Hasil untuk "Nutrition. Foods and food supply"

Prashant Kumar, Yukiyasu Domae, Weiwei Wan et al.

The food packaging industry goes through changes in food items and their weights quite rapidly. These items range from easy-to-pick, single-piece food items to flexible, long and cluttered ones. We propose a replaceable bit-based gripper system to tackle the challenge of weight-based handling of cluttered food items. The gripper features specialized food attachments(bits) that enhance its grasping capabilities, and a belt replacement system allows switching between different food items during packaging operations. It offers a wide range of control options, enabling it to grasp and drop specific weights of granular, cluttered, and entangled foods. We specifically designed bits for two flexible food items that differ in shape: ikura(salmon roe) and spaghetti. They represent the challenging categories of sticky, granular food and long, sticky, cluttered food, respectively. The gripper successfully picked up both spaghetti and ikura and demonstrated weight-specific dropping of these items with an accuracy over 80% and 95% respectively. The gripper system also exhibited quick switching between different bits, leading to the handling of a large range of food items.

Xinyue Pan, Yuhao Chen, Fengqing Zhu

Real-world meal images often contain multiple food items, making reliable compositional food image generation important for applications such as image-based dietary assessment, where multi-food data augmentation is needed, and recipe visualization. However, modern text-to-image diffusion models struggle to generate accurate multi-food images due to object entanglement, where adjacent foods (e.g., rice and soup) fuse together because many foods do not have clear boundaries. To address this challenge, we introduce Prompt Grafting (PG), a training-free framework that combines explicit spatial cues in text with implicit layout guidance during sampling. PG runs a two-stage process where a layout prompt first establishes distinct regions and the target prompt is grafted once layout formation stabilizes. The framework enables food entanglement control: users can specify which food items should remain separated or be intentionally mixed by editing the arrangement of layouts. Across two food datasets, our method significantly improves the presence of target objects and provides qualitative evidence of controllable separation.

Seungmin Lee

Supplemental Nutrition Assistance Program (SNAP) aims to improve food security of low-income households in the U.S. A new, continuous food security measure called the Probability of Food Security (PFS), which proxies for the official food security measure but is implementable on longer periods, enables the study of SNAP's effects on the intensive margin. Using variations in state-level SNAP administrative policies as an instrument for individual SNAP participation, I find that SNAP does not have significant effects on estimated food security on average, both on the entire population and low-income population whom I defined as income is below 130\% of poverty line at least once during the study period. I find SNAP has stronger positive effects on those whose estimated food security status is in the middle of the distribution, but has no significant effects in the tails of the distribution.

Ahmad AlMughrabi, Umair Haroon, Ricardo Marques et al.

Accurate food volume estimation is crucial for dietary monitoring, medical nutrition management, and food intake analysis. Existing 3D Food Volume estimation methods accurately compute the food volume but lack for food portions selection. We present VolTex, a framework that improves \change{the food object selection} in food volume estimation. Allowing users to specify a target food item via text input to be segmented, our method enables the precise selection of specific food objects in real-world scenes. The segmented object is then reconstructed using the Neural Surface Reconstruction method to generate high-fidelity 3D meshes for volume computation. Extensive evaluations on the MetaFood3D dataset demonstrate the effectiveness of our approach in isolating and reconstructing food items for accurate volume estimation. The source code is accessible at https://github.com/GCVCG/VolTex.

Umair Haroon, Ahmad AlMughrabi, Thanasis Zoumpekas et al.

Accurate food volume estimation is crucial for medical nutrition management and health monitoring applications, but current food volume estimation methods are often limited by mononuclear data, leveraging single-purpose hardware such as 3D scanners, gathering sensor-oriented information such as depth information, or relying on camera calibration using a reference object. In this paper, we present VolE, a novel framework that leverages mobile device-driven 3D reconstruction to estimate food volume. VolE captures images and camera locations in free motion to generate precise 3D models, thanks to AR-capable mobile devices. To achieve real-world measurement, VolE is a reference- and depth-free framework that leverages food video segmentation for food mask generation. We also introduce a new food dataset encompassing the challenging scenarios absent in the previous benchmarks. Our experiments demonstrate that VolE outperforms the existing volume estimation techniques across multiple datasets by achieving 2.22 % MAPE, highlighting its superior performance in food volume estimation.

