Search Results (1,136)

The rapid chilling and aging stages following pork slaughter represent a critical window for determining final physicochemical quality and flavor development. To address the destructive nature of conventional meat quality assessment methods and the limitations of rigid spectral probes when applied to irregular biological surfaces, this study developed and validated a wireless monitoring system integrating a flexible visible/near-infrared (VIS/NIR) sensing array with ensemble learning algorithms. The proposed system enables non-destructive, continuous monitoring of pork quality during cold-chain storage. A DeepEnsemble regression model based on a stacking framework was constructed by integrating Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to predict pH, moisture content, and total amino acid concentration. During a 26 h dynamic aging experiment, the proposed model achieved coefficients of determination (R²) of 0.9019, 0.9687, and 0.9600 for pH, moisture content, and total amino acids, respectively, with prediction performance exceeding that of individual regression models. The wireless transmission module maintained stable data communication under low-temperature and high-humidity conditions (−20 °C and 0–4 °C), with packet loss rates below 0.1%. These results indicate that the proposed system can effectively capture the dynamic evolution of pork quality during aging and provides a practical non-destructive approach for intelligent pork quality evaluation, cold-chain monitoring, and digital management of meat supply chains. Full article

Peach tree (Prunus persica L. Batsch) is a fruit species of great economic importance worldwide. Thousands of chemical leaf analyses are performed on a yearly basis to support decision-making about fertilizer application. However, traditional methods to determine nutrient content in plant tissue require a mix of strong acids, besides being time-consuming and generating polluting waste. Visible (Vis) and near-infrared (NIR) spectroscopy combined with multivariate techniques emerges as a potential solution to overcome limitations of traditional chemical analyses. The aim of the present study is to combine Vis-NIR spectral data and multivariate techniques to test strategies for the development of models to estimate nutrient content in peach leaves. The study estimated N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn content in the leaves of peach trees grown in two locations, namely: Pelotas and Pinto Bandeira, in Southern Brazil. Therefore, local and regional scale prediction models were developed by combining preprocessed Vis-NIR spectral data to both Savitzky–Golay first-derivative (SGD1d) and partial least squares regression (PLSR) multivariate technique. Most of the proposed prediction models showed average accuracy (R² ≥ 0.50 and <0.75, RPIQ ≥ 1.9 and <3.0). The local-1 ‘PB’ model showed higher nutrient prediction accuracy than the regional ‘PB + Pelotas’ model and the local-2 ‘Pelotas’ model. Estimates on nutrient content in peach tree leaves subjected to local, local-1 ‘PB’ and local-2 ‘Pelotas’ models fed with data collected in the same site showed better performance than calculations based on data from other sites and/or regions. Finally, the current study allowed making updates in the refinement of more sustainable techniques to set nutrient content. Full article

Advanced sensing technologies increasingly support monitoring and decision-making processes in modern agriculture. This study investigates the feasibility of developing a harvest timing monitoring workflow based on a portable hyperspectral imaging (HSI) system in the visible–near-infrared (VIS-NIR: 400–1000 nm) range, coupled with machine learning. A hierarchical Partial Least Squares–Discriminant Analysis (Hi-PLS-DA) model was developed and tested to discriminate harvestable from non-harvestable plants of Brassica rapa subsp. sylvestris through the identification of open flowers within otherwise closed flower buds in the raceme. The classification included four target plant classes, i.e., green inflorescences, green leaves, yellow flowers, and yellow leaves, along with two non-target classes, background and not-classified (NC), which were included to support the classification process. The predicted hyperspectral images demonstrated a clear distinction between closed and open flowers, supported by satisfactory classification performance (sensitivity, specificity, precision, and F1-score: 0.78–1.00). This workflow proved effective in handling intrinsic outdoor hyperspectral variability, mitigating illumination and canopy texture, and offers useful methodological insights for the possible future integration of HSI-based approaches into automated field applications, paving the way for rapid, real-time harvest decision support. Full article

The growing need for real-time, accurate monitoring of hazardous gases in environmental, industrial, and healthcare settings has highlighted the limitations of traditional sensing methods. Photonic Integrated Circuits (PICs) have become a revolutionary platform due to their high sensitivity, accurate selectivity, compact size and cost-effectiveness. We present in this work a comprehensive overview of the best-reported PIC-based gas sensors. We discuss the basic concepts behind resonance-based and absorption-based sensing. A detailed overview of the various material platforms, from well-known silicon and silicon nitride to new polymers, chalcogenide glasses, and 2D materials, is presented. A comparison of key device topologies, such as waveguides, microring resonators, Mach–Zehnder interferometers, and metasurfaces, is conducted, with performance benchmarks indicating the limit of detection (LoD). The main limitations of PIC sensors are discussed in this review. We also discuss promising technologies, especially the game-changing potential of artificial intelligence to create fully autonomous devices. Full article

