Hasil untuk "Food processing and manufacture"

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.

John E. Linhoss, Jordan D. Gruber, Janet C. Remus et al.

Summary: Elevated ammonia (NH₃) emissions from litter are a concern in modern broiler production and can negatively influence performance and welfare. While commercially available litter amendments are commonly used to mitigate ammonia emissions, their effectiveness is often short-lived, prompting interest in more sustainable alternatives such as biochar. The objective of this project was to investigate the effects of different biochar application treatments (surface-applied vs mixed) on NH₃ volatilizations from used broiler litter. Biochar was surface-applied to litter at rates of 0.48, 0.97, 1.46, and 1.95 kg/m2 and mixed in at rates of 7.5, 15.0, 22.5, and 30.0 % v/v. Poultry Litter Treatment® (PLT) was surface-applied at a rate of 0.73 kg/m2 and a control of non-amended litter was also included. Treatments were replicated three times using a total of thirty 13.2 L plastic vessels. A constant air flow of 1.5 LPM was supplied to each vessel and either exhausted to the atmosphere or a photoacoustic NH₃ gas analyzer. NH3 was measured in each vessel nine times daily during a 12 d study. Mixing biochar into the litter provided enhanced contact with litter profile and led to significantly lower overall NH₃ concentrations than the surface-applied treatment (126 ppm vs 146.6 ppm, respectively). The 22.5 and 30 % v/v mixed applications resulted in the lowest NH3 concentrations (P ≤ 0.05) for the biochar treatments. However, NH₃ concentrations from all the biochar application treatments were significantly higher than PLT (65.0 ppm). This study shows that mixing biochar into broiler litter can reduce NH₃ volatilization. However, it does not seem to be competitive with PLT in terms of NH₃ reductions alone.

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 .

Kexin XIANG, Bo ZHANG, Han ZHAO et al.

To investigate the migration mechanisms of water in different states and sugars in peach slices during drying, samples were unpretreated or pretreated with either 20% sucrose or 20% oligosaccharide solution impregnation. Each group was then processed using vacuum freeze-drying (FD) or vacuum freeze-drying combined with puffing drying (FP). The drying kinetics, water migration patterns, and sugar transport dynamics were systematically analyzed throughout the process. Results demonstrated that the T2 relaxation curve exhibited a leftward shift during drying, indicating progressive conversion of free water into immobilized and bound water in peach slices. Concurrently, intra-tissue moisture migrated outward, as evidenced by the decreasing free water content and evolving water state distribution. Compared to FD, FP significantly reduced drying duration while enhancing moisture removal efficiency. During FP treatment, initial moisture evaporation occurred preferentially from surface regions, while intracellular immobilized and bound water fractions progressively increased. During FP stage, accelerated accumulation of immobilized water in the peripheral tissue layers enhanced drying kinetics. In sugar-impregnated FP samples, a steep concentration gradient emerged: Sugar content exhibited a sharp decline in peripheral regions versus gradual depletion in medial zones, confirming unidirectional outward sugar migration. The sugar in the internal field of the peach slices presented with little migration change. In this study, migration from internal to external both happened to the water and sugar in the peach slices during the drying process, which was more prominent in the outer field of the peach slices and less prominent in the internal field. These findings can provide theoretical supports for the improvement of drying efficiency and quality control of the dried fruits and vegetables.

Raif Cergibozan, Emre Akusta

Food security refers to people's access to enough safe nutritious food in order to be able to lead a healthy active life. It also involves elements such as food availability and affordability, as well as people being able to access food that can be consumed healthily. Natural disasters, however, can seriously threaten food security. Disasters' effects on food security are especially more evident in countries such as Turkiye that are frequently exposed to natural disasters due to their geologic and geographical structure. For this reason, the study investigates the effects of natural disasters on food security in Turkiye. The research first creates the Food Security Index in order to estimate the effects of natural disasters on food security. The next phase follows the process of econometric analysis, which consists of three steps. Step one of the econometric analysis uses unit root tests to check the stationarity levels of the series. The second step uses the autoregressive distributed lag (ARDL) bounds test to examine the long-term relationship between natural disasters and food security. The third and final step estimates the effects of natural disasters on food security. According to the obtained results, the study shows earthquakes, storms, and floods to have a significant short- as well as long-term negative effect on food security. The overall impact of natural disasters on food security has also been determined to be negative.

