulated. For these environmental matrices, the methods adopted at international and national scale, covering the whole analytical process, from sampling to management of data, are reported in depth, highlighting their limitations like sensitivity, reliability and reproducibility. Finally, different promising novel/unconventional methods, that may substitute or support traditional ones for asbestos detection both in environmental and anthropic matrices, are presented and critically evaluated.

Classification and distribution of freshwater microplastics along the Italian Po river by hyperspectral imaging

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Yellow Pigment Powders Based on Lead and Antimony: Particle Size and Colour Hue

This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, that, combined in different proportions and fired at different temperatures, times, and with various additives, gave materials of yellow colours, changing in hues and particle size. Artificial yellow pigments, based on lead and antimony, have been widely studied, but no specific investigation on particle size distribution and its correlation to colour hue has been performed before. In order to evaluate the particle size distribution, segmentation of sample data has been performed using the MATLAB software environment. The extracted parameters were examined by principal component analysis (PCA) in order to detect differences and analogies between samples on the base of those parameters. Principal component analysis was also applied to colour data acquired by a reflectance spectrophotometer in the visible range according to the CIELAB colour space. Within the two examined groups, i.e., yellows containing NaCl and those containing K-tartrate, differences have been found between samples and also between different areas of the same powder indicating the inhomogeneity of the synthesised pigments. On the other hand, colour data showed homogeneity within each yellow sample and clear differences between the different powders. The comparison of results demonstrates the potentiality of the particle segmentation and analysis in the study of morphology and distribution of pigment powders produced artificially, allowing the characterisation of the lead and antimony-based pigments through micro-image analysis and colour measurements combined with a multivariate approach.

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

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.

Phytoextraction efficiency of Pteris vittata grown on a naturally As-rich soil and characterization of As-resistant rhizosphere bacteria

This study evaluated the phytoextraction capacity of the fern Pteris vittata grown on a natural arsenic-rich soil of volcanic-origin from the Viterbo area in central Italy. This calcareous soil is characterized by an average arsenic concentration of 750 mg kg⁻¹, of which 28% is bioavailable. By means of micro-energy dispersive X-ray fluorescence spectrometry (μ-XRF) we detected As in P. vittata fronds after just 10 days of growth, while a high As concentrations in fronds (5,000 mg kg⁻¹), determined by Inductively coupled plasma-optical emission spectrometry (ICP-OES), was reached after 5.5 months. Sixteen arsenate-tolerant bacterial strains were isolated from the P. vittata rhizosphere, a majority of which belong to the Bacillus genus, and of this majority only two have been previously associated with As. Six bacterial isolates were highly As-resistant (> 100 mM) two of which, homologous to Paenarthrobacter ureafaciens and Beijerinckia fluminensis, produced a high amount of IAA and siderophores and have never been isolated from P. vittata roots. Furthermore, five isolates contained the arsenate reductase gene (arsC). We conclude that P. vittata can efficiently phytoextract As when grown on this natural As-rich soil and a consortium of bacteria, largely different from that usually found in As-polluted soils, has been found in P. vittata rhizosphere.

A dataset of visible - short wave infrared reflectance spectra collected in-vivo on the dorsal and ventral aspect of arms

Advancement of technology and device miniaturization have made near infrared spectroscopy (NIRS) techniques cost–effective, small–sized, simple, and ready to use. We applied NIRS to analyze healthy human muscles in vivo, and we found that this technique produces reliable and reproducible spectral “fingerprints” of individual muscles, that can be successfully discriminated by chemometric predictive models. The dataset presented in this descriptor contains the reflectance spectra acquired in vivo from the ventral and dorsal aspects of the arm using an ASD FieldSpec® 4 Standard–Res field portable spectroradiometer (350–2500 nm), the values of the anthropometric variables measured in each subject, and the codes to assist access to the spectral data. The dataset can be used as a reference set of spectral signatures of “biceps” and “triceps” and for the development of automated methods of muscle detection.