Soybean cyst nematode (SCN) is the most economically devastating pathogen of soybean in North America. Even at low to moderate infestation levels, SCN can cause 20–30% yield loss without producing any visible aboveground symptoms. In severely infested fields, yield reductions can reach 60–70% and, in extreme cases, exceed 80%. Prior research on identifying SCN infestations has primarily relied on traditional machine-learning methods applied to Unmanned Aerial System (UAS)-based multispectral imagery, with limited success. This study hypothesizes that deep-learning (DL) methods can more effectively capture the subtle spectral and spatial signatures in multispectral images of SCN stress. To address this gap, we evaluate the performance of advanced DL architectures, including Vision Transformer (ViT) and a customized Convolutional Neural Network (CNN), for detecting SCN infestation in soybean fields using multispectral UAS imagery. Spectral analysis of the multispectral imagery revealed that the near-infrared (NIR) band is a strong discriminator between non-detected and SCN-infested areas. The DL models trained and tested across multiple growth stages showed promising results. The four-timestamp ViT model (3 June, 29 July, 19 August, and 2 September) achieved an F1-score of 0.74, while the five-timestamp SCN–CNN model (3 June, 22 July, 29 July, 19 August, and 2 September) achieved an F1-score of 0.75. Although overall performance was comparable, ViT demonstrated more stable performance across varying training and test data distributions. These findings highlight the effectiveness of DL architectures to automatically extract subtle, complex plant features from multispectral imagery throughout the growing season. Compared with manual, time-consuming soil-sampling techniques, the proposed framework enables more precise spatial and temporal monitoring of SCN infestations across fields. Full article

Glass is a highly recyclable material that provides substantial environmental benefits, including savings in raw materials and energy as well as a reduction in CO₂ emissions. To ensure the production of high-quality secondary raw materials, container glass from municipal waste separate collection must be accurately separated by color in recycling plants, where only minimal color mixing is tolerated. Color sorting is therefore a key step in glass recycling, as it directly affects both the quality and the market value of recycled cullet. Given the increasingly stringent color quality requirements for recycled glass and the high fraction of cullet used in container glass, advanced technological solutions are needed to improve sorting accuracy. In this study, a visible–near-infrared (VIS-NIR: 400–1000 nm) hyperspectral imaging (HSI) approach integrated with machine learning (ML) is proposed for the automated classification of post-consumer glass fragments from bottles and jars into five color categories: brown, dark green, light green, half-white and white. A hierarchical Partial Least Squares-Discriminant Analysis (PLS-DA) model combined with an object-based analysis strategy was developed to optimize color recognition. The proposed system achieved sensitivity and specificity values between 0.910 and 1.000, demonstrating excellent robustness and predictive capability. Validation on independent datasets confirmed the model’s reliability, with all color glass fragments correctly classified at the object level. The results highlight the potential of HSI-ML systems to enhance color sorting accuracy and process efficiency in recycling plants, contributing to improved material recovery and the advancement of sustainable, circular glass production. Full article

A water-rich muscle-equivalent tissue-mimicking phantom within a polymeric matrix was experimentally evaluated through a multimodal characterization methodology to determine whether it reproduces the coupled dielectric–thermal behavior of hydrated biological tissue under exposure to electromagnetic waves. The material was analyzed using thermogravimetric analysis, microwave dielectric spectroscopy from 1.5 to 4.0 GHz, VIS–NIR spectroscopy between 350 and 1200 nm, and terahertz time-domain reflection. The thermogravimetric results confirmed dominant water content, with primary mass loss below 200 °C, establishing hydration as the governing factor of its thermal response. Next, the microwave dielectric measurements show that the phantom exhibits a relative permittivity of 37.4 and an electrical conductivity of 2.4 S/m. On the other hand, the VIS–NIR spectra show smooth broadband absorption with limited spatial variability, and principal component analysis reveals macroscopic optical homogeneity without structural spectral distortion. In the THz regime, strong broadband attenuation characteristic of water-rich matrices is observed, and reflection-mode measurements enable robust assessment of temporal stability through time- and frequency-domain signatures. Finally, a microwave thermal validation demonstrates stable behavior under low-power excitation, whereas under hyperthermia-level irradiation, a significant thermal drift of −3.985 °C/h was reached under non-adiabatic conditions, identifying hydration-mediated moisture redistribution as the principal limitation during prolonged high-power exposure. Collectively, these results demonstrate cross-regime dielectric–thermal consistency while explicitly defining the hydration-driven constraints governing long-term stability, providing a validated reference material for broadband electromagnetic and thermal biomedical experimentation. Full article