Jing Lan, Yongzhao Xiong, Kuo Dang et al.

Small peptides, particularly tripeptides, play a crucial role in food umami taste. To dig for more umami tripeptides, the novel tripeptides pharmacophore model was established to rapidly screen umami peptides from natural tripeptide database. Twenty peptides with potential umami characteristics from 8000 tripeptides were further screened by molecular docking. The electronic tongue analysis and sensory evaluation suggested that the 20 tripeptides exhibited umami taste characteristics. The thresholds of the 20 tripeptides spanned from 0.137 to 2.237 mmol/L. Molecular dynamics simulations were used on T1R1 and four tripeptides with high umami intensity to reveal their taste mechanisms. In this study, a new screening strategy was established and 20 new umami tripeptides were identified and validated, providing a theoretical reference for rapid screening of umami tripeptides.

Xiulan Xie, Jiasui Zhan, Maozhi Ren

ABSTRACT Human civilization is threatened by food insecurity and habitat loss owing to the cumulative effects of anthropogenic and natural factors. Thus, increasing food production while using fewer resources and exploring the potential of interstellar migration is essential. Particularly, microalgae can fulfil the biological, nutritional, and efficiency requirements of industrial food production on Earth and other potential planets. Herein, we discuss the industrial production of microalgae on Earth and in outer space, along with the technological advances that will help reshape the genetic and chemical properties of microalgae for better production, nutrition, and adaptation. We propose the concept of “multiplanetary farming” to address future requirements for agricultural development. This perspective review is intended to stimulate a broad debate and research on this paramount issue for the future of mankind.

Zikun Chen, Xuequan Hou, Binyu Chen et al.

Abstract The skeletal system is vital to human health and is influenced by factors such as age and nutritional intake. Although existing studies have identified certain associations between dietary factors and orthopedic diseases, systematic analyses and theoretical perspectives remain insufficient. To address this, we present ONMR, the largest platform using Mendelian Randomization to investigate the impact of dietary intake on orthopedic disorders. By systematically integrating Genome-Wide Association Study (GWAS) data to provide over 100,000 analyses between 210 nutritional items and 503 bone-related phenotypes, ONMR provides a comprehensive framework for understanding the complex interactions between diet and skeletal health. This extensive analysis has elucidated the dual effects of dietary intake on bone health and their age-dependent characteristics. As a pivotal resource for interdisciplinary research spanning nutritional science and orthopedics, this platform could significantly contribute to the advancement of precision medicine in health management. The ONMR supports data querying, downloading, and personalized analysis, which can be accessed via a user-friendly website at https://onmr.ai-bio.net .

Evripidis P. Kechagias, Sotiris P. Gayialis, Nikolaos Panayiotou et al.

This paper addresses the critical and urgent need to reduce food losses and waste (FLW) resulting from stringent marketing standards. It proposes a comprehensive and actionable framework grounded in the three pillars of sustainability—environmental, economic, and social—to effectively evaluate FLW across the entire food supply chain. The paper involves a thorough review of existing marketing standards, including research on FLW due to marketing standards, and proposes the implementation of targeted key actions within four key food sectors: fruits, vegetables, dairy, and cereals. The study provides a detailed analysis of the significant impact marketing standards have on FLW at various stages of the supply chain, including primary production, processing, retail, and consumption. By focusing on these critical points, the research underscores the necessity of addressing marketing standards to achieve meaningful reductions in FLW. The proposed framework aims to foster improved business practices and drive the development of innovative, sector-specific solutions that balance sustainability goals with economic viability. The holistic approach followed for this research lays the foundation for ensuring that the proposed framework is adaptable and practical, leading to measurable improvements in reducing FLW and promoting sustainability across the food industry.

Gautham Vinod, Jiangpeng He, Zeman Shao et al.

Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods. However, accurate portion estimation remains a major challenge due to the loss of 3D information in the 2D representation of foods captured by smartphone cameras or wearable devices. In this paper, we propose a new framework to estimate both food volume and energy from 2D images by leveraging the power of 3D food models and physical reference in the eating scene. Our method estimates the pose of the camera and the food object in the input image and recreates the eating occasion by rendering an image of a 3D model of the food with the estimated poses. We also introduce a new dataset, SimpleFood45, which contains 2D images of 45 food items and associated annotations including food volume, weight, and energy. Our method achieves an average error of 31.10 kCal (17.67%) on this dataset, outperforming existing portion estimation methods. The dataset can be accessed at: https://lorenz.ecn.purdue.edu/~gvinod/simplefood45/ and the code can be accessed at: https://gitlab.com/viper-purdue/monocular-food-volume-3d

Marios Mertzanidis, Alexandros Psomas, Paritosh Verma

Food waste and food insecurity are two closely related pressing global issues. Food rescue organizations worldwide run programs aimed at addressing the two problems. In this paper, we partner with a non-profit organization in the state of Indiana that leads \emph{Food Drop}, a program that is designed to redirect rejected truckloads of food away from landfills and into food banks. The truckload to food bank matching decisions are currently made by an employee of our partner organization. In addition to this being a very time-consuming task, as perhaps expected from human-based matching decisions, the allocations are often skewed: a small percentage of the possible recipients receives the majority of donations. Our goal in this partnership is to completely automate Food Drop. In doing so, we need a matching algorithm for making real-time decisions that strikes a balance between ensuring fairness for the food banks that receive the food and optimizing efficiency for the truck drivers. In this paper, we describe the theoretical guarantees and experiments that dictated our choice of algorithm in the platform we built and deployed for our partner organization. Our work also makes contributions to the literature on load balancing and balls-into-bins games, that might be of independent interest. Specifically, we study the allocation of $m$ weighted balls into $n$ weighted bins, where each ball has two non-uniformly sampled random bin choices, and prove upper bounds, that hold with high probability, on the maximum load of any bin.

Pengkun Jiao, Xinlan Wu, Bin Zhu et al.

Large Multi-modal Models (LMMs) have significantly advanced a variety of vision-language tasks. The scalability and availability of high-quality training data play a pivotal role in the success of LMMs. In the realm of food, while comprehensive food datasets such as Recipe1M offer an abundance of ingredient and recipe information, they often fall short of providing ample data for nutritional analysis. The Recipe1M+ dataset, despite offering a subset for nutritional evaluation, is limited in the scale and accuracy of nutrition information. To bridge this gap, we introduce Uni-Food, a unified food dataset that comprises over 100,000 images with various food labels, including categories, ingredients, recipes, and ingredient-level nutritional information. Uni-Food is designed to provide a more holistic approach to food data analysis, thereby enhancing the performance and capabilities of LMMs in this domain. To mitigate the conflicts arising from multi-task supervision during fine-tuning of LMMs, we introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach. RoDE utilizes a diverse array of experts to address tasks of varying complexity, thereby facilitating the coordination of trainable parameters, i.e., it allocates more parameters for more complex tasks and, conversely, fewer parameters for simpler tasks. RoDE implements linear rectification union to refine the router's functionality, thereby enhancing the efficiency of sparse task allocation. These design choices endow RoDE with features that ensure GPU memory efficiency and ease of optimization. Our experimental results validate the effectiveness of our proposed approach in addressing the inherent challenges of food-related multitasking.

Abedalghani Halahlah, Felix Abik, Heikki Suhonen et al.

Wood hemicelluloses have been used as a wall material for spray-dried microencapsulation of polyphenols. Nevertheless, their incomplete water solubility could negatively impact their encapsulation efficiency (EE) and the formation of a complete protective layer, which might be alleviated synergistically by combining them with carboxymethylcellulose (CMC). Here, we explored the effects of CMC addition (0.5–3.0 %, w/w of WM) on the capacity of galactoglucomannans (GGM) and glucuronoxylans (GX) to retain bioactive compounds of bilberry during spray drying; and its contribution to the formation of wall thickness. The results revealed that EE of GGM and GX increased by 4–8 % with the CMC addition at 0.5 %, but significantly declined at higher CMC concentrations. Adding CMC improved the microcapsules' antioxidant activities, surface smoothness, and solubility, but had no effect on their particle size, thermal properties, amorphous structure, or moisture content. The majority of the GGM and GX microcapsules had a hollow internal structure surrounded by continuous wall matrix with a thickness of about 2.3–2.6 μm, which increased to 3.1–3.5 μm with the addition of 0.5 % CMC. Therefore, using CMC at an optimized proportion as a co-encapsulant improved wood hemicelluloses' ability to protect bioactive compounds during spray drying and enhanced microcapsule wall formation.