Riddhi Jain, Manasi Patwardhan, Parijat Deshpande et al.

The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are inclined towards the foods from Western region. Recent attempt towards building a VQA dataset for Indian food is a step towards addressing this challenge. However, their approach towards VQA follows a two-step process in which the answer is generated first, followed by the explanation of the expected answer. In this work, we claim that food VQA requires to follow a multi-step reasoning process to arrive at an accurate answer, especially in the context of India food, which involves understanding complex culinary context and identifying relationships between various food items. With this hypothesis we create reasoning chains upon the QA with minimal human intervention. We fine-tune smaller LLMs and VLMs with auto-validated reasoning chains and further train them using reinforcement learning with larger data. With augmentation of reasoning chains, we observed accuracy improvement of an average 10 percentage points on the baseline. We provide detailed analysis in terms the effect of addition of reasoning chains for the Indian Food VQA task. Index Terms - FoodVQA, Reasoning Chains, Reinforcement Learning, Knowledge Graph.

Hassan Kazemi Tehrani, Jun Cai, Abbas Yekanlou et al.

Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods consumed across various cultures, classic food classification models have limitations due to their reliance on fixed-sized food datasets. Studies show that people consume only a small range of foods across the existing ones, each consuming a unique set of foods. Existing class-incremental models have low accuracy for the new classes and lack personalization. This paper introduces a personalized, class-incremental food classification model designed to overcome these challenges and improve the performance of food intake monitoring systems. Our approach adapts itself to the new array of food classes, maintaining applicability and accuracy, both for new and existing classes by using personalization. Our model's primary focus is personalization, which improves classification accuracy by prioritizing a subset of foods based on an individual's eating habits, including meal frequency, times, and locations. A modified version of DSN is utilized to expand on the appearance of new food classes. Additionally, we propose a comprehensive framework that integrates this model into a food intake monitoring system. This system analyzes meal images provided by users, makes use of a smart scale to estimate food weight, utilizes a nutrient content database to calculate the amount of each macro-nutrient, and creates a dietary user profile through a mobile application. Finally, experimental evaluations on two new benchmark datasets FOOD101-Personal and VFN-Personal, personalized versions of well-known datasets for food classification, are conducted to demonstrate the effectiveness of our model in improving the classification accuracy of both new and existing classes, addressing the limitations of both conventional and class-incremental food classification models.

Ali Rostami, Z Xie, A Ishino et al.

A coronavirus pandemic is forcing people to be "at home" all over the world. In a life of hardly ever going out, we would have realized how the food we eat affects our bodies. What can we do to know our food more and control it better? To give us a clue, we are trying to build a World Food Atlas (WFA) that collects all the knowledge about food in the world. In this paper, we present two of our trials. The first is the Food Knowledge Graph (FKG), which is a graphical representation of knowledge about food and ingredient relationships derived from recipes and food nutrition data. The second is the FoodLog Athl and the RecipeLog that are applications for collecting people's detailed records about food habit. We also discuss several problems that we try to solve to build the WFA by integrating these two ideas.

NI Xingting, SUN Xizhen, LIU Huaichen, CHEN Jie, HUANG Liya, XIONG Yaqing, LI Qiang, ZHANG Fan, ZHANG Xueyuan

