Hasil untuk "Food processing and manufacture"

Van-Ba Hoa, Won-Seo Park, Ja-Yeon Yoo et al.

Abstract Increasing the use and cycling of meat by-products is essential to increase economic benefits and reduce environmental pollution. Among the meat by-products, bones are widely used as food for human consumption and are important raw materials in other related industries (e.g., pharmaceuticals). However, their shelf-life during storage and nutritional composition have not been evaluated. The main objective of this study was to assess the collagen content, amino acid and fatty acid composition, and shelf-life of bones during refrigerated storage. For this study, the leg, brisket, and pelvic bones of Hanwoo cattle collected 24 h after slaughter were used. The bones were prepared into 1 cm thick pieces, placed on trays, overwrapped with plastic film, and stored at 4 °C for 21 days. The samples were then analyzed for aerobic plate count (APC), color, total volatile basic nitrogen (TVBN), lipid oxidation, collagen, amino acid, and fatty acid composition. After 21 d of storage, the APC increased faster in brisket bone (by 5.67 log10 CFU/cm2). Brisket bone also showed a faster increase in TVBN (by 16.79 mg/100 g) and TBARS (by 4.08 mg malondialdehyde/kg) compared to other remaining bones after 21 d of storage. The a* (redness) values significantly decreased with increased storage time in all the bones. The total collagen and essential amino acid contents ranged among the bones from 7.09 to 7.54 g/100 g and 501.92 to 853.20 mg/100 g, respectively. The unsaturated fatty acid (UFA) content among the bones varied from 46.75% to 52.38%.

Dongyue Zhao, Xiuli Xu, Wei Wu et al.

Magnetic covalent organic framework nanocomposite denoted as Fe3O4@TPBD-BTA with core-shell structure was fabricated via a simple template-mediated precipitation polymerization method at mild conditions. The polyimine network shell was created through the polymerization of N,N,N′,N′-tetrakis(p-aminophenyl)-p-phenylenediamine (TPBD) and biphenyl-3,3′,5,5′-tetracarbaldehyde (BTA) in tetrahydrofuran (THF) by the Schiff-base reaction. Featuring with large good solution dispersibility, and high stability, the obtained Fe3O4@TPBD-BTA exhibited high adsorption capacities and fast adsorption for zearalenone and its Aflatoxin (AFT). The adsorption isotherms showed multilayer adsorption dominated at low concentration and monolayer adsorption at high concentration between the interface of AFs and Fe3O4@TPBD-BTA. With the Fe3O4@TPBD-BTA as sorbent, a magnetic solid-phase extraction-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was established for simultaneous adsorption and detection of five ZEAs in complex samples. The proposed method displayed favorable linearity, low limits of detection (0.006 5 − 0.008 2 μg/kg). The developed method has been applied for real sample analysis, with recoveries of 81.0%−109.6%. These results showed that Fe3O4@TPBD-BTA has a good application potential for the adsorption of AFs in milk and dairy products.

Sangjun Han, Youngmi Hur

With advances in artificial intelligence, image processing has gained significant interest. Image super-resolution is a vital technology closely related to real-world applications, as it enhances the quality of existing images. Since enhancing fine details is crucial for the super-resolution task, pixels that contribute to high-frequency information should be emphasized. This paper proposes two methods to enhance high-frequency details in super-resolution images: a Laplacian pyramid-based detail loss and a repeated upscaling and downscaling process. Total loss with our detail loss guides a model by separately generating and controlling super-resolution and detail images. This approach allows the model to focus more effectively on high-frequency components, resulting in improved super-resolution images. Additionally, repeated upscaling and downscaling amplify the effectiveness of the detail loss by extracting diverse information from multiple low-resolution features. We conduct two types of experiments. First, we design a CNN-based model incorporating our methods. This model achieves state-of-the-art results, surpassing all currently available CNN-based and even some attention-based models. Second, we apply our methods to existing attention-based models on a small scale. In all our experiments, attention-based models adding our detail loss show improvements compared to the originals. These results demonstrate our approaches effectively enhance super-resolution images across different model structures.

Zixi Xue, Xianrong Xiang, Jiaying Tang et al.

