Search Results (276)

Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L⁻¹ to low μg L⁻¹ detection limits in liquids and 10⁻³ to 10 μg cm⁻² on surfaces, with reproducibility often in the 5–15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure–response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance. Full article

Background: Women living with HIV (WLHIV) in low-income urban settings face multiple intersecting nutritional risks from food insecurity, poor dietary quality, and mental health problems. We evaluated the prevalence of household food insecurity and inadequate dietary diversity, examining their associations with depressive symptoms, antiretroviral therapy (ART)-related factors, and body mass index (BMI) among WLHIV attending routine ART clinics in Kinshasa, The Democratic Republic of Congo. This study addresses critical gaps in understanding the interplay between mental health and nutrition in the context of HIV care, with significant implications for improving health outcomes among vulnerable populations. Methods: In this clinic-based cross-sectional study (February–April 2024), we enrolled 571 women on ART in Masina 2, Kinshasa. Household food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS), dietary diversity was assessed using the Minimum Dietary Diversity for Women (MDD_W; inadequate ≤ 5 food groups in 24 h), and probable depression was assessed using the Hopkins Symptom Checklist-10 (HSCL-10), which is a validated screening tool. We obtained baseline BMIs from clinic records at ART induction, which we measured again upon survey completion. We used analysis of covariance (ANCOVA) to model follow-up BMI, adjusting for baseline values, age, ART duration, self-reported adherence, household food insecurity, dietary diversity, and probable depression. Sensitivity analyses included change-score and mixed-effects models. Results: The prevalence of any household food insecurity was high (75%; 95% CI:71.5–78.6), with 57.6% (95% CI:53.5–61.6) of the participants experiencing inadequate dietary diversity (MDD_W < 5). Furthermore, forty-two per cent (95% CI:38.4–46.5) experienced depressive symptoms and sixty-eight percent (95% CI: 64.4–72.0) adhered to antiretroviral therapy (ART). The mean MDD_W was 4.3, with a low consumption rate of animal-source foods. Baseline BMI was associated with follow-up values (adjusted β_{unstandardized}, 0.48 kg/m² per 1 kg/m² baseline, 95% CI 0.38–0.59; p < 0.001). Probable depression was independently associated with a lower follow-up BMI (adjusted β_{unstandardized}, −0.99 kg/m²; 95% CI −1.72 to −0.26; p = 0.008). Time since ART initiation showed a slight positive association with BMI (adjusted β_{unstandardized}, 0.10 kg/m² per year). Self-reported ART adherence, household food insecurity, and dietary diversity were not independently associated with follow-up BMI in fully adjusted models. The interaction between age and probable depression did not suggest heterogeneity between age groups (p = 0.503). Conclusions: In our cohort, food insecurity and poor dietary diversity were widespread but did not significantly correlate with BMI, while probable depression, a potentially modifiable factor, was independently associated with lower BMI after accounting for baseline nutritional status. These findings highlight the need for HIV care programs integrating mental health screening and services with nutrition-sensitive interventions to support recovery and long-term health among WLHIV. Full article

As one of the world’s most widely used packaging materials, paper obtains its properties from its major component: wood. Variations in the species of wood result in variations in the paper’s mechanical properties. The pulp and paper production industry is known to be a polluting industry and a consumer of a large amount of energy but remains an essential heavy industry globally. Paper production, based largely on the kraft process, is mainly intended for the food packaging sector and, thus, is associated with contamination risks. The lack of standardized regulations and the different analytical techniques used make information on the subject complex, particularly for inorganic elements where little information is available in the literature. Most research in this field is based on sample preparation using mineralization via acid digestion to obtain a liquid and homogeneous matrix, mainly with a HNO₃/H₂O₂ mixture. The most commonly used techniques are Atomic Absorption Spectrometry (AAS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), each with its advantages and disadvantages, which complicates the use of these tech-niques for routine analyses on an industrial site. In the same field of inorganic compound analysis, Microwave Plasma Atomic Emission Spectrometry (MP-AES) has become a real alternative to techniques such as AAS or ICP-AES. This technique has been used in several studies in the food and environmental fields. This publication aims to examine, for the first time, the state of the art regarding the analysis of inorganic elements in food packaging and different matrices using MP-AES. The entire manufacturing process is studied to identify possible sources of inorganic contaminants. Various analytical techniques used in the field are also presented, as well as research conducted with MP-AES to highlight the potential benefits of this technique in the field. Full article

