Search Results (98)

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

Enzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries. This review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development. In terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems. Proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes. From a nutritional perspective, enzymatic hydrolysis significantly improves the bioavailability of proteins, minerals, and dietary fibers, while simultaneously degrading antinutritional factors and harmful compounds, including phytic acid, tannins, food allergens, and acrylamide, thus contributing to improved food safety and nutritional balance. With respect to functional innovation, enzyme-directed production of bioactive peptides has demonstrated notable antihypertensive, antioxidant, and immunomodulatory activities. In addition, enzymatic synthesis of functional oligosaccharides and rare sugars, glycosylation-based modification of polyphenols, and enzyme-assisted extraction of plant bioactive compounds provide novel strategies and technological support for the development of functional foods. Owing to their high specificity and eco-friendly nature, enzyme technologies are driving food and nutrition sciences toward more precise, personalized, and sustainable development pathways. Despite these advances, critical research gaps remain, particularly in the limited mechanistic understanding of enzyme behavior in complex food matrices, the insufficient integration of multi-omics data with enzymatic process design, and the challenges associated with translating laboratory-scale enzymatic strategies into robust, data-driven, and scalable industrial applications. Full article

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost. Full article

Plant-based milk alternatives (PBMA) have emerged as popular substitutes for cow milk, driven by health, environmental, and ethical considerations. However, their ability to replicate the sensory and physicochemical properties of dairy remains a critical challenge for industry. This review critically examines the extent to which almond, soy, and oat PBMA replicate key sensory attributes of ultra-high temperature (UHT) full cream cow milk, focusing on appearance, texture, and flavour. Furthermore, it explores the relationship between these sensory attributes and the physicochemical properties of PBMA to elucidate the underlying reasons for the observed differences. A comparative analysis of compositional differences reveals fundamental limitations linked to plant protein functionality, carbohydrate structure, fat composition, and mineral fortification, all of which contribute to disparities in creaminess, mouthfeel, colour, and flavour. Technological strategies such as particle size reduction, enzymatic hydrolysis, and flavour masking have improved specific attributes, yet no PBMA fully replicates the holistic sensory experience of dairy. Emerging approaches, including blended formulations, precision fermentation, and artificial intelligence (AI)-driven optimisation, show promise in narrowing these gaps. Nonetheless, a complete replication of UHT cow milk remains elusive, highlighting the need for continued research and innovation to either approximate dairy properties more closely or enhance PBMA’s unique qualities to drive consumer acceptance. Full article

Subclinical hypocalcemia (SCH) is one of the most prevalent metabolic disorders in early-lactation dairy cows, yet its multifaceted physiological effects are often overlooked due to the absence of clinical symptoms. This study aimed to characterize SCH through an integrative assessment of blood biochemical markers, in-line milk composition, and sensor-derived behavioral traits. Seventy-five Holstein cows between 2 and 21 days in milk were classified into hypocalcemic (group 1) (Ca < 2.0 mmol/L; n = 20) and healthy (group 2) groups (n = 55). Blood samples, milk data, and rumination metrics were evaluated, and group differences were analyzed using Welch’s t-test and Pearson correlations. Cows with SCH exhibited significantly lower concentrations of Ca, PHOS, Mg, ALB, TP, GLUC, and Fe, indicating disruptions in mineral balance, protein metabolism, and energy status. Hepatic indicators (AST, ALT, GGT) did not differ between groups, whereas CREA was significantly lower in hypocalcemic cows, suggesting altered muscle metabolism rather than impaired renal function. Although differences in milk yield, composition, and rumination time did not reach statistical significance, hypocalcemic cows showed consistent biological tendencies toward reduced milk components and lower milk temperature. Correlation analysis revealed strong physiological linkages among Ca, ALB, P, TP, and Fe, underscoring the interconnected nature of mineral and protein metabolism in early lactation. These findings demonstrate that SCH is associated with coordinated biochemical and behavioral changes even in the absence of clinical signs. Integrating blood biomarkers with real-time sensor data provides a more comprehensive understanding of calcium-related metabolic challenges and highlights the potential of precision-livestock technologies for early detection. Future studies incorporating ionized calcium and longitudinal sampling are needed to refine diagnostic thresholds and improve predictive monitoring of SCH in dairy herds. Full article

The health and productivity of dairy cows are critical factors in sustainable livestock management. Along with the rapid rise in intelligence and technology, applying intelligence in livestock management helps in monitoring and provide precise and effective care for the cattle herd. This research designs an intelligent system that can assist the farmers and predict Gir cows’ diseases and a support system powered by Artificial Intelligence (AI). The proposed system integrates Internet of Things (IoT) and sensors to track and monitor critical health parameters of the Gir cow, which includes the step count, lying time, rumination time, heart rate, and various environmental factors contributing to the well-being of the cow. The data points that are gathered from the sensors is then processed and analysed using machine learning (ML) algorithms, including Random Forest (RF), decision tree (DT), Logistic Regression, K-Neighbours, and Support Vector Machine (SVM) to predict abnormalities including diseases such as lameness, mastitis, heat stress, and digestive problems. The AI techniques used in the system involve complex data processing and pattern recognition to identify early signs of diseases. The RF and DT ML models achieved the highest accuracy (100%), while SVM demonstrated robust performance with 94% accuracy. Integrating real-time monitoring with predictive analytics enables early detection of health issues, allowing timely interventions and improving overall herd management. The proposed system enhances cow welfare and optimises farm productivity but also has the potential to revolutionise the dairy industry. The complex intelligent system provides a reliable and efficient platform for disease prediction and herd management, and can significantly contribute to the sustainability and profitability of dairy farming, thereby shaping the future of the industry. Full article