μXRF Mapping as a Powerful Technique for Investigating Metal Objects from the Archaeological Site of Ferento (Central Italy)

This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test pits, named IV and V, in which metal objects were found, mainly pertaining to the medieval period and never investigated before the present work from a compositional point of view. The potentiality of µXRF mapping was tested through a Bruker Tornado M4 equipped with an Rh tube, operating at 50 kV, 500 μA, and spot 25 μm obtained with polycapillary optics. Principal component analysis (PCA) and multivariate curve resolution (MCR) were used for processing the X-ray fluorescence spectra. The results showed that the investigated items are characterized by different compositions in terms of chemical elements. Three little wheels are made of lead, while the fibulae are made of copper-based alloys with varying amounts of tin, zinc, and lead. Only one ring is iron-based, and the other objects, namely a spatula and an applique, are also made of copper-based alloys, but with different relative amounts of the main elements. In two objects, traces of gold were found, suggesting the precious character of these pieces. MCR analysis was demonstrated to be particularly useful to confirm the presence of trace elements, such as gold, as it could differentiate the signals related to minor elements from those due to major chemical elements.

Non-destructive characterization of mechanically processed waste printed circuit boards - particle liberation analysis

This work aimed to develop and propose methods for evaluating the metal degree of liberation to characterize the metal deportment/concentration and liberation/association of mechanically processed waste Printed Circuit Boards (PCBs) that hold the complex and heterogeneity structure and metal distribution/association. Waste PCBs passed through a series of mechanical processing (i.e. comminution, sieving) for the metal recovery were characterized to understand and to evaluate the metal distribution and degree of liberation of the metals in order to optimize the comminution process, avoiding excessive fine particle production. The characterizations were performed at laboratory scale, as well as utilizing large scale experimental facilities, i.e. a portable X-Ray Fluorescence (XRF), micro-XRF and Synchrotron X-Ray Tomography. The proposed methods confirmed that metal liberation was very high in the fine size fraction (0.125-0.350 mm) while many locked particles were identified in the coarse size fraction (0.350-0.500 mm). Such results were analyzed and were discussed in order to better understand metal deportment/concentration behaviors. The advantages and disadvantages related to the different characterization approaches were identified and discussed in this paper, as well as their methodological developments in a waste PCBs' mechanical processing perspective.

Production of an innovative biowaste-derived fertilizer: Rapid monitoring of physical-chemical parameters by hyperspectral imaging

In this work the possibility to apply hyperspectral imaging as a fast and non-destructive technique for the monitoring of the production process at pilot plant scale of an innovative biowaste-derived fertilizer was explored. Different mixtures of urban organic waste, farm organic residues, biochar and vegetable active principles were selected and utilized in two different European countries, Italy and Spain, for the production of the innovative fertilizer. The biowaste-derived fertilizer samples were collected from the pilot plant piles at different curing time and acquired by the hyperspectral imaging device. Spectra have been collected in the near infrared wavelength range (1000-1700 nm). Conventional analyses were carried out on the same samples in order to find correlations between the physical-chemical parameters detected at laboratory scale, and the acquired reflectance spectra. The investigated parameters were: pH, electrical conductivity, soluble total organic carbon and soluble total nitrogen. Hyperspectral data were processed adopting chemometric strategies through the application of principal component analysis, for exploratory purposes, and partial least squares analysis to establish correlations between spectral features and measured physical-chemical parameters. Good correlations, with R² ranging between 0.85 and 0.96, were obtained for all the investigated parameters. Results showed as the proposed approach, based on hyperspectral imaging, is suitable to be adopted for a rapid and non-destructive monitoring of waste-derived fertilizer production.

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

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.

