Feed additives such as 3-nitrooxypropanol (3-NOP) can reduce a dairy cow's enteric methane (CH4) emissions. Our study aimed to examine whether there is genetic variation in the cow's response in reduction of CH4 emission after receiving 3-NOP feed additives. Data were available from a project in which the CH4-mitigating effect of 3-NOP (Bovaer, DSM-Firmenich) was tested for Norwegian dairy cows. The one-year trial took place in a commercial dairy herd and included 79 Norwegian Red cows. The cows' CH4 emissions were measured by a GreenFeed unit. A total of 14,166 daily CH4 measures were analyzed, and the overall mean (SD) was 387 (96) g of CH4 per cow per day. Cows were divided into 2 groups, where 54 cows received between 1.2 and 1.5 g of 3-NOP per day and 25 cows were in a control group without any feed additives. The trait of interest, CH4-response, was defined as the cow's change in CH4 after introducing 3-NOP in the diet compared with the base level CH4 in periods without 3-NOP. The base level period consisted of 3 wk in September of 2023 and 4 wk in January and February of 2024. The trait daily CH4 (g/d) was analyzed with a linear animal repeatability model with fixed effects of 3-NOP group, parity, lactation week, and test day, and random animal genetic and permanent environment effects. Fixed effect solutions from this model were used to compute yield deviations for CH4 (YD_CH4), a corrected phenotype (i.e., daily CH4 corrected for effects of parity, lactation stage, and test day). For cows fed 3-NOP, we calculated their individual base level CH4 as the average YD_CH4 from the periods without 3-NOP. The trait CH4-response was then calculated as YD_CH4 minus base level CH4. The new CH4-response trait had a total of 7,293 daily records from 42 cows and was analyzed with a linear animal repeatability model with the fixed effect of test day and random additive genetic effect of cow. Although this is a small dataset and results should be interpreted with caution, the estimated heritability of 0.15, with an SE of 0.03, suggests that genetic variation in response in the reduction of CH4 emission after receiving 3-NOP feed additives exists for Norwegian Red dairy cows.
Roman Konečný, Michaela Horčičková, Martin Kváč
et al.
Lactoferrin (LF) is an iron-binding immunoprotein of the mammary gland whose levels increase during mastitis and may be influenced by the metabolic status of the cow. During early lactation, dairy cows are exposed to a negative energy balance (NEB) and the associated increase in susceptibility to mastitis. However, the extent to which the metabolic profile influences LF secretion in milk during the postpartum period remains unclear. The objective of this study was to assess the associations between metabolic status and milk LF contents in Holstein cows (<i>n</i> = 122) in the first twenty days of lactation. Based on the milk LF contents, the cows were categorized into two groups: LF-LOW (≤123 mg/L; <i>n</i> = 81) and LF-HIGH (>123 mg/L; <i>n</i> = 41). Serum indicators of energy and nitrogen metabolism, hepatic function, and selected macro-/microelements were measured; urine electrolytes and net acid–base excretion (U-ABB) were assessed; and milk composition, including somatic cell count (SCC), was determined. LF-HIGH cows showed higher SCC (<i>p</i> = 0.0516) and serum glucose (<i>p</i> < 0.001), together with lower serum triglycerides (<i>p</i> = 0.0101) versus LF-LOW cows. Milk beta-hydroxybutyric acid (BHB) content was lower in the LF-HIGH group (trend, <i>p</i> ≈ 0.062). LF-HIGH also exhibited significantly greater natriuresis (<i>p</i> = 0.0078) and a more negative U-ABB (<i>p</i> < 0.001), indicating higher acid–base load. In conclusion, elevated LF contents during the postpartum period were associated with the activation of local mammary gland immune defence and concurrent compensatory metabolic processes related to NEB, rather than with pronounced alterations in basic milk composition. Milk LF content may therefore be considered as a specific indicator of immunometabolic compensation during the early postpartum period, rather than as a general marker of overall cow health.