In order to analyze the off-flavor-bran flavor substances in strong-flavor (Nongxiangxing) Baijiu, the sensory evaluation of normal and bran flavor raw liquor was conducted according to the relevant national standards, and the volatile flavor substances in normal and bran flavor raw liquor were detected by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactory-mass spectrometry (GC-O-MS) technology, and using partial least square-discriminant analysis (PLS-DA) to establish model. Important aroma compounds and important potential markers for the difference between normal and bran flavor raw liquor were screened using odor activity value (OAV) and variable importance in the projection (VIP) values, and the bran flavor substances were identified by aroma addition and omission tests. The results showed that the aroma of bran flavor in bran raw liquor were stronger compared with normal raw liquor, but the pit aroma, grains aroma, aging aroma and sour aroma were weaker. A total of 135 volatile flavor substances were detected in normal and bran raw liquor, including 64 important aroma compounds (OAV>1), and 32 important potential markers (VIP>1) causing the difference between normal and bran flavor of strong-flavor Baijiu were screened out by PLS-DA. The results of aroma addition and omission tests showed that the main cause of bran flavor of strong-flavor Baijiu was the interaction of soil flavin, diethyl succinate and ethyl benzoate.

Xiaoying CHENG, Yang ZHAN, Yanan ZHOU et al.

Objective: The aim of this study was to investigate the effect of lacidophilin on inflammation and intestinal microflora in H. pylori-induced gastritis mice. Methods: Established an H. pylori-infected mice model by orally administering an H. pylori suspension. The mice were randomly divided into a model group and a lacidophilin treatment group, which received treatment for four weeks. Gram staining, Warthin-Starry silver staining, and H. pylori immunohistochemical staining were conducted to confirm the colonization of H. pylori in the gastric mucosa of mice. Hematoxylin and eosin (HE) staining was performed to observe any histopathological changes in the gastric mucosa of mice. Immunohistochemistry was carried out to observe the expression of iNOS, IL-1β, and 3-Nitrotyrosine in the gastric mucosa of mice. Additionally, the structure of intestinal microflora was analyzed using 16S rRNA sequencing. Results: The successful establishment of H. pylori-infected mice. Lacidophilin improved the stomach histopathological morphology in mice with H. pylori-induced gastritis and inhibited iNOS, IL-1β, and 3-Nitrotyrosine expression in the gastric mucosa. The composition and structure of the gut microbiota among the lacidophilin group showed noticeable differences in comparison to the H. pylori-infected group. Lacidophilin augmented the diversity of intestinal microflora in H. pylori-infected mice by regulating the abundance of Firmicutes, Actinobacteriota, Bacteroidete, and Verrucomicrobiota. At the genus level, Lacidophilin resulted in significant growth stimulation of beneficial bacteria such as g_norank_f_Muribaculaceae, Akkermansia, and Alistipes, while suppressing the proliferation of inflammation-related bacteria such as Dubosiella, thereby improving the composition and structure of the gut microbiota. Conclusion: Lacidophilin has the potential to rebalance the composition and structure of gut microbiota that has been compromised by H. pylori infection. It may have a protective effect on mice with H. pylori-induced gastritis by promoting the growth of beneficial bacteria, reducing inflammatory reactions, and alleviating oxidative damage to the gastric mucosa.

Nidia Leticia Ruiz-Zambrano, Viridiana Tejada-Ortigoza, Sergio O. Serna-Saldívar et al.

Powder beverage bases were developed using extruded and hydrolyzed chickpea supernatant through freeze-drying. This study evaluated the effects of spray-drying and adjuvant incorporation for producing powders and supernatant fermentation with LAB strains prior freeze-drying on the carbohydrate composition, structural integrity, and physical properties of chickpea beverage base powders. All powders had low insoluble (<0.2 %) and soluble (<3 %) dietary fiber contents due to processing. Starch content decreased below 9.49 % when using adjuvants and 10.61 % during fermentation. Both treatments also reduce raffinose oligosaccharide content below 6.1 mM/100 g. Spray-drying and adjuvant addition promoted a more amorphous starch structure, enhancing solubility, while fermentation induced more crystallinity, according to FTIR data. Microscopy showed small particle clusters and lack of birefringence. SEM images revealed starch granule damage from both fermentation and spray-drying, with more uniform particles in spray-dried powders. Spray-drying resulted in the smallest particle sizes (455-457 nm) and higher absolute zeta potential (8.12-9.51), indicating greater stability. Fermentation had the opposite effect, negatively impacting the colloidal system stability. Samples had a pH close to 6, except fermented powders with a pH of 3.6, affecting colloidal stability. Fermentation significantly reduced solubility but without practical implications, as all samples had solubility values over 93 %, which is desirable for beverage-based powders. Spray-drying and fermentation altered the carbohydrate profile and structure of the powders. Fermentation prior producing powders may provide benefits, but it slightly decreases the system stability. Spray-drying can be used instead of freeze-drying to produce stable chickpea base powders that are highly soluble.

