Hasil untuk "Nutrition. Foods and food supply"

N. Wani, R. A. Rather, Aiman Farooq et al.

Food waste has been identified as one of the major factors that constitute numerous anthropogenic activities, especially in developing countries. There is a growing problem with food waste that affects every part of the waste management system, from collection to disposal; finding long-term solutions necessitates involving all participants in the food supply chain, from farmers and manufacturers to distributors and consumers. In addition to food waste management, maintaining food sustainability and security globally is crucial so that every individual, household, and nation can always get food. “End hunger, achieve food security and enhanced nutrition, and promote sustainable agriculture” are among the main challenges of global sustainable development (SDG) goal 2. Therefore, sustainable food waste management technology is needed. Recent attention has been focused on global food loss and waste. One-third of food produced for human use is wasted every year. Source reduction (i.e., limiting food losses and waste) and contemporary treatment technologies appear to be the most promising strategy for converting food waste into safe, nutritious, value-added feed products and achieving sustainability. Food waste is also employed in industrial processes for the production of biofuels or biopolymers. Biofuels mitigate the detrimental effects of fossil fuels. Identifying crop-producing zones, bioenergy cultivars, and management practices will enhance the natural environment and sustainable biochemical process. Traditional food waste reduction strategies are ineffective in lowering GHG emissions and food waste treatment. The main contribution of this study is an inventory of the theoretical and practical methods of prevention and minimization of food waste and losses. It identifies the trade-offs for food safety, sustainability, and security. Moreover, it investigates the impact of COVID-19 on food waste behavior.

Akriti Taneja, Gayathri Nair, Manisha Joshi et al.

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.

T. Comunian, M. P. Silva, Clitor J F Souza

Abstract Background Continuous increase in global human population and depletion of natural resources threat to environment needs and sustainable food supply. In particular, food industries generate an extensive amount of by-products. Several studies have shown that food by-products might be suitable feedstock to recover bioactive compounds such as phenolics, carotenoids, proteins, likewise their promising technological properties. Thus, this paper aims to overview recent advances in the utilization of food by-products. Scope and approach This review deals with the main techniques for obtainment, enrichment, incorporation, and technological application of food by-products. Finally, several examples are discussed considering the application of food by-products as a bioactive source to enrich food, through encapsulation or another technological process, or as an encapsulating material. This study's main approach is to critically discuss the techno-functionality of food by-products, highlighting their potential for application in food science and technology. Key findings and conclusions There are many nutritional properties of food by-products; however, their technological properties are promising. For example, orange juice by-products can be used as encapsulating material by spray drying. The apple pomace can stabilize Pickering-type emulsions and has prebiotic potential. Saccharomyces cerevisiae can be used as an encapsulating agent to hydrophobic and hydrophilic compounds, while its extract can improve the sensory characteristics of cooked ham. Thus, by-products valorization is a challenging field of study since the circular economy concepts introduced in food science aim to ensure environmental protection and enhance economic development, reducing the environmental impact of food production.

Ho Thi Ngoc Tram, Pham Van Thinh, Truong Ngoc Minh et al.

Gamma-aminobutyric acid (GABA) is a biologically active amino acid with numerous health benefits. This study aimed to optimize the production of GABA-enriched Mang Buk brown rice through fermentation with various Lactobacillus strains. The impact of incubation temperature, duration, pH levels, and Lactobacillus concentration on GABA content were systematically investigated. The results revealed that in the presence of Lactobacillus brevis under specific conditions, GABA concentrations significantly increased to 8.16 mg/100gDW. Additionally, the condition of 35 °C and 30h of incubation consistently resulted in the highest GABA content across different Lactobacillus strains. However, the increase in the concentration of Lactobacillus involved in the fermentation process led to a rapid decline in GABA content. Simultaneously, the most suitable operating environment for yeast was determined to be at a pH range of 5–6. After fermentation under optimal conditions, the GABA content reached 24.01 mg/100g DW, representing a 141.24-fold increase compared to the initial content. These results were validated using the high-performance liquid chromatography (HPLC) method. These findings highlight the potential for tailored fermentation strategies to produce nutritionally rich GABA-enriched rice products with promising health benefits. The optimized process presents an opportunity for expanding the production of functional foods with enhanced nutritional value.

Hafiz Talha Hafeez, Hafiz Shahzad Muzammil, Zulfiqar Ahmad et al.

