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The investigation of ancient artifacts is often constrained by their scarce availability and high protection and custody protocols. Among these, coinage represents an especially valuable kind-of-samples given their uniqueness and the subjacent information that is hidden behind their composition. Their analysis are often carried out using non-destructive techniques in order to avoid any alteration of the samples. In the field of Cultural Heritage analysis, smartphone-based methodologies have experienced a significant increase during the last few years, given their wide availability and ability to yield fast results. However, their analytical application demands a thorough and careful tuning during the methodology optimization. In this work, 21 historical gold and golden coins spanning a historical period of more than 2000 years have been analytically investigated. To that end, a two-fold approach has been implemented: first, the elemental composition has been analysed using portable X-ray fluorescence; and second, an innovative smartphone-based imaging method has been applied to measure their colour. Results allowed to describe the coins from their elemental profile, identifying some potentially debased ones, as well as some others not containing any gold. When possible, the results have been compared to previously reported cases, but our samples include some previously unreported cases representing new insights. All in all, this article provides new analytical data on unanalysed unique historical samples, in terms of their elemental profile and colorimetric properties, making use of an innovative, non-invasive nor destructive, fast and affordable colorimetric smartphone-based method to characterise historical coins.

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A prompt γ-ray neutron activation analysis system has recently been developed at China advanced research reactor (CARR), the 60 MW research reactor in China Institute of Atomic Energy (CIAE). The system is set at the cold neutron beam guide with a thermal equivalent neutron flux at the sample position of 1.0 × 109 n·cm-2·s-1 with the power of 30 MW, and it is mainly composed of a neutron beam collimator, a sample chamber, a beam stopper, neutron and γ-ray shieldings and a detection system. The detection system can realize three modes of measurement: single, Compton suppression, and pair modes. The detection efficiency was calibrated up to 11 MeV using a set of radionuclides and the (n, γ) reactions of N and Cl. Boron, one of the most important elements in high-temperature alloy material studies, was analyzed in this work, as the first pilot experiment of the CARR-PGNAA system. The analytical sensitivity of 2000 cps/mg-B was obtained. The results verified the feasibility of the CARR-PGNAA system to measure boron in high-temperature alloys, and laid a foundation for the accurate quantification of boron in the next step.

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BACKGROUND: Polyphenols have long been used to evaluate grape and wine quality and it is necessary to measure them throughout various winemaking stages. They are currently assessed predominantly through analytical methods, which are characterized by time-consuming procedures and environmentally harmful practices. Non-destructive spectroscopy-based devices offer an alternative but they tend to be costly and not readily accessible for smaller wineries. This study introduces the initial steps in employing a portable, user-friendly, and cost-effective visible (VIS) spectrophotometer prototype for direct polyphenol measurement during winemaking. RESULTS: Grapes (cv Syrah, Bobal, and Cabernet Sauvignon) at different maturation stages were fermented with or without stems. Throughout fermentation, parameters such as color intensity, total polyphenol index, total anthocyanins, and tannins were monitored. Concurrently, VIS spectra were acquired using both the prototype and a commercial instrument. Chemometric approaches were then applied to establish correlation models between spectra and destructive analyses. The prototype models demonstrated an acceptable level of confidence for only a few parameters, indicating their lack of complete reliability at this stage. CONCLUSIONS: Visible spectroscopy is already utilized for polyphenol analysis in winemaking but the aspiration to automate the process in wineries, particularly with low-cost devices, remains unrealized. This study investigates the feasibility of a low-cost and user-friendly spectrophotometer. The results indicate that, in the early stages of prototype utilization, the goal is attainable but requires further development and in-depth assessments. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

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Development of non-destructive analysis systems at the R&D beamline of the Proton Accelerator Facility of the Turkish Energy, Nuclear and Minerals Research Agency (TENMAK-PAF) is described in this work. The proton beam from the cyclotron with energy between 15-30 MeV and current between 0.1 µA-1.2 mA is modified with several components and subsystems to be suitable for the application of non-destructive analysis methods. Certified reference materials (CRM) and samples with different properties were irradiated under vacuum in the irradiation chamber with the new system. Particle Induced X-ray Emission (PIXE) and Particle Induced Gamma ray Emission (PIGE) spectra of these samples were obtained with appropriate radiation detectors and measurement systems. The results of PIXE analyses and X-ray fluorescence (XRF) spectrometry were compared. PIXE and PIGE results are in good agreement with the certified values for the CRMs. This work constitutes the first reported application of non-destructive ion beam analysis methods in TENMAK-PAF and in Türkiye.

