Search Results (557)

Although fermented seasonings play a pivotal role in improving food quality, the high sodium content of many traditional products poses considerable public health concerns, including hypertension and cardiovascular disease. This study established a low-salt fermentation strategy for Mumashan chili by regulating water activity (a_w) under NaCl concentrations ranging from 4 to 12% (w/w). The a_w-regulated system effectively maintained a_w within ± 0.03 at both 25 and 40 °C, thereby sustaining stable microbial activity despite the reduced salt concentration. Compared with the control group 15% NaCl, the 4% NaCl treatments exhibited significantly higher total acidity (130–200 g/kg vs. 24–58 g/kg) and a faster consumption rate of reducing sugars, with MH12 achieving an 80% rate of reducing sugars by day 21. Sensory evaluation revealed a higher overall quality score for the low-salt chili mash (MH12, 7.7/10), which was associated with a balanced aroma profile and enhanced color stability (ΔE < 5). However, the elevated relative abundance of opportunistic pathogens (Klebsiella app., ~10%) highlights the necessity of strict raw material hygiene. Overall, these results validate the feasibility of a_w regulation for low-salt fermentation, elucidate the associations between microbial communities and flavor development, and provide a basis for future industrial applications. Full article

Growing interest in food sustainability education aims to increase awareness of food distribution systems, environmental degradation, and the connectivity of sustainable and ethical food practices. However, recent scholarship has questioned whether such pedagogical efforts are meaningfully internalized by students or lead to sustained behavioral change. Prior studies document persistent gaps in students’ understanding of sustainability impacts and the limited effectiveness of existing instructional approaches in promoting transformative engagement. To address these concerns, the Food Ethics, Sustainability and Alternatives (FESA) course was implemented with 21 undergraduate and graduate students at Montclair State University (Montclair, NJ, USA). Course outcomes were evaluated using a mixed-methods design integrating qualitative analysis with quantitative measures informed by the Theory of Planned Behavior, to identify influences on students’ attitudes, and a Transtheoretical Model (TTM) panel survey to address progression from awareness to action, administered pre- and post-semester. Qualitative findings revealed five central themes: increased self-awareness of food system contexts, heightened attention to animal ethics, the importance of structured classroom dialogue, greater recognition of food waste, and increased openness to alternative food sources. TTM results indicated significant reductions in contemplation and preparation stages, suggesting greater readiness for change, though no significant gains were observed in action or maintenance scores. Overall, the findings suggest that while food sustainability education can positively shape student attitudes, the conversion of attitudinal shifts into sustained behavioral change remains limited by external constraints, including time pressures, economic factors, culturally embedded dietary practices, structural tensions within contemporary food systems, and perceptions of limited individual efficacy. Full article

Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection. It examines UAV platforms, sensor technologies, flight protocols, image preprocessing pipelines, DL architectures, and existing datasets, and it evaluates how these components interact within UAV–DL disease-monitoring frameworks. The review also compares model performance across convolutional neural network-based and Transformer-based architectures, highlighting metrics such as accuracy, recall, F1-score, inference speed, and deployment feasibility. Persistent challenges—such as limited UAV-acquired datasets, annotation inconsistencies, geographic model bias, and inadequate real-time deployment—are identified and discussed. Finally, the paper proposes a structured research agenda including lightweight edge-deployable models, UAV-ready benchmarking protocols, and multimodal data fusion. This review provides a consolidated reference for researchers and practitioners seeking to develop practical and scalable cassava-disease detection systems. Full article

Sustainability constitutes a strategic priority not only at the level of practical implementation but also within the framework of legal regulations and policy-making processes. Within the scope of this study, the forest-related legal frameworks of selected countries from Asia and Europe have been examined. To ensure consistency and objectivity in the analysis, a set of evaluation criteria was established, with particular attention paid to their international recognition and legitimacy. In this context, the criteria developed by the Food and Agriculture Organization and Forest Europe were adopted. Based on these internationally accepted standards, the forest legislation of the selected countries was assessed and analyzed using the EFLD (Environmental and Forest Law Department) scoring methodology to determine the extent to which sustainability is integrated into their legal systems. Six criteria were defined and evaluated separately for each country based on the overall average. It was concluded that Türkiye and Kazakhstan’s forest legislation aligns with sustainability criteria compared to other countries’ legislation, Lithuania and Iran’s forest legislation is close to the overall average, and Poland’s forest legislation requires more explicit and progressive provisions in terms of sustainability. Full article

