Search Results (863)

A systematic review was conducted on the migration of compounds from plastic containers in the food and pharmaceutical industries, using Web of Science databases and following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The protocol has been registered with the OSF registry, with the DOI 10.17605/OSF.10/UQ3T2. This review included only review articles in English published within the last fifteen years. Four reviewers independently screened titles and abstracts, discussing inclusion criteria. In this comprehensive evaluation of the information present in an Excel spreadsheet, a substantial number of records were discarded because they were not representative of the topic under study. Following the review process, a total of twenty-eight key records were selected, primarily focusing on migration in the food and pharmaceutical sectors. Of these, twenty-four addressed only food, just two addressed only pharmaceutical sector, and two covered both fields, highlighting limited information on migration in pharmaceuticals, cosmetics, and related products. The analysis emphasized the types of compounds studied, the analytical methods employed, the migration tests conducted, and the toxicity assessments undertaken. The most frequently assessed compounds included phthalates, endocrine disruptors like bisphenol A, and non-intentionally added substances (NIAS). Analytical methods used typically involved pre-treatment steps, such as liquid–liquid or solid-phase extraction, followed by gas or liquid chromatography, depending on compound volatility. Full article

The fatty acid composition of some seed oils from plants of emerging interest was studied. The benefits towards human health were evaluated by taking into account current recommendations regarding dietary intake of essential, polyunsaturated, and monounsaturated fatty acids and by discussing the pathologies for which such fatty acids exert protective action. Species studied were Hemp (Cannabis sativa), Flax (Linum usitatissimum), Milk Thistle (Silybum marianum), Perilla (Perilla frutescens), Borage (Borago officinalis), and Black Cumin (Nigella sativa). Seeds were subjected to cold milling in order to maintain their original nutritional characteristics. Chemical analyses were performed via the dual-detector gas chromatography technique by means of Flame Ionization Detection (FID) and mass spectrometry (MS) and by applying a modified version of the AOAC Official Method 991.39, thanks to which it was possible to obtain the fatty acid composition expressed as mg per gram of oil: such information is not always available in the literature for the species studied here. Comparison with the fatty acid international guidelines about the recommended intakes in g/day was made. This allowed us to evaluate whether such oils are suitable to be used as fatty acid food supplements to rebalance the Western diet, which is shown to be inadequate by numerous studies. Results show that seed oils from Cannabis sativa, Linum usitatissimum, and Perilla frutescens are suitable to be used as food supplements while seed oils from Silybum marianum, Borago officinalis, and Nigella sativa are not. It is important to note that any possible benefits from other parts of the plant (leaves, stems, flowers, and roots) are not studied or questioned by the present research, which focuses solely on fatty acids in the oil extracted from the seeds. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article

Farmland abandonment poses a significant threat to China’s food security by contributing to inefficient land use. Utilizing remote sensing data and the multiple cropping index extraction method, this study extracts abandonment cropland information and analyzes its spatiotemporal patterns across China, with its findings validated against the “China Rural Revitalization Survey” (CRRS) data. The results indicate that since the 21st century, China’s cropland abandonment rate has fluctuated around 5.86%, affecting an average of 7.6 million hectares annually. Spatially, cropland abandonment is more severe in southern China, with hotspots clustered around 25° N and 30° N latitudes. This southward shift exacerbates the spatial mismatch between water resources and cropland. Furthermore, abandonment is particularly pronounced in grain production—marketing balance areas and main marketing areas, intensifying pressure on national food self-sufficiency. Slope and fragmentation also drive abandonment, with steeper (>15°) and more fragmented plots showing higher susceptibility. These complex patterns are uncovered through the study’s systematic innovations—a dual-indicator quantification method, a multi-source validation framework, a dynamic spatiotemporal atlas, and a novel interpretation of abandonment multifunctionality, which also positions farmland reuse as a buffer against unemployment risks. We thus recommend addressing land fragmentation as a core strategy, through high-standard farmland construction, innovative contract rights certification, and expanded agricultural socialized services to promote moderate-scale farming. Finally, we urge the adoption of region-specific and category-based recultivation approaches, supported by clear governance priorities. Full article