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The Fresnel lens is an optical system consisting of a series of concentric diamond grooves. One surface of the lens is smooth, while the other is engraved with concentric circles of increasing size. Optical interference, diffraction, and sensitivity to the angle of incidence are used to design the microstructure on the lens surface. The imaging of the optical surface depends on its curvature. By reducing the thickness of the lens, light can still be focused at the same focal point as with a thicker lens. Previously, lenses, including Fresnel lenses, were made of glass due to material limitations. However, the traditional grinding and polishing methods for making Fresnel lenses were not only time-consuming, but also labour-intensive. As a result, costs were high. Later, a thermal pressing process using metal moulds was invented. However, the high surface tension of glass caused some detailed parts to be deformed during the pressing process, resulting in unsatisfactory Fresnel lens performance. In addition, the complex manufacturing process and unstable processing accuracy hindered mass production. This resulted in high prices and limited applications for Fresnel lenses. These factors prevented the widespread use of early Fresnel lenses. In contrast, polymer materials offer advantages, such as low density, light weight, high strength-to-weight ratios, and corrosion resistance. They are also cost effective and available in a wide range of grades. Polymer materials have gradually replaced optical glass and other materials in the manufacture of micro-optical lenses and other miniaturised devices. Therefore, this study focuses on investigating the manufacturing parameters of Fresnel lenses in the injection moulding process. We compare the quality of products obtained by two-stage injection moulding, injection compression moulding, and IMD (in-mould decoration) techniques. The results show that the optimal method is IMD, which reduces the nodal displacement on the Fresnel lens surface and improves the transmission performance. To achieve this, we first establish a Kriging model to correlate the process parameters with optimisation objectives, mapping the design parameters and optimisation objectives. Based on the Kriging model, we integrate the NSGA-II algorithm with the predictive model to obtain the Pareto optimal solutions. By analysing the Pareto frontier, we identify the best process parameters. Finally, it is determined that the average nodal displacement on the Fresnel surface is 0.393 mm, at a holding pressure of 320.35 MPa and a melt temperature of 251.40 °C. Combined with IMD technology, product testing shows a transmittance of 95.43% and an optimisation rate of 59.64%.

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Coconut water (CW) is a popular and healthful beverage, and ensuring its quality is crucial for consumer satisfaction. This study aimed to explore the potential of near-infrared spectroscopy (NIRS) and chemometric methods for analyzing CW quality and distinguishing samples based on postharvest storage time, cultivar, and maturity. CW from nuts of Wenye No. 2 and Wenye No. 4 cultivars in China, with varying postharvest storage time and maturities, were subjected to NIRS analysis. Partial least squares regression (PLSR) models were developed to predict reducing sugar and soluble sugar contents, revealing moderate applicability but lacking accuracy, with the residual prediction deviation (RPD) values ranging from 1.54 to 1.83. Models for TSS, pH, and TSS/pH exhibited poor performance with RPD values below 1.4, indicating limited predictability. However, the study achieved a total correct classification rate exceeding 95% through orthogonal partial least squares discriminant analysis (OPLS-DA) models, effectively discriminating CW samples based on postharvest storage time, cultivar, and maturity. These findings highlight the potential of NIRS combined with appropriate chemometric methods as a valuable tool for analyzing CW quality and efficiently distinguishing samples. NIRS and chemometric techniques enhance quality control in coconut water, ensuring consumer satisfaction and product integrity.

