Development of a Powder Analysis Procedure Based on Imaging Techniques for Examining Aggregation and Segregation Phenomena.

In this manuscript, a method that utilizes classical image techniques to assess particle aggregation and segregation, with the primary goal of validating particle size distribution determined by conventional methods, is presented. This approach can represent a supplementary tool in quality control systems for powder production processes in industries such as manufacturing and pharmaceuticals. The methodology involves the acquisition of high-resolution images, followed by their fractal and textural analysis. Fractal analysis plays a crucial role by quantitatively measuring the complexity and self-similarity of particle structures. This approach allows for the numerical evaluation of aggregation and segregation phenomena, providing valuable insights into the underlying mechanisms at play. Textural analysis contributes to the characterization of patterns and spatial correlations observed in particle images. The examination of textural features offers an additional understanding of particle arrangement and organization. Consequently, it aids in validating the accuracy of particle size distribution measurements. To this end, by incorporating fractal and structural analysis, a methodology that enhances the reliability and accuracy of particle size distribution validation is obtained. It enables the identification of irregularities, anomalies, and subtle variations in particle arrangements that might not be detected by traditional measurement techniques alone.

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.

Near-Infrared Transflectance Spectroscopy Discriminates Solutions Containing Two Commercial Formulations of Botulinum Toxin Type A Diluted at Recommended Volumes for Clinical Reconstitution.

Botulinum neurotoxin type A (BoNT-A) is the active substance in pharmaceutical preparations widely used worldwide for the highly effective treatment of various disorders. Among the three commercial formulations of BoNT-A currently available in Italy for neurological indications, abobotulinum A toxin (Dysport®, Ipsen SpA, Milano, Italy) and incobotulinum A toxin (Xeomin®, Merz Pharma Italia srl, Milano, Italy) differ in the content of neurotoxin, non-toxic protein, and excipients. Clinical applications of BoNT-A adopt extremely diluted solutions (10-6 mg/mL) for injection in the target body district. Near-infrared spectroscopy (NIRS) and chemometrics allow rapid, non-invasive, and non-destructive methods for qualitative and quantitative analysis. No data are available to date on the chemometric analysis of the spectral fingerprints acquired from the diluted commercial formulations of BoNT-A. In this proof-of-concept study, we tested whether NIRS can categorize solutions of incobotulinum A toxin (lacking non-toxic proteins) and abobotulinum A toxin (containing non-toxic proteins). Distinct excipients in the two formulations were also analyzed. We acquired transmittance spectra in the visible and short-wave infrared regions (350-2500 nm) by an ASD FieldSpec 4™ Standard-Res Spectrophotoradiometer, using a submerged dip probe designed to read spectra in transflectance mode from liquid samples. After preliminary spectra pre-processing, principal component analysis was applied to characterize the spectral features of the two BoNT-A solutions and those of the various excipients diluted according to clinical standards. Partial least squares-discriminant analysis was used to implement a classification model able to discriminate the BoNT-A solutions and excipients. NIRS distinguished solutions containing distinct BoNT-A commercial formulations (abobotulinum A toxin vs. incobotulinum A toxin) diluted at recommended volumes for clinical reconstitution, distinct proteins (HSA vs. incobotulinum A toxin), very diluted solutions of simple sugars (lactose vs. sucrose), and saline or water. Predictive models of botulinum toxin formulations were also performed with the highest precision and accuracy.

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.

Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection.

In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level.

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

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.

In vivo non-invasive near-infrared spectroscopy distinguishes normal, post-stroke, and botulinum toxin treated human muscles.

In post-stroke hemiparesis, neural impairment alters muscle control, causing abnormal movement and posture in the affected limbs. A decrease in voluntary use of the paretic arm and flexed posture during rest also induce secondary tissue transformation in the upper limb muscles. To obtain a specific, accurate, and reproducible marker of the current biological status of muscles, we collected visible (VIS) and short-wave Infrared (SWIR) reflectance spectra in vivo using a portable spectroradiometer (350-2500 nm), which provided the spectral fingerprints of the elbow flexors and extensors. We compared the spectra for the affected and unaffected sides in 23 patients with post-stroke hemiparesis (25-87 years, 8 women) and eight healthy controls (33-87 years, 5 women). In eight patients, spectra were collected before and after botulinum toxin injection. Spectra underwent off-line preprocessing, principal component analysis, and partial least-squares discriminant analysis. Spectral fingerprints discriminated the muscle (biceps vs. triceps), neurological condition (normal vs. affected vs. unaffected), and effect of botulinum toxin treatment (before vs. 30 to 40 days vs. 110 to 120 days after injection). VIS-SWIR spectroscopy proved valuable for non-invasive assessment of optical properties in muscles, enabled more comprehensive evaluation of hemiparetic muscles, and provided optimal monitoring of the effectiveness of medication.