With the ever-increasing global population, dietary needs, and nutritional scarcities like micronutrient inadequacies of Vitamin A and C, the present study was planned to improve nutritional, antioxidant, physicochemical stability, and organoleptic characteristics of nutrient-dense carrot powder (CP) supplemented (0.75–2%) goat milk yogurt at 0–14 days of storage. CP and goat milk were found to uphold appreciable magnitudes of ash, dietary fiber, and protein content as 4.89 and 0.92 g/100g, 10.68 and 0.005 g/100g, 6.38 and 3.40 g/100g, respectively. The results for the nutritional and physicochemical characteristics of CP supplemented (0–2%) goat milk yogurt at the 0–14th day of storage elucidated a significant (p < 0.05) increase in total solids (10.9–13.3 %), vitamin A (0.05–1.97 mg/100g), vitamin C (2.3–3.3 mg/100g) and carotenoid contents (0.84–1.3 mg/100g). Likewise, total flavonoids, total phenolics, FRAP and DPPH values of the supplemented yogurt significantly (p < 0.05) enhanced from 117.7 to 120.2 mg CE/g, 8.2–12.1 mg GAE/100g, 39.8–41.3 μM TE/g and 58.3–59.6 %, respectively. However, a∗ value (i.e., redness) increased from −0.76 to 12.3 among color parameters. The results for the supplemented yogurt's pH, vitamin A, and carotenoid contents showed slight variation in the storage of 0–14 days. Sensory evaluation of value-added yogurt showed the highest aroma and textural scores for T2 (1.25 %), i.e., 8.2 and 8.1, whereas the data also showed the highest overall acceptability of the finished product for the treatment T2 (1.25 %), i.e., 8.1. Conclusively, CP is a promising carrier of vitamins A and C, which could be used as a viable ingredient of choice to develop novel food products with health-improving properties.

Solomon Abera Bariagabre, Gebre Tsegay, Amanuel Berhe et al.

Abstract This study investigated the effects of harvesting time and interval on honey yield and quality in northern Ethiopia. Two experiments were conducted. In Experiment I, bee colonies were divided based on hive type, having three modern and three traditional hives, and honey yield was measured at the end of every month from August to November. In Experiment II, 24 hives (12 modern, 12 traditional) were used to assess the effect of harvesting intervals on honey yield and quality at 30, 60, 90, and 120 days. Composite honey samples were collected from both experiments for laboratory analysis of quality parameters, including total reducing sugars, sucrose, specific gravity, moisture content, pH, hydroxymethylfurfural (HMF), mineral content, water-insoluble solids, and acidity. Data was analyzed using SPSS version 21, with ANOVA applied to evaluate the effects of harvesting time, interval, and hive type on honey yield and quality. The average monthly honey yield per hive was 11.38 ± 1.44 kg for modern and 2.77 ± 1.22 kg for traditional hives. In Experiment II, the average yield per interval was 8.70 ± 0.89 kg for extracted honey and 2.24 ± 0.75 kg for pressed honey. Honey harvested in October exhibited higher levels of reducing sugars and minerals, while moisture and HMF levels varied across months. All quality parameters met acceptable standards. Notably, differences between hive types require further elaboration. Harvestinwasme and interval were identified as the most significant factors influencing honey yield and quality.

Lingyan Zhao, Lingyan Zhao, Chenyang Qin et al.

BackgroundThe atherogenic index of plasma (AIP) is a reliable surrogate marker for insulin resistance and is strongly associated with both stroke risk and prognosis. However, the associations of AIP and the composite index AIP-BMI with cognitive function among patients with ischemic stroke remain insufficiently studied.MethodsThis cross-sectional study included 2,933 patients with ischemic stroke. Demographic and clinical data were collected from all participants. The AIP was calculated as log [TG (mmol/L)/HDL-C (mmol/L)], and cognitive function was evaluated using the Mini-Mental State Examination (MMSE). Multivariable linear regression models were applied to examine the associations between AIP (and AIP-BMI) and MMSE scores, adjusting for potential confounders. Stratified and sensitivity analyses were further conducted to evaluate the robustness of the findings.ResultsThe mean age of participants was 64.8 years (SD 10.2), and 2,009 (68.5%) were male. Each one-unit increase in AIP was associated with a 1.15-point reduction in MMSE score (p &lt; 0.001). Similarly, each one-unit increase in AIP-BMI corresponded to a 0.04-point decrease in MMSE score (p &lt; 0.001). The inverse associations remained consistent when AIP and AIP-BMI were analyzed by tertiles.ConclusionHigher levels of AIP and AIP-BMI are independently associated with poorer cognitive performance in patients with ischemic stroke. These findings suggest that dyslipidemia-related metabolic disturbances may contribute to post-stroke cognitive impairment.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=120858, identifier ChiCTR2100042721.

