An innovative approach based on hyperspectral imaging (HSI) was developed to monitor commercial starch-based (MaterBi®) disposable bioplastic behavior during anaerobic degradation. Mater-Bi® (MB) tableware items were selected among the ones available in supermarkets and compliant with the EN 13432 standard (EN 13432:2008). The MB items were manually cut in fragments with size ranging from 0.5 to 2 cm, removing the edges and the bottom to ensure test material homogeneity in terms of thickness. The anaerobic sludge was collected at a full-scale mesophilic anaerobic digestion plant treating a mixture of organic residues from food industries and was used as inoculum. Hyperspectral images of the samples composed of MB fragments dispersed in the anaerobic sludge were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). A chemometric approach was then developed to analyze HSI data. In more detail, Principal Component Analysis (PCA) was applied for data exploration, followed by the implementation of a classification model based on Partial Least Square-Discriminant Analysis (PLS-DA) able to identify MB in the sludge. The achieved results are very promising, especially with reference to the possibility to adopt a fast strategy to monitor the behavior of MB during the anaerobic biodegradation process.
Wine is a widely diffused beverage in the world and its production and quality are greatly influenced by the health conditions of the vine plants. The aim of the study was to investigate the possibility to monitor by Hyperspectral Imaging (HSI) the macronutrients (i.e., Ca, K, etc.) and micronutrients (i.e., Mn, Cu, Zn, etc.) variation in leaves sampled from different areas of a vineyard. The proposed approach is based on the acquisition by HSI in the Short-Wave Infrared Range (SWIR: 1000-2500 nm), of dried and milled vine leaves, followed by the implementation of a classification model based on Partial Least Square (PLS). Micro-X-Ray Fluorescence (micro-XRF) analyses were carried out on the same samples to correlate the SWIR spectral signatures with the detected chemical elements. Furthermore, HSI-based prediction maps, representative of the chemical elements distribution in samples were obtained. The achieved results are very promising, especially with reference to the possibility to adopt a fast strategy to monitor the macro- and micronutrients variation in the leaves directly in field, allowing to treat in real-time any nutritional deficiencies.
Hyperspectral imaging (HSI) is currently more and more utilized in waste recycling industry for both sensor-based sorting and quality control applications. Plastic waste is one of the flow streams in which HSI is particularly effective, due to its high capability of polymer identification in the near and short-wave infrared range, allowing to achieve high purity recycled plastic products and, therefore, secondary raw materials characterized by high quality. The aim of this work was to evaluate the potential of HSI-based data fusion, to achieve simultaneous identification of post-consumer plastic packaging flakes by polymer and color. Five different polymers among those commonly used for plastic packaging, i.e., polystyrene (PS), polyethylene terephthalate (PET), expanded polystyrene (EPS), polyethylene (PE), and polypropylene (PP), subdivided in 6 different color classes (orange, red, transparent, green, blue and white) were investigated. Two different HSI devices were used to perform the polymer and color identification, operating in the short-wave infrared range (1000-2500 nm) and in the visible range (400-750 nm), respectively. A hierarchical classification model based on partial least square - discriminant analysis (PLS-DA) was built in order to obtain a highlevel efficiency in prediction for all classes. The performances of the model were evaluated in terms of sensitivity, specificity, precision and F1 score. The obtained results were very promising, showing how HSI coupled with data fusion can be utilized as a non-invasive, fast and efficient tool to obtain high-quality recycled plastics, optimizing the industrial plastic recycling process.
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-square-discriminant 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.