Currant pomaces were valorised using food-grade supercritical CO₂ to examine how pre-drying (convective vs. freeze-drying) and species (black vs. red currant) shape extract composition and antioxidant readouts. Total phenolics (TPCs), DPPH capacity, tocopherols and fatty acids were determined; statistics employed included the Welch test with Holm adjustment and one-way ANOVA. Blackcurrant showed consistently higher TPCs than redcurrant, whereas DPPH responses were maximised in freeze-dried redcurrant. Freeze-drying increased PUFAs and concomitantly lowered SFAs within both species, with MUFAs varying within a narrower band and tending to be higher in blackcurrant. Tocopherol profiles in residues displayed homologue- and species-specific redistribution (e.g., α higher after convective drying in blackcurrant; γ/δ preferentially retained after freeze-drying), consistent with microstructure-dependent mass transfer and homologue-specific partitioning during SFE. Collectively, pre-drying emerged as the principal lever to tailor lipid class balance and antioxidant performance under fixed extraction conditions. Practically, freeze-drying is suited to PUFA-rich, antioxidant-active fractions, whereas convective drying favours more oxidation-resilient profiles. These results support process-informed ingredient design for clean-label applications and motivate yield-normalised mass balances and scale-up studies. Full article

Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy of crop distribution identification in complex environments in three ways. First, by analysing the kNDVI and EVI time series, the optimal identification window was determined to be days 156–176—a period when wheat is in the grain-filling to milk-ripening phase and maize is in the jointing to tillering phase—during which, the strongest spectral differences between the two crops occurs. Second, principal component analysis (PCA) was applied to Sentinel-2 data. The top three principal components were extracted to construct the input dataset, effectively integrating visible and near-infrared band information. This approach suppressed redundancy and noise while replacing traditional RGB datasets. Finally, the Convolutional Block Attention Module (CBAM) was integrated into the U-Net model to enhance feature focusing on key crop areas. An improved Atrous Spatial Pyramid Pooling (ASPP) module based on deep separable convolutions was adopted to reduce the computational load while boosting multi-scale context awareness. The experimental results showed the following: (1) Wheat and corn exhibit obvious phenological differences between the 156th and 176th days of the year, which can be used as the optimal time window for identifying their spatial distributions. (2) The method proposed by this research had the best performance, with its mIoU, mPA, F1-score, and overall accuracy (OA) reaching 83.03%, 91.34%, 90.73%, and 90.91%, respectively. Compared to DeeplabV3+, PSPnet, HRnet, Segformer, and U-Net, the OA improved by 5.97%, 4.55%, 2.03%, 8.99%, and 1.5%, respectively. The recognition accuracy of the PCA dataset improved by approximately 2% compared to the RGB dataset. (3) This strategy still had high accuracy when predicting wheat and corn yields in Qitai County, Xinjiang, and had a certain degree of generalisability. In summary, the improved strategy proposed in this study holds considerable application potential for identifying the spatial distribution of wheat and corn in arid regions. Full article

Background/Objectives: This study aimed to develop and validate a Dietary Practices and Food Safety Literacy Scale comprehensively assessing competencies among Korean older adults in healthy dietary practices, hygiene, and food safety. Methods: Item development was informed by a literature review, national dietary guidelines, and existing literacy frameworks. Content validity was reviewed by a 10-member expert panel using the Delphi method. Construct validity was tested using exploratory factor analyses (EFA) and confirmatory factor analyses (CFA), and reliability was assessed through Cronbach’s α, composite reliability (CR), and average variance extracted (AVE). Results: EFA identified three factors—management, decision-making, and moderation competencies—comprising 13 items. Internal consistency was acceptable (α = 0.69–0.83), and CFA supported the three-factor structure (CFI = 0.919, RMSEA = 0.087). CR values exceeded 0.70, and AVE were close to or exceeded the recommended threshold. Conclusions: The scale demonstrates sound psychometric properties and provides a practical tool for identifying competency gaps in Dietary Practices and Food Safety Literacy. Its application may guide tailored health education and community-based interventions to promote healthy aging and support public health strategies in aging societies. By translating health information literacy into measurable, behavior-oriented domains, this tool bridges the gap between theoretical constructs and practical assessment. It can be incorporated into routine health monitoring, enabling policymakers and practitioners to design evidence-based interventions that enhance older adults’ dietary self-management and food safety awareness. Full article