This study investigates a mechanically aerated, yeast-free bread technology incorporating apple-derived plant ingredients (juice, purée, and powder) in response to the growing demand for clean-label bakery products. The global bakery sector represents one of the largest food markets worldwide, with the baking yeast segment alone accounting for several billion USD annually, while interest in yeast-free and yeastless-dough products continues to expand. To address technological limitations associated with yeast exclusion, dough aeration was achieved using a two-stage whipping protocol (1000 rpm for 4 min, followed by 500 rpm for 1 min and stabilization at 500 rpm for 1 min under 4.0 ± 0.1 MPa gauge pressure), forming a stable protein–carbohydrate foam system. Rheological evaluation using Mixolab 2 showed that formulations containing 3–5% apple purée exhibited the most favorable dough development characteristics, with stability increasing from 3.30 ± 0.15 min in the control to 8.90 ± 0.20 min. Texture profiling using a CT-2 analyzer equipped with a cylindrical probe (50% compression, 60 mm/min, slices 25 mm thick, n = 5) revealed a significant reduction in crumb firmness, from 3.01 ± 0.15 N in the control to 2.12 ± 0.10 N in the purée- and powder-enriched samples (p < 0.05). Nutritional assessment indicated improvements in vitamin C content (up to 2.23 mg/100 g) and protein quality: the amino acid score, calculated according to FAO/WHO reference patterns on a mg/g-protein basis, increased from 76.5 ± 1.8% to 89.2 ± 2.3%. Microbiological analysis showed reduced total aerobic mesophilic counts after 72 h of storage—4.7 × 10³ CFU/g in the control versus 1.8–3.4 × 10³ CFU/g in apple-enriched breads. Overall, the results demonstrate that mechanical aeration combined with apple-derived ingredients enhances the structural, nutritional, and microbiological quality of yeast-free bread, offering a promising clean-label approach for functional bakery products. Full article

Small-scale farms produce more than one-third of the world’s food supply, making them a crucial contributor to global food security. In this study, an artificial intelligence of things (AIoT) framework is introduced for smart small-scale farm management. For plant disease detection, the lightweight MobileViT model, which integrates vision transformer and convolutional modules, was utilized to efficiently capture both global and local image features. Data augmentation and transfer learning were employed to enhance the model’s overall performance. MobileViT resulted in a test accuracy of 99.5%, with per-class precision, recall, and f1-score ranging between 0.92 and 1.00 considering the benchmark Plant Village dataset (14 species–38 classes). MobileViT was shown to outperform several standard deep convolutional networks, including MobileNet, ResNet and Inception, by 2–12%. Additionally, an LLM-powered interactive chatbot was integrated to provide farmers with instant plant care suggestions. For plant environment management, the powerful, cost-effective ESP32 microcontroller was utilized as the core processing unit responsible for collecting sensor data (e.g., soil moisture), controlling actuators (e.g., water pump for irrigation), and maintaining connectivity with Google Firebase Cloud. Finally, a mobile application was developed to integrate the AI and IoT system capabilities, hence providing users with a reliable platform for smart plant disease detection and environment management. Each system component was each tested individually, before being incorporated into the mobile application and tested in real-world scenarios. The presented AIoT-based solution has the potential to enhance crop productivity within small-scale farms while promoting sustainable farming practices and efficient resource management. Full article

