Search Results (234)

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

Backgrounds/Objective: The impact of metabolic disturbances induced by an unbalanced diet on cognitive decline in mid-life is now widely observed, although the mechanisms are not well identified. Here we report that glutamatergic vasoactive pathways are a key feature of high-fat-diet (HFD)-induced neurogliovascular uncoupling in mice. Methods: C57Bl6/J mice are fed either with normal diet (ND) or high-fat diet (HFD) during 6 or 12 months and characterized for metabolic status. Cerebral vascular tree from pial to intraparenchymal arteries, is investigated with Halpern’s arteriography and with differential interference contrast infrared imaging of brain slices. Results: A 70% alteration in the myogenic tone of the basilar artery is observed as early as 6 months (M6) after the HFD. Infrared imaging revealed a 77% reduction in the glutamate-induced vasodilation of intraparenchymal arterioles appearing after 12 months (M12) of the HFD. The respective contributions of enzymes involved in glutamatergic pathways were altered as a function of HFD and time. The decrease in astrocytic COX I observed at M6 was followed by a loss of neuronal COX II and a compensatory action of NOS at M12. Conclusions: This HFD-induced neurogliovascular uncoupling pathway offers therapeutic targets to consider for improving cerebral vasoactive functions while preventing peripheral metabolic disturbances. Full article

Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise. This review summarizes six cutting-edge nondestructive spectroscopic and imaging technologies, near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging (spanning the UV, visible, and NIR regions, to simultaneously capture both spatial distribution and spectral signatures of sample constituents), terahertz spectroscopy, and nuclear magnetic resonance (NMR), along with their transformative applications. We systematically elucidate the fundamental principles and distinctive merits of each technological approach, with a particular focus on their deep learning-based integration with spectral fusion techniques and hybrid spectral-heterogeneous fusion methodologies. Our analysis reveals that the synergy between spectroscopic technologies and deep learning demonstrates unparalleled superiority in speed, precision, and non-invasiveness. Future research should prioritize three directions: multimodal integration of spectroscopic technologies, edge computing in portable devices, and AI-driven applications, ultimately establishing a high-precision and sustainable food quality inspection system spanning from production to consumption. Full article

Two-dimensional (2D) materials have revolutionized the field of optoelectronics by offering exceptional properties such as atomically thin structures, high carrier mobility, tunable bandgaps, and strong light–matter interactions. These attributes make them ideal candidates for next-generation photodetectors operating across a broad spectral range—from ultraviolet to mid-infrared. This review comprehensively examines the recent progress in 2D material-based photodetectors, highlighting key material classes including graphene, transition metal dichalcogenides (TMDCs), black phosphorus (BP), MXenes, chalcogenides, and carbides. We explore their photodetection mechanisms—such as photovoltaic, photoconductive, photothermoelectric, bolometric, and plasmon-enhanced effects—and discuss their impact on critical performance metrics like responsivity, detectivity, and response time. Emphasis is placed on material integration strategies, heterostructure engineering, and plasmonic enhancements that have enabled improved sensitivity and spectral tunability. The review also addresses the remaining challenges related to environmental stability, scalability, and device architecture. Finally, we outline future directions for the development of high-performance, broadband, and flexible 2D photodetectors for diverse applications in sensing, imaging, and communication technologies. Full article

The application of intelligent video monitoring for natural resource protection and management has become increasingly common in recent years. To enhance safety monitoring during the grazing prohibition and rest period of grassland, this paper proposes a multi-object segmentation algorithm based on mid-infrared images for all-weather surveillance. The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. First, a robust background modelling, i.e., the ITF-GMM, is proposed. Unlike the basic Gaussian Mixture Model (GMM), the proposed model dynamically adjusts the learning rate according to the content difference between adjacent frames and optimizes the number of Gaussian distributions through time series histogram analysis of pixels. Second, a segmentation framework based on Con-UNet is developed to improve the feature extraction ability of UNet. In this model, the maximum pooling layer is replaced with a convolutional layer, addressing the challenge of limited training data and improving the network’s ability to preserve spatial features. Finally, an integrated computation strategy is designed to combine the outputs of ITF-GMM and Con-UNet at the pixel level, and morphological operations are performed to refine the segmentation results and suppress noises, ensuring clearer object boundaries. The experimental results show the effectiveness of proposed approach, achieving a precision of 96.92%, an accuracy of 99.87%, an intersection over union (IOU) of 94.81%, and a recall of 97.75%. Furthermore, the proposed algorithm meets real-time processing requirements, confirming its capability to enhance small-target detection in complex outdoor environments and supporting the automation of grassland monitoring and enforcement. Full article