Background/Objective: Temporal eating patterns and ultra-processed food (UPF) consumption have independently been associated with obesity and non-communicable diseases. Little is known about the temporal patterns of UPF consumption, as data is challenging to collect. Temporal data can be extracted from mobile food records (mFRs). The aim of this study was to identify the temporal eating patterns of those consuming UPFs using an mFR. Methods: A combined sample of 243 young (18–30 years) and 148 older (>30 years) adults completed a 4-day mFR. The time of eating was extracted from the mFR image metadata. UPFs were identified using the NOVA food classification system. The proportion of total energy intake (EI) from UPFs was calculated hourly. Using chi-square tests, a day-of-the-week analysis compared weekends (Friday–Sunday) with weekdays (Monday–Thursday). A multivariate logistic regression of UPF EI terciles was conducted, expressed as odds ratios and 95% confidence intervals. Results: The proportion of total EI from UPFs was significantly different between younger adults (mean ± SD = 48.8 ± 15.6%) and older adults (36.1 ± 15.1%) (p < 0.001). Age-differentiated 24 h temporal eating pattern analysis found that younger adults had two distinct UPF EI peaks, with the highest at 8 pm, followed by 1 pm. Older adults followed a more conventional three-meal pattern with an additional peak at 7 am. Weekend UPF EI was higher than on weekdays for older adults (~560 kJ, p = 0.003), with no difference for younger adults. Multivariable logistic regression found no significant associations between UPF intake terciles and demographic variables (sex, BMI, education). Conclusions: The peak UPF EI occurred at conventional mealtimes, and UPFs accounted for a substantial proportion of energy intake, especially for younger adults. The timing of UPF EI provides important information for developing public health nutrition interventions. Full article

Jin’er (Naematelia aurantialba), commonly known as golden ear, is a traditional edible fungus that has long been recognized for its medicinal and culinary properties in China. Recently, it has been registered as a new cosmetic ingredient, drawing significant attention across various fields, including medicine, food, and cosmetics, due to its array of nutritional and medicinal benefits. N. aurantialba is rich in bioactive compounds, such as polysaccharides, dietary fiber, polyphenols, and active peptides. Among these, N. aurantialba polysaccharides (NAPs) are the primary active components, exhibiting a range of biological properties, including antioxidant, hypoglycemic, immunomodulatory, intestinal flora modulatory, anti-tumor, and anti-inflammatory effects. This comprehensive review summarizes the latest advancements in the extraction, purification, structural characteristics, functional activity, and related functional mechanisms of NAPs, as well as their industrial applications. Additionally, it discusses the current limitations in NAPs research and explores its potential future research directions. This review aims to provide up-to-date information and valuable references for researchers and industry professionals interested in the potential application of NAPs in the fields of food, medicine, healthcare, and cosmetics. Full article

Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting detailed cropland information. However, due to the diverse morphological characteristics of croplands across different agricultural landscapes, existing deep learning methods encounter challenges in precise boundary localization. The advancement of large-scale vision models has led to the emergence of the Segment Anything Model (SAM), which has demonstrated remarkable performance on natural images and attracted considerable attention in the field of remote sensing image segmentation. However, when applied to high-resolution cropland extraction, SAM faces limitations in semantic expressiveness and cross-domain adaptability. To address these issues, this study proposes a dual-branch framework integrating SAM and a semantically aware network (SAM-SANet) for high-resolution cropland extraction. Specifically, a semantically aware branch based on a semantic segmentation network is applied to identify cropland areas, complemented by a boundary-constrained SAM branch that directs the model’s attention to boundary information and enhances cropland extraction performance. Additionally, a boundary-aware feature fusion module and a prompt generation and selection module are incorporated into the SAM branch for precise cropland boundary localization. The former aggregates multi-scale edge information to enhance boundary representation, while the latter generates prompts with high relevance to the boundary. To evaluate the effectiveness of the proposed approach, we construct three cropland datasets named GID-CD, JY-CD and QX-CD. Experimental results on these datasets demonstrated that SAM-SANet achieved mIoU scores of 87.58%, 91.17% and 71.39%, along with mF1 scores of 93.54%, 95.35% and 82.21%, respectively. Comparative experiments with mainstream semantic segmentation models further confirmed the superior performance of SAM-SANet in high-resolution cropland extraction. Full article

