Non-destructive Analytical Tool Research Articles

Rapid prediction of nucleosides content and origin traceability of Boletus bainiugan using Fourier transform near-infrared spectroscopy combined with chemometrics

Boletus bainiugan has high nutritional and economic values. As one of the potential medicinal active ingredients, nucleosides have important research significance. Porcini mushrooms fraud is frequently detected on the market, including substitute inferior into superior and lack of geographical origin’s certification. This behavior results in economic loss and health damage to consumers. Fourier transform near-infrared (FT-NIR) spectroscopy is a fast, efficient and reliable analytical tool. In the present study, the effect of source environment (climatic factors) on nucleoside content is analyzed for the first time. Then, the FT-NIR spectroscopy to study the origin traceability and content prediction of Boletus bainiugan are utilized. The results indicate that the nucleoside content is associated with precipitation and temperature. The combination of synchronous two-dimensional correlation spectroscopy (2DCOS) with residual neural networks (ResNet) model obtains the precise identification of the origin of Boletus bainiugan, with an accuracy of 100%. In the prediction models of content for uridine, guanosine, and adenosine, the optimal coefficient of determination of predictive set (R2P) is 0.901, and the optimum residual prediction deviation (RPD) is 3.178. FT-NIR spectroscopy has proven to be an environmentally friendly and non-destructive analytical tool for accurate origin traceability and content prediction.

Glycogen Quantification and Gender Identification in Di-, Tri-, and Tetraploid Crassostrea gigas Using Portable Near-Infrared Spectroscopy.

Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking the first instance of developing quantitative models for glycogen in tetraploid C. gigas. The research also provides a comparative analysis with models for diploid and triploid oysters, underscoring the innovative use of portable NIR technology in aquaculture. Two portable NIR spectrometers were employed: the Micro NIR 1700 (908-1676 nm) and the Micro PHAZIR RX (1624-2460 nm). Near-infrared spectra were acquired from the gonadal tissues of diploid, triploid, and tetraploid C. gigas. Quantitative models for glycogen content were developed and validated using cross-validation methods. Additionally, qualitative models for different ploidies and genders were established. For the Micro NIR 1700, the cross-validation correlation coefficients (Rcv) and cross-validation relative predictive errors (RPDcv) for glycogen were 0.949 and 3.191 for diploids, 0.915 and 2.498 for triploids, and 0.902 and 2.310 for tetraploids. The Micro PHAZIR RX achieved Rcv and RPDcv values of 0.781 and 2.240 for diploids, 0.839 and 2.504 for triploids, and 0.717 and 1.851 for tetraploids. The Micro NIR 1700 demonstrated superior quantitative performance, with RPD values exceeding 2, indicating its effectiveness in predicting glycogen content across different ploidy levels. Qualitative models showed a performance index of 91.6 for diploid and 95 for tetraploid genders using the Micro NIR 1700, while the Micro PHAZIR RX achieved correct identification rates of 99.79% and 100% for diploid and tetraploid genders, respectively. However, differentiation of ploidies was less successful with both instruments. This study's originality lies in establishing the first quantitative models for glycogen content in tetraploid C. gigas using portable NIR spectrometers, highlighting the significant advancements in non-destructive glycogen analysis. The applicability of these findings is substantial for oyster breeding programs focused on enhancing meat quality traits. These models provide a valuable phenotyping tool for selecting oysters with optimal glycogen content, demonstrating the practical utility of portable NIR technology in aquaculture.

Testing non-destructive spectrometric methods for the identification and distinction of archaeological pine wood tar and birch bark tar

Archaeological findings prove the appearance and use of birch bark tar since the Middle Palaeolithic. The production and use of birch bark tar and pine wood tar has overlapped since at least the late Neolithic, but probably for much longer. The reliable chemical identification of such archaeological tar residues can offer valuable insights into, for example, ancient technical complexity, trade and culture. In this context, the scarcity of these mainly organic residue findings in the archaeological record bears the need for non-destructive analytical tools. However, there is currently no systematic proposed way for this purpose. We aim here to verify the organic nature and test the reliability of the identification of archaeological pine wood tar and birch bark tar with a combination of SEM-EDS, FTIR microspectroscopy in reflectance mode and XRD. We examined a set of experimental adhesive replicas of pine tar and birch tar in pristine form, but also after a three-year-long weathering experiment. Additionally, we studied a set of archaeological samples, consisting of Mesolithic bone/antler points with adhering hafting residues, form the Dutch North Sea. This research shows that degradation negatively influences the reliable verification and identification of the organic residue constituents significantly. SEM-EDS as a starting point of analysis verifies the residue’s organic nature, but it cannot be used to identify birch or pine tar. XRD can identify crystalline additives in the adhesive mixture, like ochre and wax, as well as phases related to the artefact’s environment of burial and provenance. Micro-FTIR is also capable of verifying the organic matter of the residue constituents. The differentiation of birch and pine tars is hindered by vibrational modes occurring in neighbouring wavenumbers for both tars, and by the limited research on degradation markers indicative of thermal treatment to prove tar production. Until reference collections also account for degradation and include a wide variety of adhesives, results of FTIR collected in reflectance mode are best treated with some caution.