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.

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.

Near-infrared spectroscopy as a tool for in vivo analysis of human muscles.

Recent advances in materials and fabrication techniques provided portable, performant, sensing optical spectrometers readily operated by user-friendly cabled or wireless systems. Such systems allow rapid, non-invasive, and not destructive quantitative analysis of human tissues. This proof-of-principle investigation tested whether infrared spectroscopy techniques, currently utilized in a variety of areas, could be applied in living humans to categorize muscles. Using an ASD FieldSpec® 4 Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350-1000 nm and 1.1 nm at 1001-2500 nm, we acquired reflectance spectra in visible short-wave infra-red regions (350-2500 nm) from the upper limb muscles (flexors and extensors) of 20 healthy subjects (age 25-89 years, 9 women). Spectra off-line analysis included preliminary preprocessing, Principal Component Analysis, and Partial Least-Squares Discriminant Analysis. Near-infrared (NIR) spectroscopy proved valuable for noninvasive assessment of tissue optical properties in vivo. In addition to the non-invasive detection of tissue oxygenation, NIR spectroscopy provided the spectral signatures (ie, "fingerprints") of upper limb flexors and extensors, which represent specific, accurate, and reproducible measures of the overall biological status of these muscles. Thus, non-invasive NIR spectroscopy enables more thorough evaluation of the muscular system and optimal monitoring of the effectiveness of therapeutic or rehabilitative interventions.

Hyperspectral Imaging as Powerful Technique for Investigating the Stability of Painting Samples.

The aim of this work is to present the utilization of Hyperspectral Imaging for studying the stability of painting samples to simulated solar radiation, in order to evaluate their use in the restoration field. In particular, ready-to-use commercial watercolours and powder pigments were tested, with these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Samples were investigated through Hyperspectral Imaging in the short wave infrared range before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the Hyperspectral Imaging technique to identify the variations on paint layers, induced by photo-degradation, before they could be detected by eye. Furthermore, a supervised classification method for monitoring the painted surface changes, adopting a multivariate approach was successfully applied.

A methodological approach to study the stability of selected watercolours for painting reintegration, through reflectance spectrophotometry, Fourier transform infrared spectroscopy and hyperspectral imaging.

The aim of this work is to investigate the stability to simulated solar radiation of some paintings samples through a new methodological approach adopting non-invasive spectroscopic techniques. In particular, commercial watercolours and iron oxide based pigments were used, these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Reflectance spectrophotometry in the visible range and Hyperspectral Imaging in the short wave infrared were chosen as non-invasive techniques for evaluation the stability to irradiation of the chosen pigments. These were studied before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the chosen techniques in identifying the variations on paint layers, induced by photo-degradation, before they could be observed by eye. Furthermore a supervised classification method for monitoring the painted surface changes adopting a multivariate approach was successfully applied.

A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging.

The aim of this work was to recognize different polymer flakes from mixed plastic waste through an innovative hierarchical classification strategy based on hyperspectral imaging, with particular reference to low density polyethylene (LDPE) and high-density polyethylene (HDPE). A plastic waste composition assessment, including also LDPE and HDPE identification, may help to define optimal recycling strategies for product quality control. Correct handling of plastic waste is essential for its further "sustainable" recovery, maximizing the sorting performance in particular for plastics with similar characteristics as LDPE and HDPE. Five different plastic waste samples were chosen for the investigation: polypropylene (PP), LDPE, HDPE, polystyrene (PS) and polyvinyl chloride (PVC). A calibration dataset was realized utilizing the corresponding virgin polymers. Hyperspectral imaging in the short-wave infrared range (1000-2500nm) was thus applied to evaluate the different plastic spectral attributes finalized to perform their recognition/classification. After exploring polymer spectral differences by principal component analysis (PCA), a hierarchical partial least squares discriminant analysis (PLS-DA) model was built allowing the five different polymers to be recognized. The proposed methodology, based on hierarchical classification, is very powerful and fast, allowing to recognize the five different polymers in a single step.

Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging.

An innovative approach, based on HyperSpectral Imaging (HSI), was developed in order to set up an efficient method to analyze marine microplastic litter. HSI was applied to samples collected by surface-trawling plankton nets from several parts of the world (i.e. Arctic, Mediterranean, South Atlantic and North Pacific). Reliable information on abundance, size, shape and polymer type for the whole ensemble of plastic particles in each sample was retrieved from single hyperspectral images. The simultaneous characterization of the polymeric composition of the plastic debris represents an important analytical advantage considering that this information, and even the validation of the plastic nature of the small debris, is a common flaw in the analysis of marine microplastic pollution. HSI was revealed as a rapid, non-invasive, non-destructive and reliable technology for the characterization of the microplastic waste, opening a promising way for improving the plastic pollution monitoring.

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.