Arsenic (As) is recognized as one of the main toxicants worldwide. Arsenic in the environment can be due to natural sources such as the weathering of rocks and volcanic material, but its presence is increasing due to anthropogenic activities such as the use of pesticides, industrial waste and smelting. The accumulation of this heavy metal in the biosphere produces serious effects on the environment and health. Nowadays, the removal of As from drinking water to respect the law limit involves high costs filtering systems. Therefore, a low cost and eco-sustainable strategy based on the use of ferns for phytodepuration of As-contaminated groundwater was developed. The aim of this work was to investigate the possibility of monitoring, by spectroscopy working in the Vis–SWIR regions (350 – 2500 nm), the phytoextraction capacity of the hyper-accumulator Pteris vittata fern, hydroponically grown in greenhouse conditions. The proposed approach is non-destructive, being based on the acquisition of spectroscopic data on fern leaves, followed by chemometric analysis. Reflectance spectra were acquired by a portable spectrophotoradiometer (ASD FieldSpec® 4 Standard–Res). Comparative evaluations were then performed analyzing Pteris vittata leaves (fronds) collected from plants grown on both As contaminated and not contaminated water. The achieved results are very promising for the further development of a full on-site scale monitoring of the phytoremediation process.
The increasing normative requirements and market competitiveness lead the agricultural sector and the food industry to constantly look for new fast and non-destructive classification logics that can be applied for product sorting applications and/or quality control actions. With reference to hazelnut production, the dried fruits must be sorted from unwanted foreign bodies or inedible hazelnuts that can negatively affect the quality of the final product. In this perspective, the utilization of HyperSpectral Imaging (HSI) can be applied to set-up a novel hazelnuts quality control. Hazelnuts and contaminants were acquired by a push-broom hyperspectral device working in the Short-Wave InfraRed (SWIR: 1000-2500 nm) region. A PLSDA model was set up in order to identify 3 classes of products (i.e. edible hazelnuts, hazelnut shells and rotten hazelnuts) with the highest level of efficiency in full spectrum mode (Precision = 0.92, Accuracy = 0.94, Efficiency = 0.94). Subsequently, different variable selection methods (i.e. Interval PLSDA, Selectivity Ratio and Variable Importance in Projection score methods) were adopted in order to identify the fundamental bands to recognize the 3 classes and evaluate which of the variable selection methods shows efficiency values close to the values obtained by the full spectrum mode. VIP score-based classification showed the best performance, with Precision, Accuracy and Efficiency values equal to those based on full spectrum PLSDA. Classification results suggest that this methodological approach can be powerful to develop and implement hazelnut sorting and/or quality control strategies. Moreover, the variable selection approach allows to increase processing speed , compared to that in full spectrum mode, making possible online applications directly at plant scale.
An innovative approach, based on hyperspectral imaging (HSI) coupled with chemometrics, allowing the detection of arsenic (As) in the hyper-accumulator fern Pteris Vittata L., is presented in this study. The aim of this work was to investigate the possibility of monitoring by HSI the As sequestration capacity of plants grown on As-contaminated soils, in order to perform soil remediation. The proposed approach is based on the acquisition by HSI in the SWIR range (1000-2500 nm) of fern leaves, followed by the implementation of a classification model based on Partial Least Square Discriminant Analysis (PLS-DA). Following this procedure, false color maps, representative of the chemical elements distribution on the leaves were obtained, where As is clearly detected without performing any chemical analysis. The proposed approach is not invasive and not destructive. Comparative evaluations were carried out analyzing Pteris Vittata L. leaves collected from plants grown on natural soils containing different As concentrations. To evaluate reliability, robustness and analytical correctness of the proposed HSI approach, micro X-ray fluorescence (μXRF) analyses were carried out on the same samples in order to quantitatively and topologically assess As presence in the leaves of the plants. The achieved results are very promising for monitoring the phytoremediation process by detecting and controlling the uptake of As plants growing on contaminated soils.