Encapsulation and protection of hesperidin (HES) in mung bean protein isolate (MPI)-dextran (DX) conjugate-stabilized nanoemulsions (MDC NEs) were investigated in this study. The degree of grafting of MDC prepared by a dry-heating method reached 39.70 % ± 0.01 % under the optimal conditions of MPI/DX mass ratio 1:2.3, reaction temperature 58.8 °C, and reaction time 4 d. Moreover, the analyses of Fourier infrared spectroscopy, intrinsic fluorescence spectroscopy, surface hydrophobicity, and thermal stability further confirmed the covalent grafting of dextran onto MPI molecules. When encapsulated in MDC NEs at 80 MPa for three times by high-pressure homogenization, the encapsulation efficiency and loading capacity of HES were 63.62 % ± 0.01 % and 0.40 ± 0.00 g/g, respectively. The encapsulated HES exhibited higher antioxidant activity and stronger light and storage stability than the free HES. Additionally, the incorporation of HES inhibited the formation of lipid peroxides in the nanoemulsions. The findings suggest that glycosylation combined with high-pressure homogenization is an effective strategy for enhancing the stability of MPI-based emulsions and improving their encapsulation of HES. This study provides a promising approach for the development of innovative food and beverage products based on MPI emulsions or new materials for encapsulating fat-soluble bioactive compounds.

ZHANG Qian, LIANG Jiamin, XU Tengyu, XIAO Xiong, CHEN Xiong, LI Xin

Our laboratory had isolated a strain of non-Saccharomyces yeast with significant application potential from high-temperature Daqu, Lachancea thermotolerans Y-07. This study aimed to explore the interaction of L. thermotolerans Y-07 with Aspergillus oryzae M-08 and Saccharomyces cerevisiae BS-19 when used for mixed culture fermentation of Baijiu under gradient temperature conditions and to evaluate its impact on Baijiu quality. To this end, changes in biomass, sensory quality, organic acids and volatile flavor compounds were examined during the fermentation process. The results demonstrated that L. thermotolerans Y-07 produced β-phenylethanol, reduced the higher alcohol content, and significantly improved the sensory quality of Baijiu, indicating its positive role in Baijiu brewing. High-temperature conditions not only helped maintain yeast diversity during the fermentation process, but also enhanced aroma richness. The presence of S. cerevisiae BS-19 increased the biomass of L. thermotolerans Y-07 by 21.63%. Under high-temperature conditions, the presence of S. cerevisiae BS-19 facilitated the recovery and proliferation of L. thermotolerans Y-07. This study provides important insights into the mechanism of action of L. thermotolerans in Baijiu brewing and the influence of temperature on microbial interactions during the fermentation process.

Yanbing He, Chenjing Yin, Xiaohu Mao et al.

Assessing the performance level of human settlement improvement in traditional villages is significant in promoting the protection of traditional villages, but there is a lack of performance research on human settlement improvement from the perspective of corporate governance in previous studies. This paper selected 16 traditional villages as case villages and obtained a total of 345 questionnaires. By reference to the Balanced Scorecard (BSC) theory, a performance evaluation index system for human settlement improvement is constructed in this paper. In addition, the level of performance exhibited by traditional villages is evaluated and analyzed via the entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the obstacle degree analysis method. This study reveals the following findings: (1) The performance level of traditional villages in Jiaozuo city ranges between 0.28 and 0.64, with an average value of 0.49, thus indicating a medium level. (2) With respect to the subdimensions of human settlement improvement performance, the policy management dimension (0.88) exhibits the highest value, followed by the villagers dimension (0.48) and the learning and growth dimension (0.27), while the financial benefits dimension (0.10) exhibits the lowest value. (3) The obstacles affecting the performance level of human settlement improvement in different types of traditional villages are characterized by both similarities and differences. This study summarized the effects of traditional village human settlement improvement, and provided more scientific and reliable governance suggestions for future traditional village human settlement improvement, so as to better promote the protection of traditional villages and the sustainable development of the human settlement environment.

Tonmoy Ghosh, Edward Sazonov

Food image recognition is a challenging task in computer vision due to the high variability and complexity of food images. In this study, we investigate the potential of Noisy Vision Transformers (NoisyViT) for improving food classification performance. By introducing noise into the learning process, NoisyViT reduces task complexity and adjusts the entropy of the system, leading to enhanced model accuracy. We fine-tune NoisyViT on three benchmark datasets: Food2K (2,000 categories, ~1M images), Food-101 (101 categories, ~100K images), and CNFOOD-241 (241 categories, ~190K images). The performance of NoisyViT is evaluated against state-of-the-art food recognition models. Our results demonstrate that NoisyViT achieves Top-1 accuracies of 95%, 99.5%, and 96.6% on Food2K, Food-101, and CNFOOD-241, respectively, significantly outperforming existing approaches. This study underscores the potential of NoisyViT for dietary assessment, nutritional monitoring, and healthcare applications, paving the way for future advancements in vision-based food computing. Code for reproducing NoisyViT for food recognition is available at NoisyViT_Food.

Ali Rostami

Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.