Impedance-based in vitro eye irritation testing enables the categorization of diluted chemicals

Products containing chemicals with eye irritation potential need to be labeled with the respective hazard symbol. To avoid the testing of numerous dilutions of chemicals on animals, their labeling is directed by a theoretical approach. In this report, a previously described in vitro tissue model of the cornea based on human epithelial cells was used for eye irritation testing of dilutions. As a sensitive and non-destructive method to analyze the barrier function of the epithelium, impedance spectroscopy was applied. Moreover, the morphology and viability of the epithelial models were assessed. We tested four chemicals that, neatly, cause severe damage to the eye: tetrahydrofuran, acetic acid, diethylethanolamine, and benzalkonium chloride. With our test method, we were able to determine the concentrations of the chemicals which are critical for the integrity of the cornea. The threshold was < 0.1% for the most and > 5% for the least toxic substance. The described test system is not only an alternative for animal models but also for the theoretical examination of the hazard potential of diluted chemicals. By using the advantages of tissue engineering and non-destructive analysis tools, we can achieve more precise and safer labeling of the eye irritation potential of products.

A novel broadband near-infrared phosphor Ca3MgTiGe3O12:Cr3+

Near-infrared spectroscopy (NIRS) is a rapid, non-destructive analytical characterization tool. In this study, we report near-infrared (NIR) phosphor Ca3MgTiGe3O12:Cr3+ in garnet system, achieving ultra-broadband NIR emission. The emission spectrum of the phosphor ranges from 690 nm–1100 nm with a full width of half maximum (FWHM) value of ∼127 nm. The emission peak is observed at 786 nm when the excitation wavelength is 463 nm. The broadband emission seen in this study was identified as a result of 4T2 → 4A2 transition of Cr3+ in host lattice. The internal quantum efficiency (IQE) of the material was found to be as high as 66.5 %. The energy transfer mechanism, quantum yield, thermal stability and preparation conditions of the material were further investigated. These studies show that these materials with highly-efficient ultra-brosd near-infrared emissions are promising candidates for high-quality near-infrared light sources.

Effects of wet granulation process variables on the quantitative assay model of transmission Raman spectroscopy for pharmaceutical tablets

Transmission Raman spectroscopy (TRS) is a process analytical technology tool for nondestructive analysis of drug content in tablets. Although wet granulation is the most used tablet manufacturing method, most TRS studies have focused on tablets manufactured via direct compression. The effects of upstream process parameter variations, such as granulation, on the prediction performance of TRS quantitative models are unknown. We evaluated the effects of process parameter variations during granulation on the prediction performance of the TRS quantitative model. Tablets with a drug concentration of 1%w/w were used. We developed PLS calibration models for the drug concentration range of 70–130% label claims. Subsequently, we predicted the drug content of the tablets with different granulation parameters. The results of our study demonstrate that the variation in the predicted recovery due to the variation in granulation parameters was practically acceptable. The calibration model showed a good prediction performance for tablets manufactured at different granulation scales and thicknesses. Therefore, we conclude that TRS quantitative models are robust to variations in upstream processes, such as granulation and downstream variations in tableting parameters. These results suggest that TRS is a versatile non-destructive quantitative analysis method that can be applied in tablet manufacturing.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Distinct Lipid Phenotype of Cancer-Associated Fibroblasts (CAFs) Isolated From Overweight/Obese Endometrial Cancer Patients as Assessed Using Raman Spectroscopy.