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Recently, researchers are conducting studies to improve the mechanical and chemical properties of cementitious composites mixed with nanomaterials. Defects may occur inside nano-cementitious composites due to nanomaterial agglomeration in the manufacturing process. These defects can degrade the mechanical performance of the nano-cementitious composite. This study performs ultrasonic non-destructive and compressive strength tests according to the size of defects in nano-cementitious composites. Multi-walled carbon nanotubes (MWCNTs) were used for the nanomaterial, and internal defects of various sizes were considered in the center of the specimens. Ultrasonic pulse velocity was measured according to the defect size until 30 curing days, after which the compressive strength was measured. The ultrasonic pulse velocity of the nano-cementitious composites decreased by up to 9.6% in relation to that of the specimens without defects as the defect size increased, and the compressive strength decreased by up to 35.7%. This study's findings revealed a correlation between ultrasonic pulse velocity and compressive strength according to defect size. Future ultrasonic non-destructive tests will allow for the prediction of mechanical performance and the detection of defects within nano-cementitious composites.

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Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

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Portable energy-dispersive X-ray fluorescence spectroscopy (pXRF) was employed in the characterization of the pigments present in the painting "Still Life with Vase, Plate and Flowers", of unknown authorship, but previously attributed to Vincent van Gogh, belonging to the collection of the São Paulo Museum of Art (MASP). In situ pXRF measurements were carried out by means of a portable system in order to provide the museum with a scientific documentation about the materials in the painting. Spectra were taken at different color regions and different hues in the pictorial layer. Some of the materials identified in the painting were chalk and/or gypsum, lithopone, lead white, zinc white, bone black, barium yellow, chrome yellow, yellow ochre, chrome green, Prussian blue, cobalt blue, vermilion and red earth. Besides that, it was possible to suggest the use of a lake pigment. The pigments whose use is suggested by this work are in total agreement with those available to European artists in the end of the 19th century.

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This article tests the suitability of a new method to monitor the degree of substitution of cellulose acetate films, by employing a compact and inexpensive near-infrared miniaturized spectrometer (908.1-1676.2 nm) that can be easily applied in situ. The present study compares the analytical performance of the proposed method against conventional diagnostic strategies based on benchtop micro-attenuated total reflectance Fourier transform Infrared (µATR -FTIR) measurements in the mid-infrared spectral range. The novel calibration function exploits the shifts in the first overtone of the hydroxyl stretching 2νOH band of probe materials and was created using a set of analytical standards with different degrees of substitution. The robustness of the method was assessed by application on a group of sixteen historical cinematographic films. The accurate condition assessment of these films was performed in situ, in a non-invasive manner. The proposed analytical procedure is quick and easy-to-implement, and therefore it constitutes a rapid method to guide conservation strategies regarding film storage and digitalization in cultural institutions, including museums and cinematheques. Potential applications on three-dimensional objects and industrial processes are possible.

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A novel Compton suppression device has been developed at the Radiation and Nuclear Safety Authority of Finland to improve the sensitivity of measurements in the Gamma Laboratory. It utilizes γ-γ anticoincidence, but operates as a full coincidence system. Specific software has been developed for the sorting, visualization and analysis of list mode data produced by the multi-detector list-mode devices in the Gamma Laboratory. By utilizing the software, the coincidence data can be accessed and the true-coincidence losses of photo-peaks of multiple gamma-ray-emitting nuclides restored in the analysis. This simplifies data analysis and further increases the sensitivity of the device in low count-rate gamma spectrometry. A detailed Geant4 simulation model of the device was developed and used to optimize the device as well as to support calibrations and complex analysis tasks. The setup has been integrated to current laboratory information management system used in the Gamma Laboratory. Compton suppression reduces the continuous background seen by the high-purity germanium detector by a factor of 3-10, in addition to comparable reduction in the Compton continuum of any peaks in the spectrum. A comparison with the results of conventional gamma-ray spectrometry is presented.

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Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R2) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R2 between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R2) of calibration and cross-validation of 0.9577 and 0.9450, respectively.

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Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750-900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.