Abstract The application of ultraviolet (UV) light in the food industry has held great promise for a long time. UVC light (200–280 nm) possesses excellent germicidal properties to inactivate a wide range of microbial pathogens (bacteria, fungi, yeasts, molds, and viruses). UVC technology can be used to effectively prevent foodborne illnesses while increasing the shelf life of food without compromising its quality by reducing the microbial load. UVC radiation processing of food depends on a variety of factors such as the operational parameters of the UVC equipment, microbial characteristics, and the composition of the food. Thus, the application of UVC irradiation is an emerging non-thermal technique for the decontamination of food products. This review describes the fundamentals of using UVC radiation to inactivate pathogenic microbes for the decontamination of foods (e.g., fruit and vegetable juices, milk and dairy products, meat products, beef, and seafood). At last, the current status of international regulations is discussed along with the future challenges in this research field.
ABSTRACT: In addition to the metabolic and health status of cows at parturition, intrauterine conditions, the calving process, and colostrum feeding may affect endocrine and metabolic pathways in the neonate. Forty-six clinically healthy cows without dystocia were enrolled, along with their calves. Blood samples were collected from cows (4 h postpartum [p.p.]) and calves (4, 12, and 24 h p.p.). Calves were fed colostrum from their dams immediately after blood sampling at 4 and 12 h p.p. Concentrations of glucose, insulin, nonesterified fatty acids (NEFA), IGF-1, and prolactin (PRL), as well as activities of aspartate-aminotransferase and gamma-glutamyltransferase were measured in the plasma of cows. In calves, we measured various endocrine and metabolic parameters related to protein, glucose, and lipid metabolism (e.g., NEFA, phospholipids [PL], total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C]), IgG, glucose, glucagon, and insulin. Pearson correlation coefficients among parameters measured in cows and calves were calculated. At 4 h p.p. (i.e., before colostrum feeding), maternal glucose was positively correlated with glucose (r = 0.29) and NEFA in calves (r = 0.25). Plasma NEFA in dams was negatively correlated with fat metabolism (PL: r = −0.31, HDL-C: r = −0.32) and plasma IgG (r = −0.28) in calves at 4 h p.p. Positive correlations were identified between the glucose of dams and calves (12 h p.p.: r = 0.26; 24 h p.p.: r = 0.45). Maternal NEFA was positively associated with calf lipid metabolism at 24 h p.p. (PL: r = 0.44, TC: r = 0.39, LDL-C: r = 0.37, HDL-C: r = 0.36). Primarily positive and significant correlations were detected between maternal PRL and lipid metabolism-related parameters in calves (NEFA at 12 h p.p.: r = 0.26, PL at 12 h p.p.: r = 0.31; PL at 24 h p.p.: r = 0.57, TC at 24 h p.p.: r = 0.62, LDL-C at 24 h p.p.: r = 0.48, and HDL-C at 24 h p.p.: r = 0.69). In conclusion, the metabolic status of neonates is partly associated with the metabolism of their mothers before the first feeding, whereas later associations between cow and calf are likely due to colostrum feeding.
Talia Katz, Shoshana Ginsburg, Rafael Jimenez-Flores
In a consumer-centric environment, food products are created and focused on the consumer's experience and desires. One important food product used around the world is butter. Consumers expect that when they take their butter out of the refrigerator, it will be able to spread nicely and evenly; however, with conventional butter, which does not include any added vegetable oils, achieving a soft, spreadable butter is rare. This work seeks to contribute to the pool of knowledge regarding the role of catalysts in effecting butter spreadability. To meet consumer expectations, utilizing catalysts in the esterification process with butter oil has proven successful in changing the lipid structure, enabling the possibility of a softer butter. The comparison of utilizing no catalysts and 2 different catalysts, potassium hydroxide and calcium hydroxide, during the chemical esterification process showed that the use of potassium hydroxide catalyst resulted in the least firm and lowest solid fat content butter. These findings demonstrate the potential for industrial use of esterified butter oil using potassium hydroxide in commercial butter.