Yao-ru Li, Lu-hong Yang, Xuan Zhao et al.

Setaria italica seed coat represents an underutilized source of bioactive polyphenols. This study employed an integrated approach combining spatial metabolomics, computational screening, and in vitro assays to characterize its polyphenol composition and antioxidant properties. UPLC-MS/MS analysis revealed distinct metabolite profiles with numerous polyphenols significantly enriched in seed coats relative to kernels. Molecular docking identified Homoplantaginin as a high-affinity ligand for catalase, with molecular dynamics simulations confirming complex stability. In vitro antioxidant assays demonstrated concentration-dependent scavenging activity of Homoplantaginin against ABTS•+, O₂•−, and •OH radicals. These findings highlight the potential of Setaria italica seed coat as a sustainable source of natural antioxidants for functional food applications, providing scientific basis for the valorization of this agricultural byproduct.

Susan M Rattigan, Ibrahim Ngoumboute Mbouombouo, Mohamed Antar Abdou Tahirou et al.

Background: A novel ready-to-use microbiome-directed food (MDF) has been developed for the management of acute malnutrition using ingredients that promote repair of the gut microbiota of undernourished children. Objectives: This study aims to assess the acceptability of MDF compared with standard nutritional care among children with acute malnutrition. Methods: Two randomized crossover trials consisting of 2 14-d periods of at-home consumption were conducted. Children aged 6 to <24 mo with severe acute malnutrition (SAM) or moderate acute malnutrition (MAM) were individually randomized in a 1:1 ratio to the sequence of receiving MDF then standard nutritional care, or vice versa. Standard nutritional care consisted of ready-to-use therapeutic food for SAM and ready-to-use supplementary food for MAM. The primary outcome was at-home acceptability, defined as the return of ≥75% of sachets empty after the 14-d at-home consumption period. The primary analysis was a noninferiority analysis, in which MDF was considered noninferior if the lower bound of the 95% confidence interval (CI) of the difference in at-home acceptability comparing MDF with standard nutritional care was within −20 percentage points. Secondary outcomes included caregiver’s perception of the child’s liking, as well as caregiver willingness to use in the future and preference between the 2 foods. Results: In all, 128 children with SAM and 146 children with MAM were randomized. MDF was noninferior to standard nutritional care in terms of at-home acceptability among children with SAM (risk difference: −7.0; 95% CI lower bound: −11.6%) and among children with MAM (risk difference: −2.3%; 95% CI lower bound: −6.1%). There were no differences in caregiver willingness to use either food in future. Conclusions: MDF is acceptable for the management of acute malnutrition in children aged 6 to <24 mo in Niger and should be further tested in other populations with a high prevalence of acute malnutrition. Effectiveness of the novel food will be assessed in forthcoming trials. Trial registration number: This trial was registered at clinicaltrials.gov as NCT05551819.

Zixiang Zhao, Yuanjing Fu, Haiyan Wang et al.

IntroductionThe rising concerns about food security and the increasing demands for meat-based diet in China have highlighted the imbalance between the supply and demand of its feed grains. Scholars view the imports of soybeans as an unsustainable way of ensuring feed grain security. Therefore, this study investigates the optimization of soybean and pig integration zones in China.MethodsThe study has adopted a multi algorithm approach based on Random Forest Model, LSTM model, KMeans clustering, and PCA to highlight the factors influencing the integration of feed grain plantation and meat security.ResultsThe results of the PCA report that consumption, production levels, land availability, and pig quantity play an instrumental role in defining the integration of soybeans and pig farming. The results indicate that LSTM offers accurate predictions regarding the integration zones.ConclusionThe study concludes that areas with high consumption of meat and large production volumes offer an opportunity to integrate soybean cultivation and pig farming in China.