Pooja Batra, Ajay Sharma

In this paper, we analyse the impact of international migration on the food consumption and dietary diversity of left-behind households. Using the Kerala migration survey 2011, we study whether households with emigrants (on account of international migration) have higher consumption expenditure and improved dietary diversity than their non-migrating counterparts. We use ordinary least square and instrumental variable approach to answer this question. The key findings are that: a) emigrant households have higher overall consumption expenditure as well as higher expenditure on food; b) we find that international migration leads to increase in the dietary diversity of left behind households. Further, we explore the effect on food sub-group expenditure for both rural and urban households. We find that emigrant households spend more on protein (milk, pulses and egg, fish and meat), at the same time there is higher spending on non-healthy food habits (processed and ready to eat food items) among them.

Bianca Datta, Markus J. Buehler, Yvonne Chow et al.

Global food systems must deliver nutritious and sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial intelligence (AI) now offers a transformative path with the potential to link molecular composition to functional performance, bridge chemical structure to sensory outcomes, and accelerate cross-disciplinary innovation across the entire production pipeline. Here we outline AI for Food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory properties, manufacturing, and recipe generation. Early successes demonstrate how AI can predict protein performance, map molecules to flavor, and tailor consumer experiences. But significant challenges remain: lack of standardization, scarce multimodal data, cultural and nutritional diversity, and low consumer confidence. We propose three priorities to unlock the field: treating food as a programmable biomaterial, building self-driving laboratories for automated discovery, and developing deep reasoning models that integrate sustainability and human health. By embedding AI responsibly into the food innovation cycle, we can accelerate the transition to sustainable protein systems and chart a predictive, design-driven science of food for our own health and the health of our planet.

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.

Sita Acharya, Ujjal Tiwari, Rishi Ram Kattel et al.

Dairy production is one of the risky businesses, which seeks effective risk management strategies. Adoption of a livestock insurance scheme is one of the most effective risk management strategies for dairy entrepreneurs. However, in Nepal, insurance coverage is very low in the dairy sub-sector. The study aimed to assess the dairy farmer’s willingness to pay for the livestock insurance scheme. The study was carried out in Kavrepalanchowk district of Nepal in 2022. The simple random sampling technique was used to select 146 dairy farmers. Double Bounded Dichotomous Choice Contingent Valuation technique was used to estimate Willingness to Pay (WTP). About 93% of the respondent farmers were the member of cooperatives which was the major source of information flow for livestock insurance scheme to them. The result revealed that number of cattle reared, awareness about livestock insurance scheme, and experience of livestock loss have significant positive influences in the decision regarding the adoption of livestock insurance. Farmers are willing to pay more than the current premium rate for livestock insurance. Hence, only increasing the subsidy might not be the solution in expanding the livestock insurance adoption rate. Rather, alternative approach like mobilizing institutions (cooperative) in expanding the insurance scheme is required.

Dionissios T. Hristopulos

Causal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of information flow between time series. The theoretical formulation of information flow for stochastic dynamical systems provides a general expression and a data-driven statistic for the rate of entropy transfer between different system units. To advance understanding of information flow rate in terms of intuitive concepts and physically meaningful parameters, we investigate statistical properties of the data-driven information flow rate between coupled stochastic processes. We derive relations between the expectation of the information flow rate statistic and properties of the auto- and cross-correlation functions. Thus, we elucidate the dependence of the information flow rate on the analytical properties and characteristic times of the correlation functions. Our analysis provides insight into the influence of the sampling step, the strength of cross-correlations, and the temporal delay of correlations on information flow rate. We support the theoretical results with numerical simulations of correlated Gaussian processes.

Jarosław Chwastowski, Katarzyna Wójcik, Henryk Kołoczek et al.

Tap water treated in air with low-temperature and low-pressure glow plasma of low frequency was tested for its either stimulation or inhibition of the growth of the selected microorganisms commonly colonizing human organism. The growth of chosen microorganisms was monitored by estimation of optical density of their colonies. The fairly linear growth against time of all microorganisms under study accelerated after 12 h from the beginning of the experiment. Colonies of E. coli and S. cerevisiae breed in the plasma treated water had an approximately 20% stimulation of the growth which was observed between 12 and 24 h. Neither stimulation nor inhibition of the growth could be noted for colonies of Aspergillus niger, Candida albicans, Yarrowia lipolytica, and Enterococcus faecalis, in whole period of observation. The plasma-treated water had no effect upon the growth of Mycobacteria. Independently of the water tested, M. tuberculosis started proliferating on the 14th day of the experiment, M. intercellulare and M. kansai after 9 days, and the growth of M. fortuitos could be observed after 3 days.