LI Yuling, HUANG Ruoqi, YANG En

Deoxynivalenol （DON） is a toxic secondary metabolite produced by Fusarium， which mainly infects wheat， corn and other grains. It not only causes huge economic losses to the agricultural industry， but also has a potential threat to human and animal health. Therefore， how to efficiently remove DON from grains has always been an urgent problem. Currently， there is good development space in terms of cost and large-scale promotion of the use of microorganisms and their metabolites for biological detoxification of DON. This article makes a detailed description of the harm and detection technology of DON， as well as the research and application of the detoxification mechanism of DON by lactic acid bacteria in recent years， which provides a reference for the biological pest control of DON in grains and feedstuffs by lactic acid bacteria and its large-scale industrial application.

Abubakar Mohammed, Vidyasagar Potdar, Mohammed Quaddus

Blockchain technology (BCT) has been proven to have the potential to transform food supply chains (FSCs) based on its potential benefits. BCT promises to improve food supply chain processes. Despite its several benefits, little is known about the factors that drive blockchain adoption within the food supply chain and the impact of blockchain technology on the food supply chain, as empirical evidence is scarce. This study, therefore, explores factors, impacts and challenges of blockchain adoption in the FSC. The study adopts an exploratory qualitative interview approach. The data consist of Twenty-one interviews which were analyzed using thematic analysis techniques in NVivo (v12), resulting in identifying nine factors classified under three broad categories (Technology—complexity, compatibility, cost; Organization—organization size, knowledge; Environment—government support, competitive pressure, standardization, and compliance) as the most significant factors driving blockchain adoption in the FSC. In addition, five impacts were identified (visibility, performance, efficiency, trust, and value creation) to blockchain technology adoption. This study also identifies significant challenges of blockchain technology (interoperability, privacy, infrastructure conditions, and lack of knowledge). Based on the findings, the study developed a conceptual framework for blockchain adoption in food supply chains. The study adds to the corpus of knowledge by illuminating the adoption of blockchain technology and its effects on food supply chains and by giving the industry evidence-based guidance for developing its blockchain plans. The study provides full insights and awareness of blockchain adoption challenges among executives, supply chain organizations, and governmental agencies.

Yue Han, Jiangpeng He, Mridul Gupta et al.

Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input. Deep learning-based techniques are commonly used to perform image analysis such as food classification, segmentation, and portion size estimation, which rely on large amounts of food images with annotations for training. However, such data dependency poses significant barriers to real-world applications, because acquiring a substantial, diverse, and balanced set of food images can be challenging. One potential solution is to use synthetic food images for data augmentation. Although existing work has explored the use of generative adversarial networks (GAN) based structures for generation, the quality of synthetic food images still remains subpar. In addition, while diffusion-based generative models have shown promising results for general image generation tasks, the generation of food images can be challenging due to the substantial intra-class variance. In this paper, we investigate the generation of synthetic food images based on the conditional diffusion model and propose an effective clustering-based training framework, named ClusDiff, for generating high-quality and representative food images. The proposed method is evaluated on the Food-101 dataset and shows improved performance when compared with existing image generation works. We also demonstrate that the synthetic food images generated by ClusDiff can help address the severe class imbalance issue in long-tailed food classification using the VFN-LT dataset.

Rachid Belmeskine, Abed Benaichouche

This study proposes an approach for predicting the impacts of scenarios on food security and demonstrates its application in a case study. The approach involves two main steps: (1) scenario definition, in which the end user specifies the assumptions and impacts of the scenario using a scenario template, and (2) scenario evaluation, in which a Vector Autoregression (VAR) model is used in combination with Monte Carlo simulation to generate predictions for the impacts of the scenario based on the defined assumptions and impacts. The case study is based on a proprietary time series food security database created using data from the Food and Agriculture Organization of the United Nations (FAOSTAT), the World Bank, and the United States Department of Agriculture (USDA). The database contains a wide range of data on various indicators of food security, such as production, trade, consumption, prices, availability, access, and nutritional value. The results show that the proposed approach can be used to predict the potential impacts of scenarios on food security and that the proprietary time series food security database can be used to support this approach. The study provides specific insights on how this approach can inform decision-making processes related to food security such as food prices and availability in the case study region.