A new approach for the modelling of chestnut wood photo-degradation monitored by different spectroscopic techniques

The aim of this work is to study the colour and chemical modifications of the surfaces in chestnut wood samples as a consequence of irradiating in a controlled environment. The changes were investigated by a new analytical approach by combining traditional techniques such as reflectance spectrophotometry in the visible range and Fourier transform infrared spectroscopy with new hyperspectral imaging, in order to obtain forecast models to describe the phenomenon. The statistical elaboration of the experimental data allowed to validate the measurements and to obtain models enabling to relate the investigated parameters; the elaboration of the hyperspectral images by chemometric methods allowed for studying the changes in the reflectance spectra. A result of great importance is the possibility to correlate the oxidation of wood chemical components with the colour change in a totally non-invasive modality. This result is particularly relevant in the field of cultural heritage and in general in the control processes of wooden materials.

Protective behaviour monitoring on wood photo-degradation by spectroscopic techniques coupled with chemometrics

This paper reports the investigation on the effect of protective materials on poplar (Populus sp.) wood modifications as consequence of artificial photo-degradation in controlled environment. The novelty of this work is to try to understand what happens to wood surface under the protective layer. Shellac, beeswax and Linfoil® were tested to compare traditional and novel products generally used for wood. The samples, uncovered and covered by these protective layers, were artificially aged. Colour and chemical modifications due to ageing were investigated at different time intervals by reflectance spectrophotometry, Fourier transform infrared spectroscopy and hyperspectral imaging. The obtained data were elaborated by statistical and chemometric tools in order to verify their significance and to assess the relationship between groups of measurements. The results highlighted that shellac, beeswax and Linfoil® materials have a very low protective effect on wood photo-degradation for long times of exposure, by little reducing the changes of wood components.

Gravity packaging final waste recovery based on gravity separation and chemical imaging control

Plastic polymers are characterized by a high calorific value. Post-consumer plastic waste can be thus considered, in many cases, as a typical secondary solid fuels according to the European Commission directive on End of Waste (EoW). In Europe the practice of incineration is considered one of the solutions for waste disposal waste, for energy recovery and, as a consequence, for the reduction of waste sent to landfill. A full characterization of these products represents the first step to profitably and correctly utilize them. Several techniques have been investigated in this paper in order to separate and characterize post-consumer plastic packaging waste fulfilling the previous goals, that is: gravity separation (i.e. Reflux Classifier), FT-IR spectroscopy, NIR HyperSpectralImaging (HSI) based techniques and calorimetric test. The study demonstrated as the proposed separation technique and the HyperSpectral NIR Imaging approach allow to separate and recognize the different polymers (i.e. PolyVinyl Chloride (PVC), PolyStyrene (PS), PolyEthylene (PE), PoliEtilene Tereftalato (PET), PolyPropylene (PP)) in order to maximize the removal of the PVC fraction from plastic waste and to perform the full quality control of the resulting products, can be profitably utilized to set up analytical/control strategies finalized to obtain a low content of PVC in the final Solid Recovered Fuel (SRF), thus enhancing SRF quality, increasing its value and reducing the "final waste".

Concrete drill core characterization finalized to optimal dismantling and aggregates recovery

An innovative strategy, based on micro X-ray fluorescence and HyperSpectralImaging in the short wave infrared range (1000-2500nm), was developed in order to characterize drill core samples collected from End-of-Life concrete. Micro X-ray fluorescence maps were realized to check the drill cores chemical composition, to develop the best approach for HSI analyses and to verify the correctness of the obtained HSI results. HSI analysis was carried out in order to recognize and classify aggregates and mortar paste in concrete. A morphological and morphometrical analysis of aggregates was also carried out on the prediction maps. Results showed as the proposed approach can be profitably applied to analyze and characterize demolition waste materials before dismantling. Starting from an efficient in-situ characterization of the objects to dismantle, demolition actions can be optimized in order to maximize the EOL concrete derived materials, minimizing the final waste.