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

Human gut Bacteroides species play crucial roles in the metabolism of dietary polysaccharides. Polyguluronate (PG), a major component of alginate, has been widely used in the food and medical industries. However, how PG is utilized by human gut Bacteroides species has not been fully elucidated. Here, using a combination of culturomics, genomics, and state-of-the-art analytical techniques, we elucidated in detail the utilization profiles of PG by 17 different human gut Bacteroides species. Our results indicated that each Bacteroides species exhibited a unique capability for PG utilization. Among all species tested, Bacteroides xylanisolvens consumed the highest amount of PG and produced the greatest quantity of short-chain fatty acids, suggesting that it may be a keystone bacterium in PG utilization. Mass spectrometry showed that PG was degraded by B. xylanisolvens into a series of oligosaccharides. Genomic analyses confirmed that B. xylanisolvens possesses a large and divergent repertoire of carbohydrate-active enzymes. Moreover, genomic annotation identified two enzymes, PL17_2 and PL6_1, in B. xylanisolvens that are potentially responsible for PG degradation. Altogether, our study provides novel insights into PG utilization by human gut Bacteroides species, which has important implications for the development of carbohydrate-based drugs from marine resources. Full article

Early and accurate detection of plant diseases is essential for ensuring food security and maintaining sustainable agricultural productivity. However, most deep learning models for plant disease classification rely heavily on large-scale annotated datasets, which are expensive, labor-intensive, and often impractical to obtain in real-world farming environments. To address this limitation, we propose a unified self-supervised learning (SSL) framework that leverages unlabeled plant imagery to learn meaningful and transferable visual representations. Our method integrates three complementary objectives—Bootstrap Your Own Latent (BYOL), Masked Image Modeling (MIM), and contrastive learning—within a ResNet101 backbone, optimized through a hybrid loss function that captures global alignment, local structure, and instance-level distinction. GPU-based data augmentations are used to introduce stochasticity and enhance generalization during pretraining. Experimental results on the challenging PlantDoc dataset demonstrate that our model achieves an accuracy of 77.82%, with macro-averaged precision, recall, and F1-score of 80.00%, 78.24%, and 77.48%, respectively—on par with or exceeding most state-of-the-art supervised and self-supervised approaches. Furthermore, when fine-tuned on the PlantVillage dataset, the pretrained model attains 99.85% accuracy, highlighting its strong cross-domain generalization and practical transferability. These findings underscore the potential of self-supervised learning as a scalable, annotation-efficient, and robust solution for plant disease detection in real-world agricultural settings, especially where labeled data is scarce or unavailable. Full article

Urban public space is a crucial constituent of livable city construction. A pleasant and comfortable public space is not simply spacious, bright, and accessible but also subjectively preferred by citizens who use it. Efforts to understand how citizens experience and perceive therein thus matters and would significantly aid urban design and well-being improvement. This research constructs a perception lexicon for 129 sites of public street space, a significant type of public space, in Shanghai and identifies how citizens comment on these sites through sentiment analysis based on social platform texts. A Chinese natural language processing (NLP) tool is applied to sort out the extent of citizens’ feelings on the urban street environment through a 0–1 scoring system. Six types of built environment elements and five categories of urban public spaces are identified. Pleasantly perceived sites primarily locate in the urban center and sporadically distribute in the outskirts and are normally “high-density” and “multi-function” in nature. Among the five categories of urban public spaces, sites that are commercially dynamic with culture, arts, and historical elements or that have gourmet food and good walkability generally receive the higher sentiment scores, but scores of ancient town commercial streets (many are antique streets), once popular and contributing much to tourism economy, are not satisfactory. The NLP-based text analysis also quantifies the intensity of emotional perceptions toward the six types of built environment elements and their associations with the general perception. This study not only offers insights for designers and policy makers in public space optimization but also showcases a scalable, data-driven approach for integrating public emotional and experiential dimensions into urban livability assessments. Full article