Obesity is strongly linked with increased risk and poorer prognosis of endometrial cancer (EC). Cancer-associated fibroblasts (CAFs) are activated fibroblasts that form a large component of the tumor microenvironment and undergo metabolic reprogramming to provide critical metabolites for tumor growth. However, it is still unknown how obesity, characterized by a surplus of free fatty acids drives the modifications of CAFs lipid metabolism which may provide the mechanistic link between obesity and EC progression. The present study aims to evaluate the utility of Raman spectroscopy, an emerging nondestructive analytical tool to detect signature changes in lipid metabolites of CAFs from EC patients with varying body mass index. We established primary cultures of fibroblasts from human EC tissues, and CAFs of overweight/obese and nonobese women using antibody-conjugated magnetic beads isolation. These homogeneous fibroblast cultures expressed fibroblast markers, including α-smooth muscle actin and vimentin. Analysis was made in the Raman spectra region best associated with cancer progression biochemical changes in lipids (600-1800 cm-1 and 2800-3200 cm-1). Direct band analysis and ratiometric analysis were conducted to extract information from the Raman spectrum. Present results demonstrated minor shifts in the CH2 symmetric stretch of lipids at 2879 cm-1 and CH3 asymmetric stretching from protein at 2932 cm-1 in the overweight/obese CAFS compared to nonobese CAFs, indicating increased lipid content and a higher degree of lipid saturation. Principal component analysis showed that CAFs from overweight/obese and nonobese EC patients can be clearly distinguished indicating the capability of Raman spectroscopy to detect changes in biochemical components. Our results suggest Raman spectroscopy supported by chemometric analysis is a reliable technique for characterizing metabolic changes in clinical samples, providing an insight into obesity-driven alteration in CAFs, a critical stromal component during EC tumorigenesis.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review.

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Urea-Formaldehyde Resin Removal in Medium-Density Fiberboards by Steam Explosion: Developing Nondestructive Analytical Tools

The cascading recycling of wood waste is currently a hot topic. Medium-density fiberboard (MDF) is a material whose consumption has been increasing worldwide for several decades. However, current wood waste treatment processes do not allow for efficient recycling. The potential of steam explosion (SE) treatment was investigated for stripping off the urea-formaldehyde (UF) resin and producing clean fibers from MDF. Hot liquid water during the hydrothermal stage and water expansion during the decompression stage of SE provoked the (1) hydrolytic breakdown of UF and removal of >80% of resin from the fibers and (2) efficient defibration of MDF producing ≈67% of fibers with similar morphological features when compared with MDF fibers. Near infrared spectroscopy (NIRS), nitrogen quantification, and confocal laser scanning microscopy/scanning electron microscopy-wavelength dispersive spectroscopy were used to evaluate the SE effect on UF removal from MDF fibers. The sample clusters obtained by principal component analysis of NIRS spectra were correlated with the nitrogen content and processing conditions used. This work brings versatile solutions based on hydrothermal treatments, namely SE, for diverse low environmental impact and valorization routes of MDF waste. We showed that MDF processing treatment must be chosen wisely according to the future valorization of the MDF targeted.

Is the damage worth it? Testing handheld XRF as a non-destructive analytical tool for determining biogenic bone and tooth chemistry prior to destructive analyses

Isotopic analysis is destructive and requires that a specimen retains its original (biogenic) chemical composition. A specimen's relative abundance of calcium and phosphorous (Ca/P) or carbonate and phosphate (CO3/PO4) is often used to assess preservation. If a specimen's Ca/P or CO3/PO4 is similar to modern specimens, a specimen's isotopic composition may be biogenic. However, most methods for measuring these proxies are destructive. Moreover the relationships between Ca/P, CO3/PO4 and isotopic preservation are poorly established. In this study, we assessed the ability of handheld X-ray fluorescence (hXRF) to non-destructively evaluate a specimen's preservation by characterizing the calcium to phosphorous ratio (Ca/P). We first established that surface Ca/P (Ca/Psurface) for modern specimens was consistent with expectations for unaltered bone (1.3–2.3). Several specimens had slightly larger ratios, suggesting the currently accepted range may need to be expanded. Second, we tested the ability of Ca/Psurface to detect alteration using twenty Quaternary mammal teeth from Big Bone Lick, Kentucky. Ten specimens had Ca/Psurface between 1.3 and 2.3 and ten had larger ratios, suggesting alteration. Because most methods measure Ca/P in powder (Ca/Ppowder), we compared Ca/Psurface, Ca/Ppowder, and the enamel subsurface (Ca/Psubsurface). With two exceptions, Ca/Psubsurface and Ca/Ppowder were below 2.3, regardless of Ca/Psurface, suggesting that Ca/Ppowder and Ca/Psubsurface underestimate alteration. We next compared Ca/Psurface, CO3/PO4, and carbon (δ13C) and oxygen (δ18O) isotope values for the fossil teeth. Fourteen specimens were identified as altered or unaltered by both proxies, but six specimens only had one altered proxy. Specimens with both proxies altered had lower, less variable δ13C values than specimens with both proxies unaltered. Median δ18O values were similar between these groups. Individuals with altered Ca/Psurface but unaltered CO3/PO4 isotopically resembled specimens with both proxies altered. Conversely, specimens with unaltered Ca/Psurface and altered CO3/PO4 were similar to specimens with both proxies unaltered. Notably, all individuals with both proxies altered had relatively low δ13C values, including a horse and mammoth, which are normally considered grazers (and therefore should have higher δ13C values). These and other altered specimens may be isotopically compromised. Overall, our results suggest that Ca/Psurface is effective at detecting alteration non-destructively, quickly, and affordably, making it an attractive approach for analyzing unique specimens.