Upgrading of PVC rich wastes by magnetic density separation and hyperspectral imaging quality control

Polyvinylchloride (PVC) is one of the most produced polymers in Europe, with a share of 11% in terms of mass (8 milliontons) of total polymer consumption, but in 2010 only 5% of the total PVC production came from recycled materials, where other polymer recycling achieves a level of 15% on average. In order to find an innovative process to extract PVC from window frames waste, a combination of two innovative technologies was tested: magnetic density separation (MDS) and hyperspectral imaging (HSI). By its nature, MDS is a flexible high precision density separation technology that is applicable to any mixture of polymers and contaminants with non-overlapping densities. As PVC has a very distinctive high density, this technology was tested to obtain high-grade PVC pre-concentrates from window frame waste. HSI was used to perform a quality control of the products obtained by MDS showing that PVC was clearly discriminated from unwanted rubber particles of different colors. The results showed that the combined application of MDS and HSI techniques allowed to separate and to check the purity of PVC from window frame waste.

Micro-scale energy valorization of grape marcs in winery production plants

The Biochemical Methane Potential (BMP) of winery organic waste, with reference to two Italian red and white grapes (i.e. Nero Buono and Greco) by-products was investigated. The study was carried out to verify the possibility to reduce the production impact in a green-waste-management-chain-perspective. The possibility to efficiently utilize wine-related-by-products for energy production at a micro-scale (i.e. small-medium scale winery production plant) was also verified. Results showed as a good correlation can be established between the percentage of COD removal and the biogas production, as the winery can produce, from its waste methanization, about 7800 kW h year(-1) electrical and 8900 kW h year(-1) thermal. A critical evaluation was performed about the possibility to utilize the proposed approach to realize an optimal biomass waste management and an energetic valorization in a local-energy-production-perspective.

An innovative recycling process to obtain pure polyethylene and polypropylene from household waste

An innovative recycling process, based on magnetic density separation (MDS) and hyperspectral imaging (HSI), to obtain high quality polypropylene and polyethylene as secondary raw materials, is presented. More in details, MDS was applied to two different polyolefin mixtures coming from household waste. The quality of the two separated PP and PE streams, in terms of purity, was evaluated by a classification procedure based on HSI working in the near infrared range (1000-1700 nm). The classification model was built using known PE and PP samples as training set. The results obtained by HSI were compared with those obtained by classical density analysis carried in laboratory on the same polymers. The results obtained by MDS and the quality assessment of the plastic products by HSI showed that the combined action of these two technologies is a valid solution that can be implemented at industrial level.

Taking advantage of hyperspectral imaging classification of urinary stones against conventional infrared spectroscopy

The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.

Recycling-oriented characterization of plastic frames and printed circuit boards from mobile phones by electronic and chemical imaging

This study characterizes the composition of plastic frames and printed circuit boards from end-of-life mobile phones. This knowledge may help define an optimal processing strategy for using these items as potential raw materials. Correct handling of such a waste is essential for its further "sustainable" recovery, especially to maximize the extraction of base, rare and precious metals, minimizing the environmental impact of the entire process chain. A combination of electronic and chemical imaging techniques was thus examined, applied and critically evaluated in order to optimize the processing, through the identification and the topological assessment of the materials of interest and their quantitative distribution. To reach this goal, end-of-life mobile phone derived wastes have been systematically characterized adopting both "traditional" (e.g. scanning electronic microscopy combined with microanalysis and Raman spectroscopy) and innovative (e.g. hyperspectral imaging in short wave infrared field) techniques, with reference to frames and printed circuit boards. Results showed as the combination of both the approaches (i.e. traditional and classical) could dramatically improve recycling strategies set up, as well as final products recovery.

Biochemical methane potential (BMP) of artichoke waste: the inoculum effect

The aim of this work was to investigate anaerobic digestibility of artichoke waste resulting from industrial transformation. A series of batch anaerobic digestion tests was performed in order to evaluate the biochemical methane potential of the matrix in respect of the process. A comparison of the different performances of the laboratory-scale reactors operating in mesophilic conditions and utilizing three different values of the inoculum/substrate ratio was carried out. The best performance was achieved with an inoculum/substrate ratio of 2. Artichoke-processing byproducts showed a classical organic waste decomposition behaviour: a fast start-up phase, an acclimation stage, and a final stabilization phase. Following this approach, artichoke waste reached chemical oxygen demand removal of about 90% in 40 days. The high methane yield (average 408.62 mL CH4 gvs (-1) voltatile solids), makes artichoke waste a good product to be utilized in anaerobic digestion plants for biogas production.