A flexible visible/near-infrared (Vis/NIR) sensing system (FVNS) was developed for the non-destructive assessment of almond composition. Almonds from four distinct varieties were measured under non-contact conditions, and the acquired spectra were preprocessed using Savitzky–Golay (S–G) smoothing and standard normal variate (SNV). Based on the spectral data captured by the FVNS, random forest (RF) regression models were established to quantify protein and fat contents. The optimized RF models achieved prediction coefficients of determination (R²_p) of 0.91 for protein and 0.86 for fat, with corresponding residual predictive deviation (RPD) values of 3.32 and 2.67, respectively. These results demonstrate that the FVNS possesses reliable quantitative capability and can accurately capture compositional variations in almonds while maintaining low cost, portability, and real-time wireless operation. Full article

The accurate estimation of the instantaneous UV Index (UVI) is critical for public health, yet it is often attempted using broadband pyranometers (measuring Global Horizontal Irradiance GHI) or photometers (measuring Lux). This approach is known to be unreliable, particularly under the complex radiative conditions induced by clouds. However, the physical mechanisms driving this failure, specifically the changes in the spectral quality of sunlight, are not fully quantified. This study utilizes a high-resolution spectroradiometer and pyranometer at a Mediterranean site (Rende, Italy), analyzing instantaneous UVI, GHI and a set of derived analytical metrics: the Erythemal Efficacy, the UV Spectral Quality Ratio and the Clearness Index. The core metric of the paper is the Erythemal Efficacy, designed to quantify the “spectral quality” or “biological hazard” per unit of total energy. It is defined as the ratio of the instantaneous UV Index to the instantaneous GHI measured by the pyranometer. The analysis confirms a decoupling between instantaneous UVI and broadband GHI, exhibiting a wide, non-functional scatter. The paper shows that this failure is caused by the high variability of the Erythemal Efficacy, which is not a constant. Its variability is shown to be linearly governed by the internal Ultraviolet A to Ultraviolet B (UVA/UVB) spectral ratio. Most critically, the Erythemal Efficacy was found to follow a counter-intuitive trend, increasing significantly as the Clearness Index decreases. The common assumption of clouds as spectrally “grey” attenuators is flawed. Clouds act as selective filters, attenuating the GHI, dominated by Visible to Near-Infrared (VIS/NIR), more severely than the UVI. This increases the relative biological hazard of the light that penetrates thick cloud cover. This study provides a physical explanation for the failure of broadband proxies and demonstrates that instantaneous GHI or Lux-based UVI alerts are fundamentally unreliable, as they fail to capture the critical variability of spectral quality. Full article

Photothermal therapy (PTT) is an emerging minimally invasive approach for cancer treatment that relies on photothermal agents capable of efficiently converting near-infrared (NIR) light into localized heat. In this work, silica–gold nanostructures (SGNs) were synthesized and systematically evaluated to investigate how silica core size influences the photothermal response of the SGNs and optimize their performance as a photothermal agent. SGNs were synthesized with silica cores ranging from 54 to 244 nm in diameter and coated with gold nanoparticles of 4–10 nm in size, enabling controlled tuning of their localized surface plasmon resonance within the NIR region. The morphology and composition were characterized by SEM, TEM, and EDS; optical properties were analyzed by UV-Vis spectroscopy. The SGNs photothermal response low-power laser irradiation at 852 nm and 1310 nm and temperature changes were monitored using a thermographic camera. A maximum temperature increase of 7.1 °C was observed for SGNs with a silica core diameter of approximately 77 nm under the 852 nm laser irradiation. Numerical simulations of the absorption efficiency showed good agreement with experimental UV–Vis spectra and thermal measurements, revealing a size-dependent shift of the absorption toward longer wavelengths for larger nanostructures. These results demonstrate that the photothermal response of silica–gold nanostructures can be rationally tuned through the control of core size and gold growth parameters, providing a framework for the design of wavelength-matched photothermal agents for PTT applications. Full article

