Search Results (1,009)

Ensuring food safety requires the detection of trace-level contaminants such as pesticides, mycotoxins, and heavy metals. Analytical approaches for these analytes should feature high sensitivity, good selectivity, and compatibility with aqueous matrices; surface-enhanced Raman spectroscopy (SERS) satisfies these requirements. Addressing the absence of a unified comparative analytical framework, this critical review surveys recent SERS-enabled sensing strategies for food contaminants. Detection strategies differ substantially across the three contaminant classes: pesticides can be directly detected at ppb levels through substrate engineering and deep learning; mycotoxins rely on affinity-recognition elements to reach pg-mL-level sensitivity; and Raman-inactive heavy metals demand indirect readout via functional probes. Crucially, despite these divergent analytical routes, the field confronts three shared bottlenecks—spectral irreproducibility, severe matrix interference, and the lack of standardized protocols, all of which hinder regulatory adoption. Compared with near-infrared spectroscopy (NIR) and hyperspectral imaging (HSI), SERS delivers outstanding sensitivity for confirmatory trace-level analysis, while its limited throughput may be compensated by multispectral data fusion. Future advances should prioritize portable sensing hardware, explainable Artificial Intelligence (AI), and multiplexed detection to transfer laboratory-scale sensitivity toward practical field-deployable testing tools. Full article

Background/Objectives: Eating disorders are increasingly conceptualized as information-processing disorders characterized by attentional biases toward food-related stimuli. However, whether such biases are already detectable in subclinical, non-treatment-seeking populations remains unclear. This study examined whether subclinical eating pathology, assessed with the Eating Attitudes Test-26 (EAT-26), is associated with distinct patterns of visual attention toward high- and low-calorie food images in university students. Methods: In this cross-sectional observational study featuring an experimental eye-tracking task, 89 university students completed the EAT-26 and a visual task displaying paired high- and low-calorie food images alongside neutral controls. Oculomotor metrics included first fixation duration (FFD), fixation count (FC), and average fixation duration (AFD). Data were evaluated using EAT-26 threshold-based group stratification (lower-risk vs. risky eating behavior) and Spearman rank correlation analyses. Results: Global EAT-26 scores showed a significant correlation with a directional attentional bias index (rho = 0.29, p = 0.006). The Dieting subscale demonstrated no significant relationships with any oculomotor metrics. Conversely, Oral Control scores were significantly negatively correlated with the directional attentional bias index (rho = −0.27, p = 0.009) and positively tracked with low-calorie fixation counts (rho = 0.30, p = 0.004). Bulimia subscale scores showed a moderate positive correlation with the directional attentional bias index (rho = 0.26, p = 0.013), characterized by a significant decrease in low-calorie fixation counts (rho = −0.32, p = 0.002) and a strong increase in high-calorie fixation counts (rho = 0.36, p < 0.001) during sustained viewing. Conclusions: Subclinical eating pathology tendencies are associated with distinct implicit attentional profiles regarding food caloric density during sustained cognitive evaluation. Oral control drives visual prioritization of low-calorie stimuli to maintain inhibitory control, whereas bulimic tendencies reflect prolonged visual preoccupation with high-calorie reward cues. Eye-tracking represents a promising non-invasive research tool for exploring early cognitive correlates of eating-disorder risk. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems. Full article

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work presents three contributions: (i) a novel mechatronic apparatus that mechanically guarantees dual-sided imaging of every bean, (ii) a public 12-class dataset of green coffee bean defects, and (iii) an embedded, real-time inspection pipeline validated on low-cost hardware. The apparatus sequentially presents each bean, from a standard 350 g sample, to two 16-megapixel cameras under controlled LED illumination. A dataset of 9600 images spanning 12 classes (11 defects and 1 normal) was generated from expert-classified samples and enriched through data augmentation. Four convolutional neural network (CNN) architectures, VGG-16, VGG-19, ResNet-50 and YOLOv8, were trained and benchmarked using precision, recall, F1-score and mean average precision. YOLOv8 achieved the best overall performance, with a precision of 97.4%, a recall of 99.6%, an F1-score of 0.930 and a mean average precision of 96.5%, outperforming VGG-16 (accuracy 86.07%), VGG-19 (accuracy 67.03%) and ResNet-50 (accuracy 87.76%). Dual-sided acquisition raised mean per-class detection accuracy from 0.727 to 0.908, a relative gain of 25.7% over an equivalent single-sided configuration. Deployed in real-time “track” mode on a Raspberry Pi 4, the system simultaneously classifies defects and counts beans by category, processing a 350 g sample in approximately 38 min. Combining mechanical innovation with lightweight deep learning enables practical, scalable, and cost-effective quality control for laboratories specialized in coffee analysis. Full article