Background/Objectives: Under the urban–rural dual structure, rural children’s health security faces multiple challenges. These stem from geographical disadvantages, inadequate resources, and systemic flaws in medical insurance design. The outpatient cost-sharing policy is a key design to address these issues. Methods: Using data from the 2018 China Household Income Project (CHIP), this study employs Propensity Score Matching, Ordered Probit, Logit, and a Two-Part Model to examine the association between the policy and the health and economic outcomes of rural children. Conclusions: The results show that the policy is significantly associated with better child health scores and a higher probability of reimbursement. These positive associations appear to be connected to three potential factors: higher use of outpatient services, better mother’s health, and greater school-related food and accommodation expenses. In contrast to adult populations, no significant substitution between outpatient and inpatient services was observed for children, suggesting the non-discretionary and rigid nature of pediatric hospitalization decisions. This research provides robust empirical evidence for the policy’s potential benefits, offering important implications for optimizing the child medical security system. Full article

Rural and unincorporated communities (UCs) experience persistent health disparities driven by limited healthcare infrastructure, geographic isolation, and socioeconomic inequities. Health literacy (HL), the ability to obtain, understand, and use health information, is a critical yet underexplored determinant of health outcomes in these settings. This study examined HL and barriers to healthcare and health information access among low-income adults living with chronic conditions in nine rural UCs in Southern California. A descriptive cross-sectional survey was administered in English or Spanish to 222 respondents during community food distribution events. The questionnaire included demographics, self-reported health status, chronic disease history, perceived access to care and health information, trust in information sources and HL assessment using the Newest Vital Sign (NVS). Over four-fifths (82.7%) of respondents demonstrated limited or possibly limited HL. Although Spanish-speaking respondents scored significantly lower than English speakers on the NVS, language was not a significant predictor of HL after adjusting for age, gender, education and Hispanic origin. Lower education and older age were associated with reduced HL. One in four respondents reported barriers to healthcare access, primarily due to distance and appointment availability. Over half of the respondents reported difficulty accessing or understanding health information, regardless of HL or demographic characteristics. Doctors were the most trusted source of health information, while trust in government and religious organizations was lowest. Findings reveal pervasive low HL and broad challenges accessing care and health information across rural UCs, highlighting the structural and educational inequities underlying these disparities. Addressing these gaps requires community-driven, bilingual, and culturally resonant strategies that build trust, enhance communication, and strengthen health system accessibility for residents of unincorporated rural regions. Full article

Food recommender systems are pivotal in helping people make optimal dietary choices based on tremendous amounts of data. Extant studies offer different methods and techniques, but the combination of similarity search, large language models (LLMs), and multi-criteria decision-making (MCDM) remains underexplored. This study proposes a new system that leverages all three. First, we utilize an LLM to suggest queries from the same domain as the dish database. Then, the queries are vectorized and used for similarity search to generate a preliminary list of suggested menu items. Next, multiple LLMs provide scores for each item, which become the MCDM inputs, where Lin’s concordance correlation coefficient (LCCC) enhances the weighted sum scalarization technique. We evaluated the prototype on three publicly available dish datasets and at classification thresholds of 0.25, 0.50, and 0.75, and the proposed domain-adaptation approach consistently outperformed the baseline query. For example, at the 0.50 threshold, precision ranged from 49.11% to 56.60%, compared with 35.40% for the baseline. Furthermore, aggregating multiple LLMs mitigates single-model bias in recommendations. To substantiate this, a bootstrap evaluation of the proposed LCCC-based consensus weighting confirms that both the estimated weights and the induced rankings are numerically stable under sampling perturbations. To further ensure the robustness and reliability of the proposed system, we validate the results against other established weighting schemes and state-of-the-art MCDM methods. Moreover, Kendall’s $\tau$-based comparisons across weighting schemes and multiple MCDM methods confirm that the proposed LCCC-based framework produces highly consistent and statistically significant rankings, demonstrating strong robustness to methodological choices. This paper contributes a system architecture and design that can be adopted for other domains of recommender systems where the capability of multiple LLMs can benefit complex and multifaceted decision-making processes. Full article