Understanding the intricate relationship between sensory perception and physicochemical properties of cacao-based products is crucial for advancing quality control and driving product innovation. However, effectively integrating these heterogeneous data sources poses a significant challenge, particularly when sensory evaluations are derived from low-quality, subjective, and often inconsistent annotations provided by multiple experts. We propose a comprehensive framework that leverages a correlated chained Gaussian processes model for learning from crowds, termed MAR-CCGP, specifically designed for a customized Casa Luker database that integrates sensory and physicochemical data on cacao-based products. By formulating sensory evaluations as regression tasks, our approach enables the estimation of continuous perceptual scores from physicochemical inputs, while concurrently inferring the latent, input-dependent reliability of each annotator. To address the inherent noise, subjectivity, and non-stationarity in expert-generated sensory data, we introduce a three-stage methodology: (i) construction of an integrated database that unifies physicochemical parameters with corresponding sensory descriptors; (ii) application of a MAR-CCGP model to infer the underlying ground truth from noisy, crowd-sourced, and non-stationary sensory annotations; and (iii) development of a novel localized expert trustworthiness approach, also based on MAR-CCGP, which dynamically adjusts for variations in annotator consistency across the input space. Our approach provides a robust, interpretable, and scalable solution for learning from heterogeneous and noisy sensory data, establishing a principled foundation for advancing data-driven sensory analysis and product optimization in the food science domain. We validate the effectiveness of our method through a series of experiments on both semi-synthetic data and a novel real-world dataset developed in collaboration with Casa Luker, which integrates sensory evaluations with detailed physicochemical profiles of cacao-based products. Compared to state-of-the-art learning-from-crowds baselines, our framework consistently achieves superior predictive performance and more precise annotator reliability estimation, demonstrating its efficacy in multi-annotator regression settings. Of note, our unique combination of a novel database, robust noisy-data regression, and input-dependent trust scoring sets MAR-CCGP apart from existing approaches. Full article

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard environments due to factors such as occlusion, background clutter, and varying lighting conditions. This study proposes the Depthwise Asymmetric Bottleneck with Attention Mechanism Network (DABAMNet), an advanced convolutional neural network (CNN) architecture composed of multiple Depthwise Asymmetric Bottleneck Units (DABou), specifically designed to improve apple segmentation in RGB imagery. The model incorporates the Convolutional Block Attention Module (CBAM), a dual attention mechanism that enhances channel and spatial feature discrimination by adaptively emphasizing salient information while suppressing irrelevant content. Furthermore, the CBAM attention module employs multiple global pooling strategies to enrich feature representation across varying spatial resolutions. Through comprehensive ablation studies, the optimal configuration was identified as early CBAM placement after DABou unit 5, using a reduction ratio of 2 and combined global max-min pooling, which significantly improved segmentation accuracy. DABAMNet achieved an accuracy of 0.9813 and an Intersection over Union (IoU) of 0.7291, outperforming four state-of-the-art CNN benchmarks. These results demonstrate the model’s robustness in complex agricultural scenes and its potential for real-time deployment in fruit detection and harvesting systems. Overall, these findings underscore the value of attention-based architectures for agricultural image segmentation and pave the way for broader applications in sustainable crop monitoring systems. Full article