Florin Daniel Lipșa, Roxana Nicoleta Rațu, Florina Stoica
et al.
In light of the growing concerns of consumers who are increasingly turning towards healthier food options, both researchers and producers in the food industry are exploring the use of agro-industrial by-products as nutritionally valuable ingredients. This strategy not only enables the development of value-added food products, but also supports sustainability through the valorization of waste. Blackcurrant pomace (BP), a by-product obtained after juice extraction, has been shown to be rich in bioactive compounds, dietary fiber, antioxidants, and anthocyanin pigments. For these reasons, the innovative aspect of the study was its use of different proportions of BP powder, 5%, 10%, and 15%, when obtaining new varieties of stirred yogurt. This study assesses the impact of BP powder on the stirred yogurt’s antioxidant content, physicochemical properties, color, microbiological characteristics, and sensory qualities. The findings showed that BP powder intensified the yogurts’ coloration and considerably improved their antioxidant activity (which ranged from 8.21 ± 0.35 to 21.15 ± 0.49 µmol TE/g DM) and nutritional quality. The panelists’ positive acceptance was confirmed by sensory evaluation, and the 10% BP formulation (DBBP2) was rated as the most favorable. These results show that BP is a valuable ingredient for enhancing dairy products, creating nutritious, appealing yogurts while promoting sustainable food production and valorization of food waste.
Tiziana Cattai, Stefania Sardellitti, Stefania Colonnese
et al.
Water management is one of the most critical aspects of our society, together with population increase and climate change. Water scarcity requires a better characterization and monitoring of Water Distribution Networks (WDNs). This paper presents a novel framework for monitoring Water Distribution Networks (WDNs) by integrating physics-informed modeling of the nonlinear interactions between pressure and flow data with Topological Signal Processing (TSP) techniques. We represent pressure and flow data as signals defined over a second-order cell complex, enabling accurate estimation of water pressures and flows throughout the entire network from sparse sensor measurements. By formalizing hydraulic conservation laws through the TSP framework, we provide a comprehensive representation of nodal pressures and edge flows that incorporate higher-order interactions captured through the formalism of cell complexes. This provides a principled way to decompose the water flows in WDNs in three orthogonal signal components (irrotational, solenoidal and harmonic). The spectral representations of these components inherently reflect the conservation laws governing the water pressures and flows. Sparse representation in the spectral domain enable topology-based sampling and reconstruction of nodal pressures and water flows from sparse measurements. Our results demonstrate that employing cell complex-based signal representations enhances the accuracy of edge signal reconstruction, due to proper modeling of both conservative and non-conservative flows along the polygonal cells.
Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs are limited by the use of multiple independent stationary Gaussian processes (GPs), leading to prohibitive computational and parametric complexity in high-dimensional settings and restricted modeling capacity for non-stationary dynamics. To address these challenges, we propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems. Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive non-stationary implicit process prior that can capture complex transition dynamics while significantly reducing model complexity. For the inference of the implicit process, we develop a variational inference algorithm that jointly approximates the posterior over the underlying GP and the neural network parameters defining the normalizing flows. To avoid explicit variational parameterization of the latent states, we further incorporate the ensemble Kalman filter (EnKF) into the variational framework, enabling accurate and efficient state estimation. Extensive empirical evaluations on synthetic and real-world datasets demonstrate the superior performance of our ETGPSSM in system dynamics learning, high-dimensional state estimation, and time-series forecasting, outperforming existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In this paper, we propose a knowledge score for predictions from Gaussian process regression (GPR) models that quantifies the extent to which observing data have reduced our uncertainty about a prediction. The knowledge score is interpretable and naturally bounded between 0 and 1. We demonstrate in several experiments that the knowledge score can anticipate when predictions from a GPR model are accurate, and that this anticipation improves performance in tasks such as anomaly detection, extrapolation, and missing data imputation. Source code for this project is available online at https://github.com/KurtButler/GP-knowledge.