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem. Full article

Theranostics has transformed nuclear medicine from an imaging-focused discipline into a data-rich radiation medicine platform in which target expression, pharmacokinetics, tumor dose, and response can be measured in the same patient. This critical narrative review evaluates patient-specific integration of radiopharmaceutical therapy (RPT) with external-beam radiotherapy (EBRT), with emphasis on quantitative imaging, absorbed-dose estimation, spatial registration, biological interpretation, adaptation thresholds, and reporting. We performed a targeted search of PubMed/MEDLINE, ClinicalTrials.gov, U.S. Food and Drug Administration records, professional-society guidance, and reference lists for English-language evidence available through 31 July 2026, prioritizing guidelines, regulatory documents, randomized and prospective trials, technical validation studies, and clinically informative retrospective series. Established RPT platforms are distinguished from investigational combined-modality applications and emerging or speculative technologies. Liver-directed Y-90 radioembolization combined with focal EBRT remains the most developed model, whereas head and neck, prostate, meningioma, lymphoma, and bone-dominant strategies illustrate distinct clinical geometries and levels of readiness. Across platforms, direct addition of absorbed dose in gray (Gy) is a geometric description, not automatically a biological endpoint; biologically effective dose (BED) and equivalent dose in 2-Gy fractions (EQD2) should be treated as model-based estimates with explicit assumptions. Future theranostic radiation medicine should therefore be built on prospective trials with prespecified dosimetry, uncertainty analysis, adaptation rules, and shared cross-modality reporting standards. Full article

Background/Objectives: Disordered eating behaviours are increasingly common among young adults, yet nutrition-focused, region-specific data from Romania and Central and Eastern Europe (CEE) remain scarce. We characterised nutritional patterns, weight-control practices, disordered-eating indicators, body image, and psychosocial factors among Romanian young adults and tested how these differed across body mass index (BMI) categories. Methods: In a cross-sectional online survey (March–April 2026), 753 Romanian adults aged 18–30 years (93.5% female) completed a 30-item questionnaire. Self-reported anthropometric measurements were classified using WHO BMI cutoffs. Prevalences (with Wilson 95% confidence intervals [CIs]) were compared across BMI categories using the chi-square test, and multivariable logistic regression, adjusted for age and sex, estimated odds ratios (ORs). Results: Most participants were normal weight (59.1%); 11.6% were underweight, 29.3% were overweight or obese, and 86.3% were omnivorous. Dietary restriction (57.4%) and physical exercise (46.7%) were the leading weight-control methods; self-induced vomiting (6.4%) and medication (4.5%) were less frequent. Lifetime compulsive/emotional eating was reported by 50.1%, current stress-related eating by 49.1%, and guilt after eating by 66.1%. Lifetime night eating reached 31.9% (current, 12.9%). Body dissatisfaction (36.1% “not at all satisfied”), difficulty looking in the mirror (66.7%) and self-reported stress (82.2%) were widespread. In adjusted models, dietary restriction, emotional eating and guilt after eating rose steeply and monotonically with BMI (overweight/obesity vs. normal weight: aOR 2.34, 2.49 and 2.73, respectively; all p < 0.001), whereas current night eating and frequent self-weighing did not differ across BMI categories (p = 0.92 and p = 0.82). Dietary restriction increased from 24.1% in underweight to 55.5% in normal-weight and 74.2% in overweight/obesity participants; corresponding gradients were also observed for emotional eating and food-related guilt. Conclusions: In this predominantly female, largely urban online convenience sample of Romanian young adults, self-reported behavioural risk indicators related to disordered eating were common. Restriction, emotional eating and food-related guilt increased with BMI, whereas night eating and self-weighing showed no clear BMI gradient. These findings should be interpreted cautiously and should not be generalised to Romanian young men or rural populations without confirmation in more representative samples. Full article