Food image recognition is a key research area in food computing, with applications in dietary assessment, menu analysis, and nutrition monitoring. However, imaging devices and environmental factors introduce noise, limiting classification performance. To address this, we propose a food image recognition method based on anti-noise learning and covariance feature enhancement. Specifically, we design a Noise Adaptive Recognition Module (NARM), which incorporates noisy images during training and treats denoising as an auxiliary task to enhance noise invariance and recognition accuracy. To mitigate the adverse effects of noise and strengthen the representation of small eigenvalues, we introduce Eigenvalue-Enhanced Global Covariance Pooling (EGCP) into NARM. Furthermore, we develop a Weighted Multi-Granularity Fusion (WMF) method to improve feature extraction. Combined with the Progressive Temperature-Aware Feature Distillation (PTAFD) strategy, our approach optimizes model efficiency without adding overhead to the backbone. Experimental results demonstrate that our model achieves state-of-the-art performance on the ETH Food-101 and Vireo Food-172 datasets. Specifically, it reaches a Top-1 accuracy of 92.57% on ETH Food-101, outperforming existing methods, and it also delivers strong results in Top-5 on ETH Food-101 and both Top-1 and Top-5 on Vireo Food-172. These findings confirmed the effectiveness and robustness of the proposed approach in real-world food image recognition. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Lactic acid bacteria (LAB) are central to food, feed, and health biotechnology, yet their genomes have long resisted rapid, precise manipulation. This review charts the evolution of LAB genome-editing strategies from labor-intensive RecA-dependent double-crossovers to state-of-the-art CRISPR and CRISPR-associated transposase systems. Native homologous recombination, transposon mutagenesis, and phage-derived recombineering opened the door to targeted gene disruption, but low efficiencies and marker footprints limited throughput. Recent phage RecT/RecE-mediated recombineering and CRISPR/Cas counter-selection now enable scar-less point edits, seamless deletions, and multi-kilobase insertions at efficiencies approaching model organisms. Endogenous Cas9 systems, dCas-based CRISPR interference, and CRISPR-guided transposases further extend the toolbox, allowing multiplex knockouts, precise single-base mutations, conditional knockdowns, and payloads up to 10 kb. The remaining hurdles include strain-specific barriers, reliance on selection markers for large edits, and the limited host-range of recombinases. Nevertheless, convergence of phage enzymes, CRISPR counter-selection and high-throughput oligo recombineering is rapidly transforming LAB into versatile chassis for cell-factory and therapeutic applications. Full article

The art of food–drink pairing has always fascinated gourmets and cooking enthusiasts. While wine has long held pride of place on the table, natural mineral water plays a central role in this new concept. Through the Pyramid of Natural Mineral Waters, we aim to explore the relationships between the structure of water and food, flavors and aromas, revealing a world of previously unexplored nuances and tastes. This new approach is based on the analysis of the fixed residue of water, i.e., the amount of mineral salts dissolved in it. The fixed residue gives the water unique organoleptic characteristics, influencing the perception of flavors and sensations in the mouth. By analyzing the technical data sheet of mineral waters designed by us, it is possible to identify their main characteristics and combine them in a consistent way with various dishes, as proposed in the pyramid scheme. There are many possible combinations between natural mineral waters and foods, depending on numerous factors, including the type of water and the salts dissolved in it, the type of food, the cooking method, and the types of sauces and condiments present in the dish. To guide consumers in this fascinating universe, the figure of the water sommelier, or so-called hydro-sommelier, was born. As expert connoisseurs of natural mineral waters, they are able to recommend the ideal water for every occasion, maximizing the taste characteristics of the food served at the table. This study is completed with the construction of the Pyramid of Natural Mineral Waters, which relates the composition of water, specifically the salient characteristics related to dissolved minerals, with the respective food combinations recommended by us, in relation to the structure of both water and food. Full article

Oleogels are a subclass of organogels that present a healthier alternative to traditional saturated and trans solid fats in food products. The unique structure and composition that oleogels possess make them able to provide desirable sensory and textural features to a range of food products, such as baked goods, processed meats, dairy products, and confectionery, while also improving the nutritional profiles of these food products. The fact that oleogels have the potential to bring about healthier food products, thereby contributing to a better diet, makes interest in the subject ever-increasing, especially due to the global issue of obesity and related health issues. Research studies have demonstrated that oleogels can effectively replace conventional fats without compromising flavor or texture. The use of plant-based gelators brings about a reduction in saturated fat content, as well as aligns with consumer demands for clean-label and sustainable food options. Oleogels minimize oil migration in foods due to their high oil-binding capacity, which in turn enhances food product shelf life and stability. Although oleogels are highly advantageous, their adoption in the food industry presents challenges, such as oil stability, sensory acceptance, and the scalability of production processes. Concerns such as mixed consumer perceptions of taste and mouthfeel and oxidative stability during processing and storage evidence the need for further research to optimize oleogel formulations. Addressing these limitations is fundamental for amplifying the use of oleogels and fulfilling their promise as a sustainable and healthier fat alternative in food products. As the oleogel industry continues to evolve, future research directions will focus on enhancing understanding of their properties, improving sensory evaluations, addressing regulatory challenges, and promoting sustainable production practices. The present report summarizes and updates the state-of-the-art about the structure, the properties, and the applications of oleogels in the food industry to highlight their full potential. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article