Spatially Offset Raman Spectroscopy for Characterization of a Solid-State System

Solid-state batteries represent a promising technology in the field of high-energy-density and safe storage systems. Improving the understanding of how defects form within these cells would greatly facilitate future development, which would be best served by applying nondestructive analytical tools capable of characterization of the key components and their changes during cycling and/or aging. Spatially offset Raman spectroscopy (SORS) represents a potentially useful technique, but currently there is a lack of knowledge regarding its use in this field. To fill this gap, we present an investigation into the use of simple defocused micro-SORS on systems constructed using typical components found within solid-state cells. By analyzing the constituents and the assembled system, it was possible to obtain depth profiling spectra and show that spectra may be obtained from layers which are normally obscured, demonstrating the technique’s potential for nondestructive chemical analysis of the subsurface. In this way, the results presented validate the potential of micro-SORS as a technique to develop to support future solid-state battery development, as well as the nondestructive battery analytical field.

An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions.

The geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO categorization. Compared to the Douro region, oils from Côa had higher total phenol contents (505 versus 279 mg GAE/kg) and greater oxidative stabilities (17.5 versus 10.6 h). The majority of Côa oils were fruity-green, bitter, and pungent oils. Conversely, Douro oils exhibited a more intense fruity-ripe and sweet sensation. Accordingly, different volatiles were detected, belonging to eight chemical families, from which aldehydes were the most abundant. Additionally, all oils were evaluated using a lab-made electronic nose, with metal oxide semiconductor sensors. The electrical fingerprints, together with principal component analysis, enabled the unsupervised recognition of the oils' geographical origin, and their successful supervised linear discrimination (sensitivity of 98.5% and specificity of 98.4%; internal validation). The E-nose also quantified the contents of the two main volatile chemical classes (alcohols and aldehydes) and of the total volatiles content, for the studied olive oils split by geographical origin, using multivariate linear regression models (0.981 ≤ R2 ≤ 0.998 and 0.40 ≤ RMSE ≤ 2.79 mg/kg oil; internal validation). The E-nose-MOS was shown to be a fast, green, non-invasive and cost-effective tool for authenticating the geographical origin of the studied olive oils and to estimate the contents of the most abundant chemical classes of volatiles.

Analysis of volatile odor compounds and aroma properties of European vinegar by the ultra-fast gas chromatographic electronic nose

Vinegar is a very popular condiment with different species and aroma profiles. Eighty-three volatile odor compounds (VOCs) were identified from European vinegar using ultra-fast gas chromatographic electronic nose (UFGC E-nose), of which there were 61 VOCs with odor activity value (OAV) ≥ 1. Among the 61 VOCs, acetic acid, ethyl isovalerate, propionaldehyde, butanoic acid, ethyl cinnamate, and guaiacol contributed significantly to the aroma property and type of European vinegar. There were obvious clustering trends of apple vinegar, wine vinegar, and balsamic vinegar in principal component analysis (PCA). The clustering of the vinegar-like groups was more obvious in orthogonal partial least squares discriminant analysis (OPLS-DA). The volatile markers discriminating vinegar, including acetic acid, propionic acid, pyrazine, furfural, isopropyl alcohol, and so on, were screened out by variable importance for the projection (VIP). These results demonstrated that UFGC E-nose is a novel, rapid and non-destructive analytical tool and can also be used for identifying aroma compounds and tracing the food species.

Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

As a powerful, non-destructive analysis tool based on thermal neutron capture reaction, prompt gamma neutron activation analysis (PGNAA) indeed requires the appropriate neutron source. Neutrons produced by electron Linac-based neutron sources should be thermalized to be appropriate for PGNAA. As a result, thermalization devices (TDs) are used for the usual fast neutron beam to simultaneously maximize the thermal neutron flux and minimize the non- thermal neutron flux at the beam port of TD. To achieve the desired thermal neutron flux, the optimized geometry of TD including the proper materials for moderators and collimator, as well as the optimized dimensions are required. In this context, TD optimization using only Monte Carlo approaches such as MCNP is a multi-parameter problem and time-consuming task. In this work, multilayer perceptron (MLP) neural network has been applied in combination with Q-learning algorithm to optimize the geometry of TD containing collimator and two moderators. Using MLP, both thickness and diameter of the collimator at the beam port of TD have first been optimized for different input electron energies of Linac as well as for moderators’ thickness values and the collimator. Then, the MLP has been learned by the thermal and non-thermal neutron flux simultaneously at the beam port of TD calculated by MCNPX2.6 code. After selecting the optimized geometry of the collimator, a combination of Q-learning algorithm and MLP artificial neural network have been used to find the optimal moderators’ thickness for different input electron energies of Linac. Results verify that the final optimum setup can be obtained based on the prepared dataset in a considerably smaller number of simulations compared to conventional calculation methods as implemented in MCNP.

Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour

Quinoa flour is prone to economically motivated adulteration due to its high nutritional value and growing demand worldwide. In this study, portable near-infrared spectroscopy (NIRS) combined with multivariate analysis was used to detect adulteration in quinoa flour. The initial investigation was carried out using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) to identify the most potential adulterate in quinoa flour. Quinoa flour samples were then adulterated with identified adulterant in the range of 0–51% (w/w). The partial least squares regression (PLSR) model built with original spectral data had the best performance for detecting the level of adulteration, with a coefficient of determination (Rp2) of 0.94, root-mean-square error of prediction (RMSEP) of 3.04%, ratio of prediction to deviation (RPD) of 4.04, and range error ratio (RER) of 11.84. The variable importance in projection (VIP) resulting from the PLSR model was used to select 13 informative spectral bands. The new PLSR model led to an R2p of 0.98, RMSEP of 1.60%, RPD of 7.71, and RER of 22.56. The results demonstrated the potential of portable NIRS as a rapid, low cost and non-destructive analytical tool for adulteration detection in quinoa flour.

Neutron imaging of lithium batteries

<h2>Summary</h2> Advanced batteries are critical to achieving net zero and are proposed within decarbonization strategies for transport and grid-scale applications, alongside their ubiquitous application in consumer devices. Immense progress has been made in lithium battery technology in recent years, but significant challenges remain and new development strategies are required to improve performance, fully exploit power density capacities, utilize sustainable resources, and lower production costs. Suitable characterization techniques are crucial for understanding, <i>inter alia</i>, three-dimensional (3D) diffusion processes and formation of passivation layers or dendrites, which can lead to drastic capacity reduction and potentially to hazardous short circuiting. Studies of such phenomena typically utilize 2D or 3D imaging techniques, providing locally resolved information. 3D X-ray imaging is a widely used standard method, while time-lapse (4D) tomography is increasingly required for understanding the processes and transformations in an operational battery. Neutron imaging overcomes some of the limitations of X-ray tomography for battery studies. Notably, the high visibility of neutrons for light-Z elements, in particular hydrogen and lithium, enables the direct observation of lithium diffusion, electrolyte consumption, and gas formation in lithium batteries. Neutron imaging as a non-destructive analytical tool has been steadily growing in many disciplines as a result of improvements to neutron detectors and imaging facilities, providing increasingly higher spatial and temporal resolutions. Further, ongoing developments in diffraction imaging for mapping the structural and microstructural properties of battery components make the use of neutrons increasingly attractive. Here, we provide an overview of neutron imaging techniques, generally outlining advances and limitations for studies on batteries and reviewing imaging studies of lithium batteries. We conclude with an outlook on development methods in the field and discuss their potential and significance for future battery research.