Frozen seafood plays an important role in the global food supply, but maintaining its quality during frozen storage and cold-chain distribution remains a significant challenge. Although freezing effectively slows microbial growth and enzymatic activity, it cannot completely prevent quality deterioration. During frozen storage, seafood undergoes a series of interconnected physicochemical changes, including ice crystal growth, protein denaturation and oxidation, lipid oxidation, water redistribution, and texture deterioration. These changes gradually reduce sensory quality, nutritional value, and overall commercial acceptability. Conventional quality assessment methods, including destructive laboratory analyses and sensory evaluation, are still widely used. However, they are often labor-intensive, time-consuming, and unsuitable for rapid or real-time monitoring in modern cold-chain systems. As a result, increasing attention has been given to non-destructive sensing technologies that can evaluate seafood quality quickly and objectively. This review summarizes the major mechanisms responsible for quality deterioration in frozen seafood, together with recent advances in sensing technologies used to monitor these changes. The sensing approaches discussed include near-infrared (NIR) and Raman spectroscopy, hyperspectral and fluorescence imaging, low-field nuclear magnetic resonance (LF-NMR), electronic nose (E-nose), electronic tongue (E-tongue), colorimetric sensor arrays (CSAs), and biosensors. This review also discusses the growing role of artificial intelligence in frozen seafood quality assessment, including chemometrics, machine learning, deep learning, and multi-sensor data fusion. Particular attention is given to their applications in quality prediction, industrial implementation, and decision support. Finally, current challenges and future research needs are highlighted, with emphasis on the development of interpretable, transferable, and real-time monitoring systems that can support more reliable quality assurance throughout the frozen seafood supply chain. Full article

Plasmonic biosensing technologies have emerged as powerful analytical tools for sensitive and label-free characterisation of biomolecular interactions and complex samples. The increasing demand for comprehensive molecular profiling has accelerated the development of multiplexing strategies that enable simultaneous analysis of multiple analytes and molecular interactions. This Feature Paper examines multiplexing through the complementary spatial, spectral, and temporal dimensions of multiplexing, together with their hybrid combinations and associated analytical trade-offs. Compared with other optical biosensing approaches, including interferometric, photonic, and fluorescence-based sensing platforms, plasmonic biosensors remain attractive owing to their combination of label-free detection, real-time interaction monitoring, sensitive interfacial analysis, and compatibility with multiplexed assay formats. This Feature Paper critically discusses current multiplexing strategies, focusing primarily on surface plasmon resonance (SPR), imaging SPR (SPRi), localised SPR (LSPR), surface-enhanced Raman scattering (SERS), and related nanoplasmonic biosensing approaches, together with recent advances in surface biofunctionalisation, antifouling interfaces, and molecular recognition strategies. Representative applications in biomedical diagnostics and non-clinical settings are highlighted, with examples such as liquid biopsy, glycoprofiling, extracellular vesicle profiling, and food and environmental analysis, alongside key challenges in reproducibility, standardisation, data interpretation, and clinical translation. In addition, selected non-plasmonic optical biosensing technologies are briefly discussed to position plasmonic biosensing within the broader landscape of multiplexed optical biosensing. This Feature Paper argues that the future of multiplexed plasmonic biosensing will depend less on further improvements in sensor performance than on robust, standardised analytical systems. Full article