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.

Yuan Ma, Bernhard Brümmer, Xiaohua Yu

This study is aimed at assessing agricultural and environmental performance and analyzing whether observable productivity changes stem from technologically induced or environmentally induced components. Based on individual farm household data from Hubei Province covering the period 2004 to 2010, we decompose total factor productivity (TFP) into technical efficiency change (TEC), technical change (TC), scale effect (SE), and the environmentally related allocative effect (AE) as a means of evaluating environmental performance. The empirical results indicate that the average TFP decrease rate is 2.8%, which reflects the comprehensive outcome of all relevant components. Regarding direct pollution-related inputs (fertilizer and land), improving nitrogen (N) fertilizer application efficiency and land use efficiency can contribute not only to less cropland expansion and greater productivity growth but also to N loss reduction and N pollution abatement in the short and long term. Concerning indirect pollution-related inputs (labor, intermediate input, etc.), although increases in quasi-fixed inputs (labor and intermediate input) can lead to both N and productivity growth, the magnitude of the positive effects of quasi-fixed inputs on productivity cannot offset the negative effect of fertilizer on productivity; thus, more scientific and economical fertilizer application is the key to improving agricultural productivity and benefiting the environment and the ecosystem.

Feiyan Yang, Xin Wen, Siwei Xie et al.

Efficient and comprehensive analysis of lipid profiles in yak ghee samples collected from different elevations is crucial for optimal utilization of these resources. Unfortunately, such research is relatively rare. Yak ghee collected from three locations at different altitudes (S2: 2986 m; S5: 3671 m; S6: 4508 m) were analyzed by quantitative lipidomic. Our analysis identified a total of 176 lipids, and 147 s lipid of them were upregulated and 29 lipids were downregulated. These lipids have the potential to serve as biomarkers for distinguishing yak ghee from different altitudes. Notably, S2 exhibited higher levels of fatty acids (21:1) and branched fatty acid esters of hydroxy fatty acids (14:0/18:0), while S5 showed increased levels of phosphatidylserine (O-20:0/19:1) and glycerophosphoric acid (19:0/22:1). S6 displayed higher levels of triacylglycerol (17:0/20:5/22:3), ceramide alpha-hydroxy fatty acid-sphingosine (d17:3/34:2), and acyl glucosylceramides (16:0–18:0–18:1). Yak ghee exhibited a high content of neutralizing glycerophospholipids and various functional lipids, including sphingolipids and 21 newly discovered functional lipids. Our findings provide insights into quantitative changes in yak ghee lipids during different altitudes, development of yak ghee products, and screening of potential biomarkers.

Ali Taghavi, Azadeh Ranjbar Nedamani, Ali motevali et al.

In this study, potato starch was treated with cold plasma and then dried at different temperatures. Cold plasma was generated using a plasma reactor with copper and steel electrodes, a voltage of 20 kV, a current of 3 mA, and a frequency of 50 Hz, using atmospheric air, and was applied to the sample. The flow behavior of 2% starch dispersion was then fitted with rheological models including the power law, Herschel-Bulkley, and Casson models using the regression toolbox of SPSS 20 software. The results showed that the choice of the appropriate rheological model depends on the type of treatment. However, the Herschel-Bulkley model provided a more accurate fit with the data in most cases. With an increase in pre-gelatinization temperature, the final viscosity of the starch samples decreased. The n coefficient in samples that were not pre-gelatinized increased with other factors, but in samples pre-treated at 55°C, it decreased with other factors. Drying temperature generally reduced the viscosity of the samples. However, at each temperature, the effect of pre-gelatinization temperature and cold plasma treatment time on viscosity changes was significant. Coefficients such as k and n also decreased with increasing drying temperature. Increasing the cold plasma treatment time reduced the viscosity and k in the samples but showed significant fluctuations in the value of n. In samples that received the longest cold plasma treatment time of 30 minutes, increasing the drying and pre-gelatinization temperatures reduced n and changed the fluid behavior from nearly Newtonian to shear-thinning. The findings of this study not only provide a better understanding of the rheological behavior of starch but also offer strategies for optimizing industrial processes related to the production and application of this type of starch.