Background: Childhood obesity is a global issue, influenced by energy-dense foods and powerful cues that affect brain areas regulating food intake. The olfactory system, linked to food preferences and consumption, is inversely related to body mass index. However, no studies have assessed the possible effect of eating behavior traits on the relationship between olfactory capacity and obesity. Objectives: The aim of this study was to examine whether olfactory capacity, eating behavior traits, and body mass index are associated with obesity in adolescents. Methods: An analysis of 204 Chilean adolescents was undertaken in a cross-sectional study. The proportion of participants with normal weight was found to be 39.2%, that of overweight was 25.9%, and that of obesity was 34.8%. Anthropometric measurements (weight, height, BMI Z-score), eating behavior, and olfactory capacity were evaluated. The Child Eating Behavior Questionnaire (CEBQ) and Food Reinforcement Value Questionnaire (FRVQ) were used to assess eating behavior. The Sniffing sticks test was used to assess olfactory capacity. Results: In the global sample, 1.0% had anosmia, 20.5% had hyposmia, 61.0% had normosmia, and 17.5% were supersmellers. Girls showed higher odor identification percentages than boys (p = 0.01). No gender differences were found in olfactory threshold, discrimination, identification, or TDI (threshold–discrimination–identification) scores, nor nutritional status. Stratified analysis revealed that girls with obesity had significantly lower odor discrimination capacity compared to those with normal weight. Conclusions: the study highlights a potential link between olfactory function and obesity, with obese girls showing reduced odor discrimination compared to normal-weight girls. Further research is needed to explore these mechanisms and their implications for targeted obesity interventions. Full article

Food ingredient prediction from images is a challenging multi-label classification task with significant applications in dietary assessment and automated recipe recommendation systems. This task is particularly difficult due to highly imbalanced classes in real-world datasets, where most ingredients appear infrequently while several common ingredients dominate. In such imbalanced scenarios, the F-score metric is often used to provide a balanced evaluation measure. However, existing methods for training artificial neural networks to directly optimize for the F-score typically rely on computationally expensive hyperparameter optimization. This paper presents a novel approach for direct F-score optimization by reformulating the problem as cost-sensitive classifier optimization. We propose a computationally efficient algorithm for estimating the optimal relative cost parameters. When evaluated on the Recipe1M dataset, our approach achieved a micro F1 score of 0.5616. This represents a substantial improvement from the state-of-the-art method’s score of 0.4927. Our F-score optimization framework offers a principled and generalizable solution to class imbalance problems. It can be extended to other imbalanced binary and multi-label classification tasks beyond food analysis. Full article

Background: Dietary patterns influence systemic inflammation, which is involved in the pathogenesis of non-communicable diseases. The dietary inflammatory index (DII) quantifies the inflammatory potential of the diet and varies across populations due to cultural and regional eating habits. Limited data exist on the inflammatory potential of diets in Saudi Arabia. This study aimed to assess the inflammatory potential of the diet and its association with sociodemographic and lifestyle factors among adults in Saudi Arabia. Methods: A cross-sectional study included 256 adults aged 18–50 years residing in Saudi Arabia. Participants were recruited using convenience sampling via social media platforms. Data were collected between November 2024 and August 2025 using a validated Saudi food frequency questionnaire and sociodemographic survey. Energy-adjusted DII (E-DII) scores were calculated using 42 food parameters. Non-parametric tests (Mann–Whitney U and Kruskal–Wallis) were applied to examine associations between E-DII and sociodemographic variables. Results: Significant differences in E-DII were observed by sex (p < 0.001). Males had higher E-DII scores than females, suggesting more pro-inflammatory diets. Participants with postgraduate education tended to have lower E-DII than participants with only a high school degree, reflecting more anti-inflammatory dietary patterns. However, this trend was not statistically significant (p = 0.06). The mean E-DII was 4.8 ± 1.3, indicating a predominantly pro-inflammatory dietary pattern. No significant differences were found across age, education, income, BMI, marital status, employment, or smoking status. Conclusions: Sex was a key determinant of dietary inflammatory potential. Adults demonstrated overall pro-inflammatory dietary patterns in Saudi Arabia. Public health interventions should target higher risk groups, such as males with a higher risk of non-communicable diseases, to promote anti-inflammatory dietary habits and reduce chronic disease risk in this population. Full article