Accurate crop classification is essential for sustainable agriculture activities and food security studies. Recent advancements in remote sensing data acquisition and analysis techniques enable various solutions for cropland detection; however, reliable crop maps are still lacking in many heterogeneous semi-arid regions (e.g., Central Asia). Machine learning approaches address such challenges and distinguish different crop types using multiple datasets. The main aim of this study is to optimize crop classification outcomes by integrating multi-sensor datasets leveraging numerous vegetation indices through different machine learning models. Four datasets: Landsat-8 (DS-1), Sentinel-2 (DS-2), optical Sentinel-2 integrated with SAR Sentinel-1 (DS-3), and Sentinel-1 (DS-4) were used for the developed experiments. Five vegetation indices, NDVI, GNDVI, EVI, SAVI, and MSAVI, were derived using Sentinel-2 and Landsat-8 bands; in addition, NDRE was only obtained for Sentinel-2 exploiting the red edge band. Three input scenarios were considered for model training and image classification, featuring solely NDVI and its related bands; a set of vegetation indices and their associated bands for optical imagery; and VV, VH, and VV/VH ratio bands for SAR data. Five classifiers, Gradient Boosting Tree (GBT), Random Forest (RF), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Minimum Distance (MD), were employed to assess the machine learning quality for scene classification. Findings demonstrated that Sentinel-2 outperforms Landsat-8 images due to the higher spatial resolution and red edge bands. DS-3 consistently outperforms both DS-2 (optical-only) and DS-4 (SAR-only) across all classifiers, enhancing the overall accuracy up to 2.38% over the optical dataset, and up to 13.28% over the SAR data, demonstrating the added details on canopy spectral reflectance, structure and moisture content. Using multiple vegetation indices consistently improves performance over NDVI alone across DS-1, DS-2, and DS-3, with gains reaching up to 96.22% due to the complementary information captured by multi-index spectral sensitivity. The GBT and RF classifiers consistently achieved the highest classification performance, effectively combining multiple decision trees to capture complex nonlinear relationships and decision boundaries; meanwhile, the MD classifier exhibited the lowest accuracy due to its reliance solely on distances to class mean vectors. All in all, the presented approach offers a robust framework for crop classification supplemented with multiple data sources using different VI feature scenarios and variable machine learning tools for precise farming applications in semi-arid regions. Full article

Indigenous grains, such as pearl millet, can contribute to alleviating food insecurity in South Africa and addressing the health and well-being of consumers. Stigmatisation and a lack of consumer knowledge about indigenous grains have been found to stifle the consumption of these grains. Therefore, this study aimed to explore South African consumers’ perceived understanding and experience of pearl millet and the external product attributes that attract their attention and steer them towards purchasing and consuming this indigenous grain. This interpretivist phenomenological exploratory study used a qualitative methodology. Small synchronous online focus groups were used to gather the data. The thematic analyses revealed that consumers are generally perceived as uncertain about and lacking experience in the use and consumption of pearl millet, which results from factors such as product unfamiliarity, a lack of knowledge, the product being overlooked in stores, and various assumptions about the grain. Consumers’ perceived willingness to purchase pearl millet may be influenced by several product attraction indicators, including price, packaging, whether it is produced locally, retailer image, and quality-enhancement attributes, which marketers and product developers in South Africa should use to grow consumer interest. This study contributes towards changing the consumer perceived approach to pearl millet by identifying the factors that hamper and limit the choice of pearl millet consumption. South Africa can ill afford to neglect marketing and improving consumer awareness of pearl millet if the health and well-being of South African consumers are to be improved. Full article

Background/Objectives: Early detection of breast cancer (BC) substantially improves outcomes, with a 5-year survival rate of 99% for early-stage disease. Risk-based screening, which tailors recommendations according to individual clinical, personal, and lifestyle factors, is a promising strategy to enhance early detection. However, previous studies have reported inconsistent evidence of association with BC risk for several modifiable lifestyle factors including premenopausal adiposity, sedentary behaviour, oral contraceptive use. To address these uncertainties, we investigated the association between clinical, personal, and modifiable lifestyle factors and BC risk in the Prevention, Imaging, Network, Knowledge (P.I.N.K.) project. P.I.N.K. generated real-world evidence by integrating clinical data with information collected through a structured questionnaire evaluating the associations between BC and clinical, personal, and lifestyle factors in 27,123 Italian women aged ≥ 40 years, who underwent integrated breast imaging diagnostics. Methods: Characteristics of women with and without BC were compared. Adjusted logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest models were applied to identify relevant predictors. Variables selected by at least two methods were included in a multivariate logistic regression model, with BC diagnosis as the outcome. Results: Accordingly with previous studies a consistent association was found for high breast density, late menopause, overweight, abdominal adiposity, cardiovascular-cerebrovascular and oncological comorbidities and smoking. Greater consumption of plant-based foods, lower intake of red and processed meat, and regular physical activity were associated with lower odds of BC as well. Conversely, an unexpected inverse association was assessed for menstrual irregularities, hyperlipidaemia, and oral contraceptive use. Conclusions: The integration of multiple sources of information both clinical and self-reported data collected directly from women adds to the growing evidence on the role of modifiable lifestyle factors in BC risk in supporting more targeted prevention strategies. Future prospective studies should further investigate the long-term effects of lifetime physical activity trajectories, different oral contraceptive formulations, and detailed reproductive history, including menstrual cycle characteristics and menopausal timing, to refine individualized BC risk prediction. Full article