Recycling-oriented characterization of polyolefin packaging waste

Packaging waste is one of the main sources of secondary polyolefins. It is essential to characterize polyolefins derived from this waste stream in such way, that not only mechanical sorting methods can effectively separate, but also that on-line sensor systems can quantitatively assess their distribution. The characterization methodology is hierarchical, relating all properties of waste particles in any phase of the processing ultimately to the input End-Of-Life products. The present paper documents a pre-concentrate obtained by hand picking of mixed Romanian household waste. Investigations have been addressed to identify the composition of this polyolefin waste stream, to study the polyolefin density distribution, to distinguish the polymer manufacturing methods (i.e. injection molding and blow molding) by flake physical properties and finally to perform all the required characterization and identification by hyperspectral imaging. On the basis of these analyses, polyolefins from packaging wastes can be recycled by density separation and their rheological properties and wall thickness indicate the molding procedures. Hyperspectral imaging based procedures have been also applied to set up quality control actions for recycled products.

Classification of oat and groat kernels using NIR hyperspectral imaging

An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains.

Hyperspectral imaging based method for fast characterization of kidney stone types

The formation of kidney stones is a common and highly studied disease, which causes intense pain and presents a high recidivism. In order to find the causes of this problem, the characterization of the main compounds is of great importance. In this sense, the analysis of the composition and structure of the stone can give key information about the urine parameters during the crystal growth. But the usual methods employed are slow, analyst dependent and the information obtained is poor. In the present work, the near infrared (NIR)-hyperspectral imaging technique was used for the analysis of 215 samples of kidney stones, including the main types usually found and their mixtures. The NIR reflectance spectra of the analyzed stones showed significant differences that were used for their classification. To do so, a method was created by the use of artificial neural networks, which showed a probability higher than 90% for right classification of the stones. The promising results, robust methodology, and the fast analytical process, without the need of an expert assistance, lead to an easy implementation at the clinical laboratories, offering the urologist a rapid diagnosis that shall contribute to minimize urolithiasis recidivism.

Characterization of post-consumer polyolefin wastes by hyperspectral imaging for quality control in recycling processes

In this paper new analytical inspection strategies, based on hyperspectral imaging (HSI) in the VIS-NIR and NIR wavelength ranges (400-1000 and 1000-1700 nm, respectively), have been investigated and set up in order to define quality control logics that could be applied at industrial plant level for polyolefins recycling. The research was developed inside the European FP7 Project W2Plastics "Magnetic Sorting and Ultrasound Sensor Technologies for Production of High Purity Secondary Polyolefins from Waste". The main aim of the project is the separation of pure polyethylene and polypropylene adopting an innovative process, the magnetic density separation (MDS). Spectra of plastic particles and contaminants resulting from post-consumer complex wastes and of virgin polyolefins have been acquired by HSI and by Raman spectroscopy. The classification results obtained applying principal component analysis (PCA) on HSI data have been compared with those obtained by Raman spectroscopy, in order to validate the proposed innovative methodology. Results showed that HSI sensing techniques allow to identify both polyolefins and contaminants. Results also demonstrated that HSI has a great potentiality as a tool for quality control of feed (identification of contaminants in the plastic waste) and of the two different pure polypropylene and polyethylene flow streams resulting from the MDS-based recycling process.