R. S. Svyatnenko, A. I. Marynin, S. I. Litvynchuk et al.

Studies of beekeeping products reveal the presence of a significant complex of biologically active substances in them, which arouses great interest among scientists and specialists in the field of medicine and nutrition. One of the little-studied products – perga, is characterized by a unique composition of complex compounds, characterized by their natural harmony, bioavailability and synergistic interaction. This research substance, which is the result of the active activity of bees, includes a balanced spectrum of biologically active components that can affect various aspects of human health. In particular, its potential is considered in the context of strengthening immunity, increasing energy reserves and general support of physiological processes. Such a well-known product can become an important addition to the diet and an approach to maintaining optimal health. The physico-chemical and organoleptic characteristics of buckwheat perga from different regions were studied in order to establish possible differences in their composition and quality. The analysis of perga samples from the Boryspil and Pereyaslav-Khmelnytsky districts showed that the mass fraction of mechanical impurities in both cases is 0 %, confirming their high quality and purity. It was determined that the moisture content of the perga from Boryspil district is 8.7 %, and from Pereyaslav-Khmelnytskyi – 7.6 %, which may be due to differences in the conditions of collection and storage. The pH level in perga from Boryspil district is 5, and from Pereyaslav-Khmelnytskyi  – 3.9, which indicates possible differences in chemical conditions. The study also revealed a difference in the mass content of flavonoid compounds: in perga from Boryspil district, this indicator is 3.7 %, and from Pereyaslav-Khmelnytskyi – 2.5 %. The organoleptic analysis showed a high index of color (5 points) for both areas, indicating stability and intensity of color. Taste and smell received 4.8 and 4.5 points, respectively, confirming the presence of high-quality aroma and taste. Appearance and consistency were also highly rated (4.4 and 4.8 points), indicating the variety and naturalness of the lumps and their crumbly structure. The general analysis confirms the presence of differences in the composition and characteristics of buckwheat perga from different regions. These differences may be related to environmental factors, but they correspond to DSTU 7074:2009.

Amy Hui-Mei Lin, Andrea Gómez-Maqueo

Strengthening food security, in places where land and natural resources are limited or no longer available, is challenging. This is especially true for the production of staple food carbohydrates. Unlike some alternative foods, such as cultured meats, producing food carbohydrates using conventional agri-food approaches requires many natural resources, which are not available in some regions such as Singapore. Therefore, we must develop new, sustainable methods to enhance the quantity and nutritional quality of foods rich in carbohydrates. In this article, we review current developments in food security in the city-state of Singapore and emphasize the essential role of food carbohydrates in the food security plan. We discuss technology developments (i.e., indoor vertical farming, urban farming) used to enhance crop quality and production. We also make a few recommendations such as exploring underutilized and unconventional crops that are resilient and nutrient-dense, identifying hidden resources in local ecosystems (i.e., revalorizing agri-food processing by-products), and producing alternative carbohydrates (i.e., microbial and synthetic carbohydrates). Experience and approaches developed in Singapore provide an example to other regions and may inspire creativity in securing food availability.

J.R. Swift, M.A. Turner, J.C. Reynolds

A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the classification results for which were compared against artificial neural network methods. The artificial neural network was found to outperform PCA, achieving ≥95% accuracy for all sampling conditions, with only two misclassifications under the ideal conditions, while requiring less development time.

Andrew Peng, Jiangpeng He, Fengqing Zhu

Food image analysis is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning-based methods learn the visual representation for downstream tasks based on human annotation of each food image. However, most food images in real life are obtained without labels, and data annotation requires plenty of time and human effort, which is not feasible for real-world applications. To make use of the vast amount of unlabeled images, many existing works focus on unsupervised or self-supervised learning of visual representations directly from unlabeled data. However, none of these existing works focus on food images, which is more challenging than general objects due to its high inter-class similarity and intra-class variance. In this paper, we focus on the implementation and analysis of existing representative self-supervised learning methods on food images. Specifically, we first compare the performance of six selected self-supervised learning models on the Food-101 dataset. Then we analyze the pros and cons of each selected model when training on food data to identify the key factors that can help improve the performance. Finally, we propose several ideas for future work on self-supervised visual representation learning for food images.

Xinyue Pan, Jiangpeng He, Fengqing Zhu

Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, existing works in food classification predominantly focuses on predicting 'food types', which do not contain direct nutritional composition information. This limitation arises from the inherent discrepancies in nutrition databases, which are tasked with associating each 'food item' with its respective information. Therefore, in this work we aim to classify food items to align with nutrition database. To this end, we first introduce VFN-nutrient dataset by annotating each food image in VFN with a food item that includes nutritional composition information. Such annotation of food items, being more discriminative than food types, creates a hierarchical structure within the dataset. However, since the food item annotations are solely based on nutritional composition information, they do not always show visual relations with each other, which poses significant challenges when applying deep learning-based techniques for classification. To address this issue, we then propose a multi-stage hierarchical framework for food item classification by iteratively clustering and merging food items during the training process, which allows the deep model to extract image features that are discriminative across labels. Our method is evaluated on VFN-nutrient dataset and achieve promising results compared with existing work in terms of both food type and food item classification.

Ben Hayes, Jordie Shier, György Fazekas et al.

The term "differentiable digital signal processing" describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music & speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably. Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research.