Marine-derived bioactive peptides (MBPs) are emerging as promising natural agents for regulating inflammatory responses. MBPs, typically obtained through enzymatic hydrolysis of proteins from various marine organisms such as fish, mollusks, and algae, exhibit diverse biological activities, including antioxidant, immunomodulatory, and anti-inflammatory effects. The ability of MBPs to modulate key inflammatory mediators such as TNF-α, IL-6, and COX-2, primarily through pathways like NF-κB and MAPK, highlights the therapeutic potential of MBPs in managing chronic inflammatory diseases. However, most existing studies are confined to in vitro assays or animal models, with limited translation to human clinical applications. This review explores the stability, bioavailability, and metabolic rate of MBPs under physiological conditions, which remain poorly understood. In addition, a lack of standardized protocols for peptide extraction, purification, and efficacy evaluation hinders comparative analysis across studies and also different proteomics approaches for separation, purification, identification, and quantification of marine-derived peptides with therapeutic properties. The structure–function relationship of MBPs is also underexplored, limiting rational design and targeted applications in functional foods or therapeutic products. These limitations are largely due to a lack of consolidated information and integrated research efforts. To address these challenges, this review summarizes recent progress in identifying MBPs with anti-inflammatory potentials, outlines key mechanisms, and highlights current limitations. Additionally, this review also emphasizes the need to enhance mechanistic understanding, optimize delivery strategies, and advance clinical validation to fully realize the therapeutic potential of MBPs. Full article

Background: Antimicrobial drugs are used to treat bacterial diseases in livestock production systems, including bovine respiratory disease (BRD) in feedlot cattle. It is recommended that therapeutic antimicrobial use (AMU) in food animals be informed by diagnostic tests to limit the emergence of antimicrobial resistance (AMR) and preserve the effectiveness of available drugs. Recent evidence demonstrates preliminary support for the pen as a prospective target for AMR testing-based interventions in higher-risk cattle. Methods: A previously reported agent-based model (ABM) was modified and then used in this study to investigate the potential for different pen-level sampling and laboratory testing-informed BRD treatment strategies to favorably impact selected antimicrobial stewardship and management outcomes in the western Canadian context. The incorporation of sample testing to guide treatment choice was hypothesized to reduce BRD relapses, subsequent AMU treatments and resultant AMR in sentinel pathogen Mannheimia haemolytica. The ABM was extended to include a discrete event simulation (DES) workflow that models the testing process, including the time at sample collection (0 or 13 days on feed) and the type of AMR diagnostic test (antimicrobial susceptibility testing or long-read metagenomic sequencing). Candidate testing scenarios were simulated for both a test-only control and testing-informed treatment (TI) setting (n = 52 total experiments). Key model outputs were generated for both the pen and feedlot levels and extracted to data repositories. Results: There was no effect of the TI strategy on the stewardship or economic outcomes of interest under baseline ecological and treatment conditions. Changes in the type and number of uses by antimicrobial class were observed when baseline AMR in M. haemolytica was assumed to be higher at feedlot arrival, but there was no corresponding impact on subsequent resistance or morbidity measures. The impacts of sample timing and diagnostic test accuracy on AMR test positivity and other outputs were subsequently explored with a theoretical “extreme” BRD treatment protocol that maximized selection pressure for AMR. Conclusions: The successful implementation of a pen-level sampling and diagnostic strategy would be critically dependent on many interrelated factors, including the BRD treatment protocol, the prevalences of resistance to the treatment classes, the accuracy of available AMR diagnostic tests, and the selected “treatment change” thresholds. This study demonstrates how the hybrid ABM-DES model can be used for future experimentation with interventions proposed to limit AMR risk in the context of BRD management. Full article