Background/Objectives: Dance is a unique and physically demanding athletic art form that necessitates optimal nutritional intake for performance and long-term health. Nutrition knowledge (NK) is an essential factor that drives dietary behaviours and attitudes of athletes and performers, including dancers. Despite its importance, there appears to be limited research investigating the NK of dancers. This scoping review therefore aims to systematically explore the currently published literature on the NK of dancers globally, to understand the current levels of nutrition knowledge and gaps in knowledge as well as related dietary behaviours and inform future research and resource development for this population. Methods: Four electronic databases (MEDLINE, CINAHL, PubMed, and Web of Science) were searched in June 2026 for articles. Eligibility criteria included: Original research published in peer-reviewed journals between 2000–2025. Only English language studies reporting on the NK (general, sports, specific) of dancers were included. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was followed. Results: Of the 58 studies initially identified, a total of nine studies were included. All were quantitative and either cross-sectional (n = 5) or intervention (n = 4) studies. Data was collated from six countries with a total of 942 participants, and NK was directly assessed in all of the reviewed papers. The methodological quality and bias were assessed using the “Academy of Nutrition and Dietetics Quality Criteria Checklist for Primary Research” tool. Study designs and tools used to assess NK were heterogenous. Overall, dancers demonstrated low-to-moderate NK, with gaps in knowledge surrounding macronutrient composition of foods, and exhibited restrictive dietary patterns and body image concerns. Reliance on non-expert information sources was prevalent. Interventions designed to improve NK and related dietary behaviours showed positive impacts. Conclusions: Dancers’ NK is low-to-moderate, with suboptimal dietary behaviours, often driven by aesthetic pressures and a reliance on informal nutrition advice. Given the success of interventions to improve NK, future research should focus on targeted nutrition education strategies, focused on identified NK gaps for optimising dancers’ health, performance, and long-term well-being. Full article

Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. However, existing research often overlooks parameters reflecting crop population structure and fails to account for dynamic shifts in the contributions of multidimensional agronomic variables across growth stages, thereby limiting prediction accuracy and model stability. To address these limitations, a winter wheat yield estimation model was developed. This model integrates multi-source and multi-temporal data, incorporates stem tiller density, a key population structure parameter, and accounts for dynamic variation across growth stages. Unmanned aerial vehicle multi-spectral images were collected at four key growth stages: jointing (stem elongation with detectable nodes), booting (flag leaf sheath swelling preceding heading), heading (spike emergence) and filling (grain filling with dry matter accumulation). Three growth indicators, stem tiller density, leaf area index and above-ground biomass, were measured. Two comprehensive growth indicators were derived using the coefficient of variation and the CRITIC weighting methods, respectively (CGI_CV and CGI_CR). Correlation and feature importance analyses were used to identify sensitive vegetation indices (VIs), which were subsequently integrated with the comprehensive growth indicators. Single-stage, multi-source feature fusion and multi-temporal yield estimation models were established using the Kernel Extreme Learning Machine and its optimised algorithm using the Crested Porcupine Optimizer. The results showed the following: (1) among the individual growth stages, features from the filling stage achieved the highest prediction accuracy; (2) the fusion of multi-source features (VIs + CGI_CR) enhanced the prediction accuracy of the model, achieving a validation set R² of 0.884 and a relative prediction deviation of 2.916 at the filling stage; and (3) the multi-temporal model further improved predictive performance, with the validation R² reaching 0.920, indicating that information from different growth stages contributed complementarily to yield prediction and improved overall model performance. By contrast, the model exhibited relatively weak predictive capability at the early growth stages and was better-suited to early risk identification. Meanwhile, its generalisation ability under cross-regional and inter-annual conditions still requires further validation. Overall, integrating multi-source and multi-temporal data can enhance the precision and stability of predicting winter wheat yield, thereby facilitating precision agriculture management. Full article