Ioannis Sarafis, Alexandros Papadopoulos, Anastasios Delopoulos

In this paper, we propose a weakly supervised semantic segmentation approach for food images which takes advantage of the zero-shot capabilities and promptability of the Segment Anything Model (SAM) along with the attention mechanisms of Vision Transformers (ViTs). Specifically, we use class activation maps (CAMs) from ViTs to generate prompts for SAM, resulting in masks suitable for food image segmentation. The ViT model, a Swin Transformer, is trained exclusively using image-level annotations, eliminating the need for pixel-level annotations during training. Additionally, to enhance the quality of the SAM-generated masks, we examine the use of image preprocessing techniques in combination with single-mask and multi-mask SAM generation strategies. The methodology is evaluated on the FoodSeg103 dataset, generating an average of 2.4 masks per image (excluding background), and achieving an mIoU of 0.54 for the multi-mask scenario. We envision the proposed approach as a tool to accelerate food image annotation tasks or as an integrated component in food and nutrition tracking applications.

Joseph T. Harrington, Vsevolod Cheburkanov, Mykyta Kizilov et al.

The increasing demands on modern plant and food production due to climate change, regulatory pressures, and the Sustainable Development Goals necessitate advanced photonic technologies for improved sustainability. Deep ultraviolet resonant Raman (DUVRR) spectroscopy offers precise spectral fingerprinting and potential disinfection capabilities, making it a promising tool for agricultural and food sciences. We developed a cost-effective, portable DUVRR spectroscopy system using a mercury (Hg) lamp as the excitation source at 253.65 \unit{\nano\meter}. The system was tested on diverse samples, including alcohol solvents, organic extracts, and industrial chemicals. The DUVRR system successfully resolved sub-1000 \unit{\per\centi\meter} Raman peaks, enabling detailed spectral fingerprints of various constituents and biomarkers. The system's high sensitivity and specificity ensure precise identification of nutritional values and food quality. The DUV light used in the system, defined here as less than 260 \unit{\nano\meter}, demonstrated potential disinfection properties, adding significant value for food safety applications. The highly sensitive detection capability of our DUVRR system at low powers has significant implications for plant and agricultural sciences. The detailed spectral information enhances the evaluation of nutritional values, food quality, and ripening processes. This dually-functional system is highly valuable for precision farming, food production, and quality control. Our DUVRR spectroscopy system provides a highly sensitive, affordable, and portable method for the spectroscopic evaluation and disinfection of food and agricultural samples. Its ability to resolve detailed Raman peaks below 1000 \unit{\per\centi\meter}, combined with DUV light's disinfection capabilities, makes it a promising tool for advancing sustainability and safety in agriculture and food production.

Xinyue Pan, Yuhao Chen, Jiangpeng He et al.

Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain both eggs and noodles as separate entities. Multi-noun food categories are common in real-world datasets and account for a large portion of entries in benchmarks such as UEC-256. These compound names often cause generative models to misinterpret the semantics, producing unintended ingredients or objects. This is due to insufficient multi-noun category related knowledge in the text encoder and misinterpretation of multi-noun relationships, leading to incorrect spatial layouts. To overcome these challenges, we propose FoCULR (Food Category Understanding and Layout Refinement) which incorporates food domain knowledge and introduces core concepts early in the generation process. Experimental results demonstrate that the integration of these techniques improves image generation performance in the food domain.

Li-tao Wang, Mu-Jie Lv, J. An et al.

Due to the growing global population, reduction in arable land and effects of climate change, incongruity between food supply and demand has become increasingly severe. Nowadays, with awareness of the elementary nutrients required for human growth, increasing attention is being paid to the health and medical functions of food. Along with increased food production achieved by modern agricultural techniques, underutilised functional foods are an important strategy for solving food security problems and maintaining the nutritional quality of the human diet. Rosa roxburghii Tratt (RRT) is a natural fruit that contains unique functional and nutritional constituents, which are characterised by a high anti-oxidant potential. This review summarises the biological characteristics, chemical composition, health-promoting properties and development status of RRT products to inspire investigations on the use of RRT fruit as a functional food, dietary supplement and pharmaceutical additive. The nutrients and functional ingredients of RRT fruit are described in detail to provide more reference information for nutritionists and pharmacists.

Abhirup Khanna, Sapna Jain, A. Burgio et al.

Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Mohammed Ahmed

Horticultural crops perform a very important role in both food and nutritional security more especially in developing countries. Fruit and vegetables have high content of both nutritional and health promoting compounds which consist mostly of essential vitamins, minerals and anti-oxidant that is not present in major staple food crops. Fruits and vegetables supply a large constituent of human nutrition, as they are vital sources of minerals, vitamins, nutrients, dietary fiber, ant-oxidant and phytochemicals that are easy to produce and have short production cycle. A diet rich in fruits and vegetables can help in reducing high blood pressure, lower the risk of heart disease and stroke, provide protection against most types of cancer, reduce the risk of eye and digestive problems, and have a significant effect controlling blood sugar levels which serve as appetite booster. The higher the amount of daily intake of fruits and vegetables, the lower the risk of developing cardiovascular diseases. Fruits and vegetables also contain fiber that assist in bowel movements and can relieve or prevent constipation. Eating fruits and vegetables can also keep eyes healthy and may help prevent two common eye diseases like cataracts and macular degeneration. The FAO, IFAD, UNICEF, WFP and WHO report on state of World food and nutrition security for the year 2021 painted a gloomier picture. The report estimated that between 720 and 811 million people in the world faced hunger in 2020 with close to 2.37 billion people did not have access to adequate food in 2020. Thus the need to encourage the production of horticultural crops can help in enhancing food and nutritional security since it is an enterprise that maximizes small space in terms of value and health benefits.

EFSA Panel on Plant Health (PLH), Claude Bragard, Paula Baptista et al.

Abstract The EFSA Panel on Plant Health performed a pest categorisation of Bailey's rust mite, Calepitrimerus baileyi Keifer (Acariformes: Eriophyidae), following the commodity risk assessment of Malus domestica plants from Türkiye performed by EFSA, in which C. baileyi was identified as a pest of possible concern to the European Union. This mite is not listed in Annex II of Commission Implementing Regulation (EU) 2019/2072. The eriophyid is known to occur in Africa, America, Asia, Europe (Greece and Serbia) and Oceania on Malus spp., which is the only confirmed host genus for C. baileyi. Plants for planting of Malus spp. are the main potential pathway for entry into the EU. However, plants for planting of the genus Malus Mill. are considered as high‐risk plants (EU 2018/2019) and therefore prohibited from entering the EU unless granted a country‐specific derogation. This is the case for the import of Malus spp. plants for planting from Serbia ((EU) 2020/1361 corrected by 2022/1309). Therefore, this derogation could provide a plausible entry pathway for C. baileyi into the EU. Climatic conditions and the ample availability of the host, Malus spp., in the EU are conducive for establishment, as proven by the occurrence of C. baileyi in Greece. However, the species is not reported as having an impact in Greece, despite reports of damage outside the EU. Measures to prevent further entry and spread of C. baileyi in the EU are available. C. baileyi satisfies all the criteria that are within the remit of EFSA to assess for it to be regarded as a potential Union quarantine pest. However, uncertainties about the distribution of C. baileyi within the EU and its impact on apples in the EU are considered key and affect the confidence of conclusions for this categorisation.

Anuja Tayal, Barbara Di Eugenio, Devika Salunke et al.

We propose a dialogue system that enables heart failure patients to inquire about salt content in foods and help them monitor and reduce salt intake. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a template-based conversational dataset. The dataset is structured to ask clarification questions to identify food items and their salt content. Our findings indicate that while fine-tuning transformer-based models on the dataset yields limited performance, the integration of Neuro-Symbolic Rules significantly enhances the system's performance. Our experiments show that by integrating neuro-symbolic rules, our system achieves an improvement in joint goal accuracy of over 20% across different data sizes compared to naively fine-tuning transformer-based models.

Zuzanna Gawrysiak, Agata Żywot, Agnieszka Ławrynowicz

We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing.

Kun Fu, Ying Dai

Recognizing food images presents unique challenges due to the variable spatial layout and shape changes of ingredients with different cooking and cutting methods. This study introduces an advanced approach for recognizing ingredients segmented from food images. The method localizes the candidate regions of the ingredients using the locating and sliding window techniques. Then, these regions are assigned into ingredient classes using a CNN (Convolutional Neural Network)-based single-ingredient classification model trained on a dataset of single-ingredient images. To address the challenge of processing speed in multi-ingredient recognition, a novel model pruning method is proposed that enhances the efficiency of the classification model. Subsequently, the multi-ingredient identification is achieved through a decision-making scheme, incorporating two novel algorithms. The single-ingredient image dataset, designed in accordance with the book entitled "New Food Ingredients List FOODS 2021", encompasses 9982 images across 110 diverse categories, emphasizing variety in ingredient shapes. In addition, a multi-ingredient image dataset is developed to rigorously evaluate the performance of our approach. Experimental results validate the effectiveness of our method, particularly highlighting its improved capability in recognizing multiple ingredients. This marks a significant advancement in the field of food image analysis.