Background/Objectives: The Jiangnan diet—a traditional dietary pattern prevalent in Eastern China—is a newly proposed dietary pattern. This study provides additional epidemiological evidence for the promotion of the Jiangnan diet through examining the association between the Jiangnan diet and mild cognitive impairment (MCI). Methods: A multicenter cross-sectional study was carried out during 2020 among 1084 community-dwelling adults aged 55 years and above across multiple sites in Zhejiang Province, China. Data collection encompassed basic information of the population, cognition (using the Montreal Cognitive Assessment), dietary intake information (using the Food Frequency Questionnaire, FFQ), life pattern, depressive symptoms (using the Mental Health Assessment Scale for the Elderly), and physical examinations (e.g., height, weight). The dietary patterns were assessed using a validated semi-quantitative FFQ. Factor analysis was used to analyze the 16 categories of food intake of the participants, and dietary patterns and the “Jiangnan diet” were extracted. The Jiangnan diet scores were categorized into quartiles: Q1 (lowest) to Q4 (highest). Multivariate logistic regression was employed to examine the association between adherence to the Jiangnan diet and the prevalence of MCI, with results expressed as odds ratios (OR) and 95% confidence intervals (CI). Results: The estimated prevalence of MCI in the study population was 24.6%. The dietary pattern characterized by whole grains, low salt, and low oil was identified as the “Jiangnan diet”. Participants with the highest adherence to the “Jiangnan diet” pattern had 79.2% lower odds of MCI than those with the lowest adherence (odds ratio = 0.208, 95% CI = 0.120~0.362, p < 0.0001) after adjusting for age, frequency of social activities, depression, hypertension, alcohol consumption, and energy intake. Conclusions: High adherence to the Jiangnan diet was associated with lower odds of MCI. To further verify the relationship between the Jiangnan diet and MCI, future studies will focus on longitudinal research exploring different dietary patterns and disease outcomes across various regions. Full article

Cranberry (Vaccinium macrocarpon Ait.) is an herbaceous, evergreen, dwarf shrub of the genus Vaccinium in the family Ericaceae, often used as a functional food. Cranberries are primarily distributed in the northern United States—including Massachusetts, Wisconsin, and Maine—as well as in Quebec, Canada; the state of Columbia; Chile in South America; and northeastern Europe. They are also found in China’s Greater Khingan Range and Fuyuan City, Jiamusi, Heilongjiang Province. The plants thrive in cool environments and exhibit considerable adaptability to soil conditions, preferring acidic soils. Cranberries are rich in a variety of biologically active components, such as polyphenols (proanthocyanidins, chlorogenic acid, flavonols, anthocyanins, caffeic acid, etc.), triterpenoids, and other nutrients. Studies have shown that the chemical components extracted from cranberry fruit have pharmacological effects such as antioxidant, anti-inflammatory, anti-cancer, and urinary tract infection prevention and treatment, and are commonly used clinically in the treatment of cardiovascular diseases, the prevention of urinary tract infections, blood pressure lowering, and the fight against Helicobacter pylori, among other clinical diseases. Cranberries also play a huge role in daily nutrition, and they are named for their richness in a variety of mineral elements, trace elements and vitamins. This work uses information from Pubmed, Web of Science, Scopus, CNKI (China National Knowledge Infrastructure), and related papers. In this paper, a comprehensive review of the phytochemical composition, pharmacological mechanism of action, clinical application value and nutritional significance of cranberry was conducted in recent years to provide references for the further extraction of chemical components in cranberry and rational clinical application, which can help to guide people to rationalize their diets and promote the formation of healthy diets. Full article

Oleogels are a new trend in food technology aimed at enhancing the nutritional value of products that contain solid fats. Solid fats are rich in nutritionally unfavorable saturated fatty acids (FAs), but oleogels (lipid systems derived from oleogelator and oils) are in unsaturated FAs. The use of oils rich in oxidation-prone unsaturated FAs in pastry products requires the use of an antioxidant, such as green tea extract (GTE). However, new products with altered composition should also be acceptable to consumers. The purpose of this study was to carry out a sensory evaluation of short-dough biscuits obtained with 1% of GTE and high-oleic oleogels and to find what may influence consumers’ anticipated perception. Products with monoacylglycerols (MAG) and candelilla wax (CLX) oleogels and non-gelled high-oleic rapeseed oil (HORO) were found to have equal or better sensory quality compared to products with traditional baker’s (palm) fat (PF). In terms of sensory attributes, products with ethyl cellulose oleogels were the most distinct from the other biscuits. Their sensory qualities were significantly lower, with a more noticeable rancid odor and taste. Products with CLX and MAG oleogels were more preferred than those with PF, as consumers were more likely to purchase them based on sensory impressions. Information about the presence of GTE and HORO in the formulation was shown to affect the perception of the product. Similarly, the information about the low saturated fat content encouraged consumers to purchase such products. Based on this, it can be concluded that displaying this type of information can help educate consumers, support better decision-making, and promote the selection of more nutritious options. Full article