Xiaopeng Wang, Chengyi Tu, Shuhao Chen et al.

As the global population and the per capita demand for resource intensive diets continues to grow, the corresponding increase in food demand challenges the global food system, enhancing its reliance on trade. Most previous research typically constructed either unweighted networks or weighted solely by tonnage to represent food trade, and focused on bilateral trade relationships between pairs of countries. This study investigates the properties of global food trade constructed in terms of total food calories associated with all the main food products exchanged along each trade link (edge of the food trade network). Utilizing data from the Food and Agriculture Organization between 1986 and 2022, we construct a directed, weighted network of net caloric flows between countries. This approach highlights the importance of considering nutritional value in discussions of food security and trade policies, offering a more holistic view of global food trade dynamics. Our analysis reveals significant heterogeneity in trade patterns, with certain countries emerging as major exporters or importers of food calories. Moreover, we employ network measures, including network connectivity, network heterogeneity, network modularity, and node correlation similarity, to elucidate the structural dynamics of global net food calorie trade networks that are relevant to the stability and resilience of the global food system. Our work provides a more nuanced understanding of global food trade dynamics, emphasizing the need for comprehensive strategies to enhance the resilience and sustainability of food trade networks.

Ahmad AlMughrabi, Adrián Galán, Ricardo Marques et al.

Food segmentation, including in videos, is vital for addressing real-world health, agriculture, and food biotechnology issues. Current limitations lead to inaccurate nutritional analysis, inefficient crop management, and suboptimal food processing, impacting food security and public health. Improving segmentation techniques can enhance dietary assessments, agricultural productivity, and the food production process. This study introduces the development of a robust framework for high-quality, near-real-time segmentation and tracking of food items in videos, using minimal hardware resources. We present FoodMem, a novel framework designed to segment food items from video sequences of 360-degree unbounded scenes. FoodMem can consistently generate masks of food portions in a video sequence, overcoming the limitations of existing semantic segmentation models, such as flickering and prohibitive inference speeds in video processing contexts. To address these issues, FoodMem leverages a two-phase solution: a transformer segmentation phase to create initial segmentation masks and a memory-based tracking phase to monitor food masks in complex scenes. Our framework outperforms current state-of-the-art food segmentation models, yielding superior performance across various conditions, such as camera angles, lighting, reflections, scene complexity, and food diversity. This results in reduced segmentation noise, elimination of artifacts, and completion of missing segments. Here, we also introduce a new annotated food dataset encompassing challenging scenarios absent in previous benchmarks. Extensive experiments conducted on MetaFood3D, Nutrition5k, and Vegetables & Fruits datasets demonstrate that FoodMem enhances the state-of-the-art by 2.5% mean average precision in food video segmentation and is 58 x faster on average.

Maxwell Kaye, Graham K. MacDonald, Eric Galbraith

Integrated global food system analysis is hampered by the fragmentation of data among food types, processes, and scales. Studies also often neglect the connection to human metabolism -- the ultimate driver of food demand. Here we use a common energetic framework to harmonize data on 95 individual food commodities across food system processes, including production, processing, animal feed and consumption, and estimate human metabolism from body size, demographic, and activity data. We estimate that the share of unmetabolized food calories globally doubled between 1990 and 2019 (from about 10 to 20% of the total calories available for human consumption) as food supply outpaced energy expenditure. Approximately half (51%) of the global population's metabolic demands could theoretically be met by production in the same local 1-degree grid cell (~ 10,000 km2) when holding diets constant. Our open-source framework can be applied to assess strategies to reduce food system inefficiencies from photosynthesis to metabolism while meeting local energetic demands.

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.