The mid-infrared band is the last major observational window for the ground-based large solar telescopes in the 21st century. Achieving ultra-narrowband filter imaging is a fundamental challenge that all solar telescopes encounter as they progress towards the mid-infrared spectrum. The guided-mode resonance filtering (GMRF) technology provides a promising solution to this critical issue. This paper describes in detail the fundamental principles and calculation procedure of guided-mode resonance filtering. Building upon this foundation, a preliminary design and simulation of a mid-infrared guided-mode resonance filter are carried out. The results show that when the thickness of the sub-wavelength grating is an even multiple of the half-wavelength, it is feasible to attain ultra-narrowband filtering with a bandwidth below 0.03 nm by increasing the grating thickness and decreasing the grating fill factor. Nevertheless, the high sensitivity of the resonant wavelength to the angle of incidence still stands as a formidable obstacle that demands further investigation and resolution. Full article

To address issues of food authenticity, such as fraud and origin tracing, it is essential to employ methods in food fingerprinting that are efficient, economical, and easy to use. This review highlights the capabilities of vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, as non-invasive tools for food authentication. These methods offer rapid, cost-effective, and environmentally friendly analysis across diverse food matrices. This review further discusses recent advances such as hyperspectral imaging, portable devices, and data fusion strategies that integrate chemometrics and artificial intelligence. Despite their promise, challenges remain, including limited sensitivity for certain compounds, spectral overlaps, fluorescence interference in Raman spectroscopy, and the need for standardized validation protocols. Looking forward, trends such as the miniaturization of devices, real-time monitoring, and AI-enhanced spectral interpretation are expected to significantly advance the field of food authentication. Full article

This study systematically investigates the combustion behavior and kinetic characteristics of oil shale semi-coke. Thermogravimetric analysis (TGA) experiments, combined with both model-free and model-based methods, were used to explore the thermal characteristics, kinetic parameters, and reaction mechanisms of the combustion process. The results show that the combustion process of oil shale semi-coke can be divided into three stages: a low-temperature stage (50–310 °C), a mid-temperature stage (310–670 °C), and a high-temperature stage (670–950 °C). The mid-temperature stage is the core of the combustion process, accounting for approximately 28–37% of the total mass loss, with the released energy concentrated and exhibiting significant thermal chemical activity. Kinetic parameters calculated using the model-free methods (OFW and KAS) and the model-based Coats–Redfern method reveal that the activation energy gradually increases with the conversion rate, indicating a multi-step reaction characteristic of the combustion process. The F2-R3-F2 model, with its segmented mechanism (boundary layer + second-order reaction), better fits the physicochemical changes during semi-coke combustion, and the analysis of mineral phase transformations is more reasonable. Therefore, the F2-R3-F2 model is identified as the optimal model in this study and provides a scientific basis for the optimization of oil shale semi-coke combustion processes. Furthermore, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were conducted on oil shale semi-coke samples before and after combustion to study the changes in the combustion residues. SEM images show that after combustion, the surface of the semi-coke sample exhibits a large number of irregular holes, with increased pore size and a honeycomb-like structure, indicating that the carbonaceous components were oxidized and decomposed during combustion, forming a porous structure. XRD analysis shows that the characteristic peaks of quartz (Q) are enhanced after combustion, while those of calcite (C) and pyrite (P) are weakened, suggesting that the mineral components underwent decomposition and transformation during combustion, particularly the decomposition of calcite into CO₂ at high temperatures. Infrared spectroscopy (IR) analysis reveals that after combustion, the amount of hydrocarbons in the semi-coke decreases, while aromatic compounds and incompletely decomposed organic materials are retained, further confirming the changes in organic matter during combustion. Full article

Thermal imaging technology has revolutionized various fields, but current high operating temperature (HOT) mid-wave infrared (MWIR) cameras, particularly those based on xBn detectors, face limitations in size and cost due to the need for cooling to 150 Kelvin. This study explores the potential of extending the operating temperature of these cameras to 180 Kelvin, leveraging advanced AI algorithms to mitigate the increased thermal noise expected at higher temperatures. This research investigates the feasibility and effectiveness of this approach for remote sensing applications, combining experimental data with cutting-edge image enhancement techniques like Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The findings demonstrate the potential of 180 Kelvin operation for xBn MWIR cameras, particularly in daylight conditions, paving the way for a new generation of more affordable and compact thermal imaging systems. Full article