Justin G. Bendall, Abraham S. Chawanji, Bertram Y. Fong
et al.
ABSTRACT: Whole milk powder (WMP) manufactured in New Zealand in 1907 was sent to the Antarctic continent with the Shackleton-led British Antarctic Expedition from 1907 to 1909. This powder was stored at ambient conditions at Shackleton's Hut at Cape Royds, Antarctica, for over 100 yr before a sample was collected on behalf of Fonterra by the Antarctic Heritage Trust. Having spent most of its existence both dried and in frozen storage, any deleterious reactions within the WMP would have been markedly retarded. The composition and some properties of the roller-dried Shackleton's WMP are reported along with those of 2 modern spray-dried New Zealand WMP. The Shackleton powder was less white and more yellow than the modern WMP and was composed of flakes rather than agglomerated particles, consistent with that expected of a roller-dried powder. Headspace analysis showed lipolytic and oxidative volatile compounds were present in the Shackleton WMP, indicting some deterioration of the milk either before powder manufacture or on storage of the finished product. On a moisture-free basis, the Shackleton WMP had higher protein, higher fat (with a markedly higher free fat level), higher ash, and a lower lactose level than the modern WMP. The lysine level was lower in the Shackleton WMP compared with the spray-dried powders, whereas the fatty acid composition was relatively similar. The sodium level was markedly higher in the Shackleton WMP compared with the spray-dried powder, which is probably due to the addition of an alkaline sodium salt to adjust the pH of the milk before roller drying. Lead, iron, and tin levels were markedly higher in the Shackleton WMP compared with the spray-dried powders, possibly due to the equipment used in powder manufacture and the tin-plated cases used for storage. The proteins in the Shackleton WMP were more lactosylated than in the spray-dried powders. The Shackleton WMP had a higher ratio of κ-casein A to B variants and a higher ratio of β-lactoglobulin B to A variants than the spray-dried powders, whereas the αS1-casein, β-casein, αS2-casein, and α-lactalbumin protein variants were similar in all powders. The total phospholipid content was markedly lower in the Shackleton WMP than the spray-dried powders, primarily due to a lower phosphatidylethanolamine concentration. The molecular species distributions within the phospholipid classes were generally similar in the 3 powders. Claims are sometimes encountered that the milk of today is different from that consumed by previous generations. However, this comparative study has shown that the Shackleton WMP was generally similar to modern WMP. Although differences in some components and properties were observed, these were attributable to the manufacturing equipment and processes used in the pioneering years of WMP manufacture.
We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces the reliance on input audio features while preserving the integrity of the base SSLR. CA-SSLR improves the model's capabilities and demonstrates its generality on unseen tasks with minimal task-specific tuning. Our method employs linear modulation to dynamically adjust internal representations, enabling fine-grained adaptability without significantly altering the original model behavior. Experiments show that CA-SSLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks. Specifically, CA-SSLR achieves a 10% relative reduction in LID errors, a 37% improvement in ASR CER on the ML-SUPERB benchmark, and a 27% decrease in SV EER on VoxCeleb-1, demonstrating its effectiveness.
Food loss and waste is a major concern in the United States and globally, with dairy foods representing one of the top categories of food lost and wasted. Estimates indicate that in the United States, approximately a quarter of dairy products are lost at the production level or wasted at the retail or consumer level annually. Premature microbial spoilage of dairy products, including fluid milk, cheese, and cultured products, is a primary contributor to dairy food waste. Microbial contamination may occur at various points throughout the production and processing continuum and includes organisms such as gram-negative bacteria (e.g., Pseudomonas), gram-positive bacteria (e.g., Paenibacillus), and a wide range of fungal organisms. These organisms grow at refrigerated storage temperatures, often rapidly, and create various degradative enzymes that result in off-odors, flavors, and body defects (e.g., coagulation), rendering them inedible. Reducing premature dairy food spoilage will in turn reduce waste throughout the dairy continuum. Strategies to reduce premature spoilage include reducing raw material contamination on-farm, physically removing microbial contaminants, employing biocontrol agents to reduce outgrowth of microbial contaminants, tracking and eliminating microbial contaminants using advanced molecular microbiological techniques, and others. This review will address the primary microbial causes of premature dairy product spoilage and methods of controlling this spoilage to reduce loss and waste in dairy products.