Hyperspectral imaging based kinetic approach to assess quality deterioration in fresh mushrooms (Agaricus bisporus) during postharvest storage

Hyperspectral imaging (HSI) is a non-destructive analytical tool that can be used for sensing multiple quality attributes for fresh commodities, but is usually constrained by complex data processing. Here, we report the development of HSI based simple kinetic models to assess quality deterioration in A. bisporus mushrooms. The commercially available fresh mushroom packages kept at constant refrigeration temperature (4 °C) were selected for HSI analysis in the visible and near infrared (VNIR) wavelengths (400–1000 nm) for a postharvest storage of 11 d. The observed HSI reflectance patterns clearly showed two sensitive wavelength regions: i) 450–700 nm and ii) 800–900 nm, which were termed as HSI index-I and HSI index-II, respectively. Alongside this, physicochemical and microbiological characterization of mushrooms were carried out and were related to HSI quality indices. The kinetic analysis showed that the HSI index-I is a useful quality indicator for the assessment of storage time for A. bisporus mushrooms. Furthermore, the physical quality attributes such as color and texture, which were changed significantly (p < 0.05) during the storage were linked to HSI index-I through linear regression models. The data processing approach presented in this study is quite useful that will facilitate the application of HSI in quality assessments of fresh commodities. • Hyperspectral imaging (HSI) based kinetic models are presented for A. bisporus . • HSI index-I (450–700 nm) was found a strong quality indicator. • HSI index-I can be simultaneously linked to color and texture parameters.

A time-course Raman spectroscopic analysis of spontaneous in vitro microcalcifications in a breast cancer cell line

Microcalcifications are early markers of breast cancer and can provide valuable prognostic information to support clinical decision-making. Current detection of calcifications in breast tissue is based on X-ray mammography, which involves the use of ionizing radiation with potentially detrimental effects, or MRI scans, which have limited spatial resolution. Additionally, these techniques are not capable of discriminating between microcalcifications from benign and malignant lesions. Several studies show that vibrational spectroscopic techniques are capable of discriminating and classifying breast lesions, with a pathology grade based on the chemical composition of the microcalcifications. However, the occurrence of microcalcifications in the breast and the underlying mineralization process are still not fully understood. Using a previously established model of in vitro mineralization, the MDA-MB-231 human breast cancer cell line was induced using two osteogenic agents, inorganic phosphate (Pi) and β-glycerophosphate (βG), and direct monitoring of the mineralization process was conducted using Raman micro-spectroscopy. MDA-MB-231 cells cultured in a medium supplemented with Pi presented more rapid mineralization (by day 3) than cells exposed to βG (by day 11). A redshift of the phosphate stretching peak for cells supplemented with βG revealed the presence of different precursor phases (octacalcium phosphate) during apatite crystal formation. These results demonstrate that Raman micro-spectroscopy is a powerful tool for nondestructive analysis of mineral species and can provide valuable information for evaluating mineralization dynamics and any associated breast cancer progression, if utilized in pathological samples.The mineralization of MDA-MB-231 breast cells was investigated using Raman micro-spectroscopy. Mineralization was induced by two osteogenic agents: inorganic phosphate (Pi) and β-Glycerophosphate (βG). The results show that the uptake of Pi allows a faster mineralization and the uptake of βG indicated the presence of a precursor phase during the hydroxyapatite crystal formation.

Infrared spectroscopy and forensic entomology: Can this union work? A literature review.

For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in Forensic entomology (FE) field, with vibrational techniques such as Near-infrared spectroscopy (NIRS) and Medium-infrared spectroscopy (MIRS) underutilized in forensic sciences. Thus, this work describes the potential of NIRS, MIRS, and other spectroscopic methodologies, for entomological analysis in FE, as well as discusses its future uses for criminal or civil investigations. After a thorough research on scientific journals database, a total of 33 publications were found in scientific journals, with direct or indirect application to FE, including experimental applications of NIRS and MIRS in taxonomic discrimination of species, larval age prediction, detection of toxic substances in insects from environments or crime scenes, and detection of internal or external infestations by live or dead insects in stored products. Besides, NIRS and MIRS combined with multivariate analysis were efficient, inexpensive, fast, and non-destructive analytical tools. However, more than 51% of the spectroscopic publications are concentrated in the stored products field, and so we discuss the need for expansion and more direct application in other FE areas. We hope the number of articles continues to increase, and as NIRS and MIRS technology progress, they advance in forensic research and routine use.