Early detection of toxigenic fungi on maize by hyperspectral imaging analysis

Fungi can grow on many food commodities. Some fungal species, such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger and Fusarium spp., can produce, under suitable conditions, mycotoxins, secondary metabolites which are toxic for humans and animals. Toxigenic fungi are a real issue, especially for the cereal industry. The aim of this work is to carry out a non destructive, hyperspectral imaging-based method to detect toxigenic fungi on maize kernels, and to discriminate between healthy and diseased kernels. A desktop spectral scanner equipped with an imaging based spectrometer ImSpector- Specim V10, working in the visible-near infrared spectral range (400-1000 nm) was used. The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective: i.e. from 48 h after inoculation with A. niger or A. flavus.

Non-parametric analysis of infrared spectra for recognition of glass and glass ceramic fragments in recycling plants

Glass ceramic detection in glass recycling plants represents a still unsolved problem, as glass ceramic material looks like normal glass and is usually detected only by specialized personnel. The presence of glass-like contaminants inside waste glass products, resulting from both industrial and differentiated urban waste collection, increases process production costs and reduces final product quality. In this paper an innovative approach for glass ceramic recognition, based on the non-parametric analysis of infrared spectra, is proposed and investigated. The work was specifically addressed to the spectral classification of glass and glass ceramic fragments collected in an actual recycling plant from three different production lines: flat glass, colored container-glass and white container-glass. The analyses, carried out in the near and mid-infrared (NIR-MIR) spectral field (1280-4480 nm), show that glass ceramic and glass fragments can be recognized by applying a wavelet transform, with a small classification error. Moreover, a method for selecting only a small subset of relevant wavelength ratios is suggested, allowing the conduct of a fast recognition of the two classes of materials. The results show how the proposed approach can be utilized to develop a classification engine to be integrated inside a hardware and software sorting architecture for fast "on-line" ceramic glass recognition and separation.

Spectral cullet classification in the mid-infrared field for ceramic glass contaminants detection

The presence of glass-like contaminants inside waste glass products, usually resulting from both industrial and differentiated urban waste collection, has greatly increased in recent years, due to the introduction to the market of a large amount of goods manufactured from ceramic glass. The presence of contaminants in the glass recycling streams reduces product quality and increases production costs. The detection of ceramic glass detection is an unresolved problem, as such material looks like normal glass and can only be detected by trained personnel. In this study an innovative approach to ceramic glass recognition, based on the spectral signature in the mid-infrared (MIR) field, was proposed and investigated. The study specifically addressed the spectral characterization of glass and ceramic glass fragments collected in a real recycling plant from two different production lines: coloured container glass and white container glass. To define suitable inspection strategies to separate the useful (glass) from the polluting (ceramic glass) materials at the recycling plants, fragments presenting different colour, thickness, size, shape and manufacturing were selected. Both dirty and clean cullet was considered. The analyses, carried out in the MIR spectral field (2280-4480 nm), show that ceramic glass and glass fragments can be recognized according to their different spectral signature. In particular, by selecting a specific wavelength ratio the two classes of materials can be rapidly recognized, suggesting the possibility of developing an integrated hardware and software sorting system for 'on-line' ceramic glass separation.

Imaging spectroscopy based strategies for ceramic glass contaminants removal in glass recycling

The presence of ceramic glass contaminants in glass recycling plants reduces production quality and increases production costs. The problem of ceramic glass inspection is related to the fact that its detectable physical and pictorial properties are quite similar to those of glass. As a consequence, at the sorting plant scale, ceramic glass looks like normal glass and is detectable only by specialized personnel. In this paper an innovative approach for ceramic glass recognition, based on imaging spectroscopy, is proposed and investigated. In order to define suitable inspection strategies for the separation between useful (glass) and polluting (ceramic glass) materials, reference samples of glass and ceramic glass presenting different colors, thicknesses, shapes and manufacturing processes have been selected. Reflectance spectra have been obtained using two equipment covering the visible and near infrared wavelength ranges (400-1000 and 1000-1700 nm). Results showed as recognition of glass and ceramic glass is possible using selected wavelength ratios, in both visible and near infrared fields.