Teaching-based robotic systems offer an accessible alternative to complex inverse kinematics programming for food processing automation. Traditional model-based approaches require precise system identification and analytical solutions that are challenging for custom-built robots with manufacturing tolerances and mechanical uncertainties. This study developed a custom six-degree-of-freedom robotic arm using modular brushless motors controlled via Controller Area Network communication and Robot Operating System 2, a teaching mode where users manually demonstrate trajectories that are recorded at 100 Hz. Forty-five demonstration trajectories were collected across three geometric patterns (rectangle, triangle, circle) and augmented to 270 samples. A bidirectional Long Short-Term Memory network with attention mechanism was trained to classify patterns, achieving 83.33% test accuracy and outperforming baseline deep learning models (1D-CNN: 77.78%, TCN: 66.67%, GRU: 44.44%), while being marginally exceeded by Random Forest (86.11%). Rectangle patterns showed strongest recognition (78.57% F1-score), while circle patterns achieved highest performance (91.67% F1-score). However, severe overfitting was observed, with validation accuracy peaking at 85.19% at epoch 24 before degradation, indicating insufficient dataset size despite five-fold augmentation. The results demonstrate proof-of-concept feasibility for pattern recognition from limited teaching demonstrations, providing a pathway for robotic food processing without extensive programming expertise, though larger datasets and robust feedback control strategies are required for production deployment. Full article

This study examines the adoption of artificial intelligence (AI) among micro and small hospitality enterprises in Slovenia, a small EU economy where digital transformation remains limited. It explores how organisational characteristics and managers’ attitudes toward AI are related to its adoption and firms’ operating revenues. Data were collected from 286 accommodation and food-and-beverage enterprises through a structured questionnaire completed by managers or owner–managers, complemented by secondary official financial data. Using ordinary least squares regression, the analysis examined associations among organisational characteristics, managerial attitudes, AI use intention and adoption, and financial performance. The results indicate that firm size and structural features alone are not closely linked to digital transformation. AI adoption shows stronger associations with managers’ positive attitudes and with factors such as non-family ownership and smaller firm size. The overall General Attitudes toward AI Scale (GAAIS) score showed no direct relationship with revenue, but two specific items—enthusiasm for AI and recognition of business opportunities—were positively associated with higher revenues. Among AI tools, only smart text editors and CRM systems were statistically associated with revenues, suggesting that better-performing firms are more likely to use simpler, more affordable technologies. The study provides contextual evidence on behavioural and organisational dimensions of AI adoption in resource-constrained hospitality SMEs. Full article

High-elevation agricultural systems face increased risks due to climate change, thus livelihood, food security, and rural areas are threatened. In this study, a region-specific Agricultural Vulnerability Index is constructed to assess the climate vulnerability of 41 panchayats in the Lahaul and Spiti district of Himachal Pradesh, India. Using a multi-dimensional framework incorporating exposure, sensitivity, and adaptive capacity across 57 indicators, the AVI scores and spatial analyses normalised agricultural vulnerability conditions. The AVI scores ranged from 0.471 to 0.553, with Langza (0.553), Sagnam (0.551), and Lalung (0.550) being considered as the most vulnerable panchayats due to climatic extremes, seasonal instability, and limited adaptive mechanisms, while the areas of Goshal (0.471) and Khangsar (0.474) showed lower vulnerability. The agricultural vulnerability shows aspects of the multidimensional framework under ecological fragility and socio-economic constraints. Identifying spatial risk patterns makes this research instrumental in evidence-based planning for climate-resilience agriculture. Such analyses accentuate the need for an integrated approach encompassing infrastructure development, policy changes, a confluence of technologies, and community participation in building adaptive capacity for mountain farming systems. Full article