Abstract Dairy Industry is enlisted as one of the top most industries among the entire industries food arena. Apart from high consumption of water in multiple milk products like cheese, curd, butter, yogurt, dry milk powder; dairy industry further involves in some other processes like sanitary, cooling of milk products, cleaning and washing of the processing equipments. During the making and process of these products a large amount of water is used. But the problem with this water is its reuse and toxicity as it cannot be used again as it is. Dairy wastewater treatment is a big issue as dairy wastewater releases a high amount of Chemical Oxygen Demand , inorganic and organic particles, Biological Oxygen Demand and nutrients. Such contaminated water if not handled appropriately, it pollutes water bodies and largely affects our ecosystem and biodiversity. Dairy Industries play important role in the water pollution and the quality of the water. Thus, Suitable treatments for wastewater are required to use effective disposal methods as the large amount of the water is used during the dairy processing and making of milk related products and this large amount of water is not longer useful as it has very high amount of the contaminants which make it non-recyclable. In this review article, it is briefly discussed about the processes in the dairy industry and work done in such processes, amount of wastewater and, major sources of wastewater as well as the harmful effects of wastewater on the environment.
Nadia Innocente, Niccolò Renoldi, Erica Moret
et al.
ABSTRACT: The possible contribution of brine-derived microflora to the sensory attributes of cheese is still a rather unexplored field. In this study, 365 bacteria and 105 yeast strains isolated from 11 cheese brines were qualitatively tested for proteolytic and lipolytic activities, and positive strains were identified by sequencing. Among bacteria, Staphylococcus equorum was the most frequent, followed by Macrococcus caseolyticus and Corynebacterium flavescens. As for yeasts, Debaryomyces hansenii, Clavispora lusitaniae, and Torulaspora delbrueckii were most frequently identified. A total of 38% of bacteria and 59% of yeasts showed at least 1 of the metabolic activities tested, with lipolytic activity being the most widespread (81% of bacteria and 95% of yeasts). Subsequently 15 strains of bacteria and 10 yeasts were inoculated in a curd-based medium and assessed via headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry to determine their volatilome. After a 30-d incubation at 12°C, most strains showed a viability increase of about 2 log cfu/mL, suggesting good adaptability to the cheese environment. A total of 26 compounds were detected in the headspace, carbonyl compounds and alcohols being the major contributors to the volatile profile of the curd-based medium. Multivariate analysis was carried out to elucidate the overall differences in volatiles produced by selected strains. Principal component analysis and hierarchical clustering analysis demonstrated that the brine-related microorganisms were separated into 3 different groups, suggesting their different abilities to produce volatile compounds. Some of the selected strains have been shown to have interesting aromatic potential and to possibly contribute to the sensory properties of cheese.
ABSTRACT: Calf mortality can be used as an indicator of animal health and welfare on dairy farms. However, several challenges surround the estimation and reporting of this metric, specifically: (1) lack of records or reliable data, (2) methods of data collection, and (3) inconsistencies in calculation and definitions used. Therefore, despite its importance, the lack of consensus on a definition of calf mortality makes it difficult to compare mortality rates between dairy farms or studies. Monitoring factors associated with calf mortality is vital to create preventative strategies. Although common strategies have been set about how to raise dairy calves and manage dairy calves, discrepancies among studies evaluating factors associated with calf mortality still exist. This review summarizes research on the evaluation of calf mortality and associated risk factors, specifically, the lack of reliable data and standardization of the definition of calf mortality. In addition, current strategies to monitor and prevent calf mortality will be presented in this review.