The development of sustainable pigments from natural sources is gaining interest due to environmental concerns and the need for bio-based alternatives to synthetic dyes. This study investigates the synthesis of hybrid pigments by adsorbing anthocyanins—extracted from pomegranate agro-waste—onto halloysite (HA) nanotubes. A full factorial design was applied to evaluate the influence of pH and surfactant type (cetylpyridinium bromide and sodium dodecyl sulfate) on pigment colour and the thermal and structural stability of the hybrids. Adsorption was carried out in 400 mL dispersion baths containing 10 g of HA and 5% w/w anthocyanins. Surfactants (2% w/w) were added before the pigment, followed by 200 µL of silane. Dispersions were stirred at high speed for 1 h and then at 500 rpm for 23 h to ensure adsorption without premature desorption. Characterisation (TGA, XRD, FTIR, UV-Vis/NIR, SEM, EDX, BET) confirmed the preservation of HA structure and minimal changes in thermal behaviour. Pigment colour varied with synthesis conditions, especially pH: a higher pH increased brightness and yielded yellowish tones, while a lower pH resulted in reddish-blue hues with greater variability. The results confirm halloysite’s potential as a stable carrier for natural dyes and demonstrate that pH effectively tunes hybrid pigment colour. Full article

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness. Full article

Ultrasmall near-infrared (NIR)-responsive quantum dots (QDs) are highly promising for deep-tissue phototherapy but often face challenges with biocompatibility and clearance. In this study, Cu₂ZnSnS₄ quantum dots (CZTS QDs) were synthesized via a non-injection method and surface-functionalized with glutathione (GSH) to create water-dispersible and biocompatible CZTS@GSH QDs. Comprehensive characterization using XRD, TEM, DLS, XPS, and UV-Vis spectroscopy confirmed a sphalerite-type ZnS crystal structure, an average hydrodynamic diameter of ~6.2 nm, and a band gap of 1.47 eV (843.5 nm). The CZTS@GSH QDs demonstrated effective photothermal conversion under 808 nm laser irradiation, achieving a temperature increase sufficient for photothermal therapy (PTT). Furthermore, using a DPBF assay, the QDs were shown to generate singlet oxygen, confirming their photodynamic therapy (PDT) capability. Owing to their ultrasmall size, strong NIR absorption, and demonstrated dual PTT/PDT functions, the CZTS@GSH QDs are established as a nanoplatform with potential for combined cancer treatment. Full article

Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agriculture spectral library, this study evaluated the effects of raw and preprocessed spectra on the prediction accuracy of eleven key soil properties across Vis–NIR and MIR regions using multiple machine learning algorithms. Spectral preprocessing, combining baseline correction and standard normal variate transformation, consistently improved prediction accuracy compared to the raw spectra. Overall, MIR-based models consistently outperformed Vis–NIR across all soil properties, with the largest performance gains observed for potassium, bulk density, and nitrate nitrogen. Among the machine learning approaches evaluated, artificial neural networks and categorical boosting algorithms provided the strongest and most consistent predictive performance across both spectral regions. These findings demonstrate that combining appropriate spectral preprocessing, spectral region selection, and advanced machine learning algorithms can substantially improve soil property prediction using spectroscopy. Full article

This study aimed to evaluate the effectiveness of spectral absorption-feature indices, derived from soil hyperspectral diffuse reflectance spectroscopy, as covariates within a multivariate geostatistical framework to enhance the digital mapping of soil organic carbon (SOC). The approach also incorporated exhaustively measured auxiliary variables derived from topographic and textural attributes. The research was conducted in a 1.39-km² forested catchment, where 135 topsoil samples (0–0.20 m depth) were collected from soils classified as Typic Xerumbrepts and Ultic Haploxeralfs. All samples were analyzed for SOC concentration, soil texture, and diffuse reflectance spectra across the VIS–NIR–SWIR region (350–2500 nm). The continuum-removal technique was applied to compute radiometric indices associated with absorption features in the visible region and at 1400, 1900, and 2200 nm. Results demonstrated that these indices effectively captured the SOC spatial variability when combined with silt fraction and topographic attributes, which, among the other covariates, actually exhibited the strongest spatial relationships with SOC. Compared to univariate ordinary kriging, the multivariate geostatistical approach yielded improved prediction accuracy in cross-validation, mostly due to the use of hyperspectral indices as auxiliary variables. Moreover, the geostatistical analysis revealed that the multivariate frame of spatial association was characterized by two distinct spatial scales. The findings of this work then support the use of hyperspectral indices as valuable covariates for digital modelling of SOC distribution even in landscapes characterized by heterogeneous topography and pedology. Full article