Multi-messenger astronomy and time-domain astronomy are strongly linked even if they do not have the same objectives. Multi-messenger astronomy is an astrophysical observation approach born by the simultaneous, even if casual, detection of a few events discovered up to now. In contrast, time-domain astronomy is a recent technological trend that aims to make observations to explore the sky not with imaging, astrometry, photometry or spectroscopy but through the fast dynamic behavior of celestial objects. Time-domain astronomy aims to detect events on a temporal scale between seconds and nanoseconds. In this paper, a time-domain infrared fast detector for ground-based telescopes is proposed. This instrument can be useful for multi-messenger observations, and it is able to detect fast astronomical signals in the order of 1 ns. It is based on HgCdTe photoconductors, but the InAsSb photovoltaic detector has also been tested. The detection system designed to detect fast mid-infrared bursts includes trigger modules, an off-line noise-canceling strategy, and a classifier of the transients. Classification is derived from the analysis of fast instabilities in particle circular accelerators. This paper aims to be a preliminary feasibility study. Full article

Mid-wave infrared (MWIR) hyperspectral imaging integrates MWIR technology with hyperspectral remote sensing, enabling the capture of radiative information that is difficult to obtain in the visible spectrum, thus demonstrating significant value in camouflage recognition and stealth design. However, there is a notable lack of open-source datasets and effective classification methods in this field. To address these challenges, this study proposes a dual-channel attention convolutional neural network (DACNet). First, we constructed four MWIR camouflage datasets (GCL, SSCL, CW, and LC) to fill a critical data gap. Second, to address the issues of spectral confusion between camouflaged targets and backgrounds and blurred spatial boundaries, DACNet employs independent spectral and spatial branches to extract deep spectral–spatial features while dynamically weighting these features through channel and spatial attention mechanisms, significantly enhancing target–background differentiation. Our experimental results demonstrate that DACNet achieves an average accuracy (AA) of 99.96%, 99.45%, 100%, and 95.88%; an overall accuracy (OA) of 99.94%, 99.52%, 100%, and 96.39%; and Kappa coefficients of 99.91%, 99.41%, 100%, and 95.21% across the four datasets. The classification results exhibit sharp edges and minimal noise, outperforming five deep learning methods and three machine learning approaches. Additional generalization experiments on public datasets further validate DACNet’s superiority in providing an efficient and novel approach for hyperspectral camouflage data classification. Full article

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R² = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R² = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R² = 0.86), AWP with red-edge reflectance (R² = 0.71), turnips with NIR, GNDVI, and CHM (R² = 0.95), and mixed species with NIR and blue band reflectance (R² = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture. Full article

When an unmanned aerial vehicle (UAV) tilts to capture an image of a ground target, variations in object distance may lead to uneven resolution distribution, with the focal length ranging from zero to the full field of view. The field-of-view focal length (FFL), which is a function of the field of view, characterizes the optical properties of the system for each viewing angle. The field-of-view focal length (FFL) quantifies the incremental change in image height resulting from marginal rays exiting the optical system, with infinitesimal angular variations at the field boundary. The optical aberration manifests as an effective focal length variation that exhibits field-dependent characteristics. Through systematic calculation and optimization of the field-of-view focal lengths (FFLs) for ground resolution (GR) control, a mid-wave infrared (MWIR) optical system has been successfully designed, featuring a 10° × 8° field of view (FOV) with an F-number of 3. The optical system implements field-adapted focal length adjustment across distinct viewing angles to ensure consistent ground resolution preservation throughout the full field of view. The designed optical system achieves near-diffraction-limited modulation transfer function (MTF) performance across the full field of view, with all dispersion spots consistently confined within the Airy disk at every viewing angle. The optical system demonstrates superior imaging performance with all dispersion spots confined within the Airy disk radius, fully complying with stringent image quality specifications. Featuring a compact structural configuration, the system exhibits optimal suitability for airborne ground-target reconnaissance applications. Full article

We propose a wide field-of-view (FOV) refractive and meta-optics hybrid imaging system designed for the mid-wave infrared spectrum (3–5 μm) to address the challenge of high-quality imaging in wide FOV applications. The system consists of only three refractive lenses and two metasurfaces (one functioning as a circular polarizer and the other as a phase element), with a total length of 29 mm. Through a detailed analysis of modulation transfer function curves and spot diagrams, the system achieves 178° FOV while maintaining exceptional imaging performance across a temperature range of −40 °C to 60 °C. The system demonstrates the potential for extending applications to other wavelengths and scenarios, thereby contributing to the advancement of high-performance compact optical systems. Full article