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A commercial high-resolution X-ray computed tomography (CT) was utilized for non-destructive analysis of single fibers. The micro-CT apparatus was employed because it is applicable to both colored and colorless fibers. A sample preparation using adhesive sheets was demonstrated, and the method is similar to typical tape-lift sample collection method in crime cases. Different cross-sectional shapes of nylon and polyester single fibers were non-destructively distinguished, and the method is applicable to all types of fibers. Cross-sectional areas, aperture ratios, and volumes of individual fibers were directly and automatically measured using the open-source software. The observed parameters were within a coefficient of variation of 3%. In addition, a mass of a single fragment of a fiber can be estimated when the local density is given.

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Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (aw ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.

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Mechanical properties such as the Young modulus, shear modulus and Poisson's coefficient are very important to define different materials applications, for basic research and for quality control procedures. Impulse excitation technique (IET) is a non-destructive, easy and fast method for characterization of elastic and acoustic properties of materials. The technique consists in sending a mechanical impulse in a sample and measuring the output sound wave. Commercial instruments are widely spread in metal industry, but they are not diffused in academic research centres. Such instruments can be easily self-built at low cost, allowing a much wider diffusion and exploitation in many fields involving materials characterization, since they guarantee high precision and high data reproducibility. For a proper acoustic characterization, necessary to obtain reliable mechanical data, a calibration of the instrument must be performed, for a proper association of the acoustic response to the features of each specific material. In this data article, a data set of impulses, collected on different materials by a self-built instrument for IET, named IETeasy, is provided for mechanical properties characterization by a self-built IET tool, and multivariate statistical analysis purposes. The aim is double in the short term: on one hand, providing a verified data set useful to develop, test and verify methods of analysis and tailor the IETeasy instrument on the needs of each specific user; on the other hand, giving a benchmark for any one designing, building and testing his IET home-made instrument. In the long term, since the data base is open, any contribution consisting in data collected by similar self-made or commercial instruments can be added to the data base, with the aim of building a large collection of data, useful for automatic recognition of sound outputs by machine learning or other multivariate or monovariate data analysis approaches, and for instrument performance comparison and alignment.

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The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.

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The honey bee (Apis mellifera L. 1778) is an essential element in maintaining the diversity of the biosphere and food production. One of its most important parasites is Varroa destructor, Anderson and Trueman, 2000, which plays a role in the vectoring of deformed wing virus (DWV) in honey bee colonies. Our aim was to measure the potential morphometric changes in the pre-imaginal stage of A. mellifera caused by varroosis by means of computed tomography, hence supplying evidence for the presumable role that V. destructor plays as a virus vector. Based on our results, the developmental disorders in honey bees that ensued during the pre-imaginal stages were evident. The total-body length and abdomen length of parasitized specimens were shorter than those of their intact companions. In addition, the calculated quotients of the total-body/abdomen, head/thorax, and head/abdomen in parasitized samples were significantly altered upon infestation. In our view, these phenotypical disorders can also be traced to viral infection mediated by parasitism, which was confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) analysis. Capitalizing on a non-destructive method, our study reveals the deformation of the honey bee due to mite parasitism and the intermediary role this pest plays in viral infection, inside the brood cell.

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A non-destructive discrimination method for crystals in solid dosage drug forms was first developed using a combination of Raman spectroscopy and X-ray micro-computed tomography (X-ray CT). Identification of the crystal form of an active pharmaceutical ingredient (API) at the appropriate pharmaceutical dosage is crucial, as the crystal form is a determinant of the quality and performance of the final formulation. To develop a non-destructive analytical methodology for the discrimination of solid API crystals in a solid dosage form, we utilized a combination of Raman spectroscopy and X-ray CT to differentiate between ranitidine crystal polymorphs (forms 1 and 2) in tablet formulations containing three excipients. The difference in electron density correlated with the true density between ranitidine polymorphs, thereby enabling the discrimination of crystal forms and visualization of their three-dimensional spatial localization inside the tablets through X-ray CT imaging. Furthermore, X-ray CT imaging revealed that the crystal particles were of varying densities, sizes, and shapes within the same batch. These findings suggest that X-ray CT is not only an imaging tool but also a unique method for quantitative physicochemical characterization to study crystal polymorphs and solid dosage forms.

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