The main purpose of this contribution is to report some first preliminary analyses of a new and never investigated decorative technique named Tattoo Wall, especially the possible changes due to ageing artificially induced by extreme humidity conditions in view of a possible application in crypts, churches or outside wall paintings. This innovative decorating technique involves transferring digital images on wall surfaces through a transfer paper with solvent-based ink and fixative. For the experimental tests, we chose to work on a color scale as wide as possible, to test each single color, and on different materials. The printed colors were applied on hydraulic mortar, containing marble powder combined with Ledan C30, particularly suitable for restoration in environments with high relative humidity (RH%). Moisture ageing was obtained by placing the sample in a box with RH% fixed to 92% thanks to the presence of salts (sodium sulphate deca-hydrated) for compressively two years (96 weeks). Reflectance spectrophotometry for color measurements and hyperspectral imaging (HSI) were used to assess the effect of high relative humidity exposure. The experimental data were statistically treated in order to evaluate their significance. Testing enabled us to verify the stability and durability of Tattoo Wall® under high relative humidity, with little chromatic alterations. Testing could and should be conducted also on different surfaces and materials (paintings on canvas and wood, oil on wall, etc.) to make it as complete as possible and guarantee the use of Tattoo Wall® in most cases of pictorial reintegration, reducing the risk of human error.
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.
The focus of this study was to investigate the potential of hyperspectral imaging (HSI) in the monitoring of commercial consolidant products applied on wood samples. Poplar (Populus spp.) and walnut (Juglans Regia L.) were chosen for the consolidant application. Both traditional and innovative products were selected, based on acrylic, epoxy, and aliphatic compounds. Wood samples were stressed by freeze/thaw cycles in order to cause material degradation without the loss of wood components. Then the consolidant was applied under vacuum. The samples were finally artificially aged for 168 h in a solar box chamber. The samples were acquired in the short wave infrared (1000 to 2500 nm) range by SISUChema XL™ device (Specim, Finland) after 168 h of irradiation. As comparison, color measurement was also used as an economic, simple, and noninvasive technique to evaluate the deterioration and consolidation effects on wood. All data were then processed adopting a chemometric approach finalized to define correlation models, HSI based, between consolidating materials, wood species, and short-time aging effects.
The focus of this study was addressed to investigate the potentiality of HyperSpectralImaging (HSI) in the monitoring of commercial consolidant products applied on wood samples. Poplar (Populus Sp.) and walnut (Juglans Regia L.) were chosen for the consolidant application. Both traditional and innovative products were selected, based on acrylic, epoxy and aliphatic compounds. Wood samples were stresses by freeze/thaw cycles in order to cause material degradation. Then the consolidants were applied under vacuum. The samples were finally artificially aged for 168 hours in a solar box chamber. The samples were acquired in the SWIR (1000-2500 nm) range by SISUChema XL™ device (Specim, Finland) after 168 hours of irradiation. As comparison, color measurement was also used as economic, simple and noninvasive technique to evaluate the deterioration and consolidation effects on wood. All data were then processed adopting a chemometric approach finalized to define correlation models, HSI based, between consolidating materials, wood species and short time ageing effects.
The use of Hyper-Spectral Imaging (HSI) as a diagnostic tool in the field of cultural heritage is of great interest
presenting high potentialities. This analysis, in fact, is non-destructive, non-invasive and portable. Furthermore, the
possibility to couple hyperspectral data with chemometric techniques allows getting qualitative and/or quantitative
information on the nature and physical-chemical characteristics of the investigated materials. A study was carried out to
explore the possibilities offered by this approach to identify pigments in paintings. More in detail, six pigments have
been selected and they have been then mixed with four different binders and applied to a wood support. The resulting
reference samples were acquired by HSI in the SWIR wavelength range (1000-2500 nm). Data were processed adopting
a chemometric approach based on the PLS Toolbox (Eigenvector Research, Inc.) running inside Matlab® (The
Mathworks, Inc.). The aim of the study was to verify, according to the information acquired in the investigated
wavelength region, the correlation existing between collected spectral signatures and sample characteristics related to the
different selected pigments and binders. Results were very good showing as correlations exist. New scenarios can thus be
envisaged for analysis, characterization, conservation and restoration of paintings, considering that the developed
approach allows to obtain, just “in one shot”, information, not only on the type of pigment, but also on the utilized binder
and support.
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