Yuehao Yin, Huiyan Qi, Bin Zhu et al.

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple public food benchmarks for multi-task learning by leveraging the instruct-following paradigm. In the second stage, we construct a multi-round conversation dataset and a reasoning segmentation dataset to fine-tune the model, enabling it to conduct professional dialogues and generate segmentation masks based on complex reasoning in the food domain. Our fine-tuned FoodLMM achieves state-of-the-art results across several food benchmarks. We will make our code, models and datasets publicly available.

Hui Yang, Xi-Xi Wan, Hui Ma et al.

BackgroundPatients with critical illness often develop low skeletal muscle mass (LSMM) for multiple reasons. Numerous studies have explored the association between LSMM and mortality. The prevalence of LSMM and its association with mortality are unclear. This systematic review and meta-analysis was performed to examine the prevalence and mortality risk of LSMM among critically ill patients.MethodsThree internet databases (Embase, PubMed, and Web of Science) were searched by two independent investigators to identify relevant studies. A random-effects model was used to pool the prevalence of LSMM and its association with mortality. The GRADE assessment tool was used to assess the overall quality of evidence.ResultsIn total, 1,582 records were initially identified in our search, and 38 studies involving 6,891 patients were included in the final quantitative analysis. The pooled prevalence of LSMM was 51.0% [95% confidence interval (CI), 44.5–57.5%]. The subgroup analysis showed that the prevalence of LSMM in patients with and without mechanical ventilation was 53.4% (95% CI, 43.2–63.6%) and 48.9% (95% CI, 39.7–58.1%), respectively (P-value for difference = 0.44). The pooled results showed that critically ill patients with LSMM had a higher risk of mortality than those without LSMM, with a pooled odds ratio of 2.35 (95% CI, 1.91–2.89). The subgroup analysis based on the muscle mass assessment tool showed that critically ill patients with LSMM had a higher risk of mortality than those with normal skeletal muscle mass regardless of the different assessment tools used. In addition, the association between LSMM and mortality was statistically significant, independent of the different types of mortality.ConclusionOur study revealed that critically ill patients had a high prevalence of LSMM and that critically ill patients with LSMM had a higher risk of mortality than those without LSMM. However, large-scale and high-quality prospective cohort studies, especially those based on muscle ultrasound, are required to validate these findings.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022379200.

Habtamu Hawaz, Mestawet Taye, Diriba Muleta

Food safety remains the main health concern in the developing countries. Thus, the major purpose of the present study was to characterize and determine antibiotic susceptibility patterns of Listeria monocytogenes from raw milk samples collected from southern Ethiopia. Two hundred and forty raw cow milk samples were collected from dairy farms and smallholder dairy producers using a simple random sampling technique and analyzed by cultural and multiplex PCR methods. The antimicrobial susceptibility profile of L. monocytogenes was evaluated using the standard disk diffusion method. Over 28% of the samples were found positive for Listeria spp., of which 17 (7.08%) isolates were identified as L. monocytogenes after morphological and biochemical confirmation. The prevalence of L. monocytogenes was 6.02% in Hawassa city, 5.56% in Dale district, and 9.41% in Arsi Negele district. L. monocytogenes was higher in the wet season (9.32%) than in the dry season (4.92%). The gene for Listeria specific 16S rRNA was detected in all the 17 examined isolates, while hlyA and iapA were only found in 11 of them. Furthermore, no isolate was identified to have the prfA, actA, or plcA genes. Antimicrobial resistance profiling revealed that all the L. monocytogenes isolates were resistant to nalidixic acid (100%), followed by erythromycin (88.24%). However, all the L. monocytogenes isolates were sensitive to vancomycin, gentamicin, and sulfamethoxazole. Raw cow milk is a potential source of L. monocytogenes and it poses a threat to human and animal health. Therefore, it is crucial that dairy producers and vendors of raw milk in the study areas should take considerable precautions to prevent Listeria species from contaminating raw fresh milk.