Background/Objective: Milk and fermented dairy products are widely consumed functional foods and beverages, offering not only essential nutrients but also bioactive compounds with potential to modulate host immunity, metabolism, and the gut microbiome. This narrative review aims to synthesize current knowledge on the relationship between dairy consumption, gut microbiome, immune modulation, adverse reactions to food, physical performance and cardiometabolic health. Methods: An extensive literature analysis was conducted to explore how milk and fermented dairy products modulate the gut microbiome and influence the immune and cardiometabolic health. This study synthesis focused on key dairy bioactive compounds, such as probiotics, miRNAs, milk-derived peptides and exosomes and on evaluating their proposed mechanisms of action in inflammation and metabolic regulation, and their possible influence on physical performance through gut–microbiome interactions. Additionally, advances in metagenomic and metabolomic technologies were reviewed for their potential to uncover host–microbiota interactions relevant to precision nutrition strategies. Results: Fermented dairy products have shown potential in promoting beneficial bacteria growth such as Lactobacillus and Bifidobacterium, short-chain fatty acid synthesis and reduction in proinflammatory biomarkers. Specific dairy-derived peptides and exosomal components may further support gut barrier integrity, immune regulation and improve physical performance and reduce cardiometabolic risk factors. Additionally, emerging evidence links individual gut microbiota profiles to specific metabolic responses, including tolerance to lactose and bovine milk proteins. Conclusions: Integrating microbiome science with traditional nutritional paradigms enhances our understanding of how dairy influences immune and cardiometabolic health. Overall, current evidence suggests that investigating dairy-microbiome interactions, alongside lifestyle factors such as physical activity, may inform future personalized nutrition strategies aimed at supporting metabolic and immune health. Full article

This study evaluated six supervised machine learning (ML) models for early detection of subclinical ketosis and low-grade ruminal acidosis in dairy cows during the transition period. Ninety-four Holstein cows were monitored for 21 days postpartum using in-line milk analyzers and intraruminal sensors that continuously recorded milk composition, behavioral, and physiological parameters. Based on clinical examination, blood β-hydroxybutyrate concentration, and fat-to-protein ratio, cows were classified as healthy (n = 44), subclinical ketosis (n = 24), or subclinical acidosis (n = 26). Among the tested models, Random Forest and XGBoost achieved perfect accuracy within this dataset, while Logistic Regression reached 89.5%, Decision Tree 84.2%, and both Naive Bayes and Support Vector Machine 78.9%. These results suggest that ensemble approaches, particularly Random Forest and XGBoost, show strong potential for integration with precision livestock technologies, but their apparent performance should be interpreted cautiously and confirmed in larger, multi-farm studies. Full article

Optical scattering techniques often lead to simplified assumptions about secondary factors, such as neglecting the absorption effect of particles or the residual particle size micro-distribution after homogenization; these are made to enhance measurement efficiency. However, such simplifications can introduce systematic errors in precise detection. This study uses the scattering–transmission ratio composition determination method as an example, revises the basic scattering–transmission ratio model to incorporate absorption effects, and demonstrates the coefficient calculation process. Furthermore, Mie key coefficients, including the particle size micro-distribution—which are core parameters of this method—are derived. Based on these models, effective particles from image processing are analyzed to assess the impact of these two factors. The results demonstrate the joint influence of the micro-distribution and absorption characteristics of milk fat particles on Mie key coefficients and composition determination, exhibiting non-uniform enhancement and reduction effects. Specifically, at a wavelength of 800 nm, the scattering–transmission ratio of the modified model increases by a factor of 1.56 compared to the traditional model at a volume concentration of 0.5%, while at 3.3% concentration, the scattering–transmission ratio of the modified model is approximately one-third of the traditional model. These findings provide a theoretical basis for developing dairy product quality assessment technologies. Full article

Splicing regulatory sequences are cornerstones for exon recognition. Mutations that modify them can severely compromise mRNA maturation and protein production. A wide range of mutations, including SNPs and InDels, can influence splicing regulatory signals either directly (e.g., altering canonical donor and acceptor dinucleotides) or indirectly (e.g., creating cryptic splice sites). CSN1S1 and CSN1S2 genes encode for the two main milk proteins, αs1 and αs2 caseins, respectively. They represent a remarkable and unique example of the possibilities for alternative splicing of individual genes, both due to the high number of alternative splices identified to date and for recognized allele-specific splicing events. To date, at least 13 alleles of CSN1S1 originating from mutations that affect canonical splice sites have been described in Bos taurus (CSN1S1 A, A1, and H), Ovis aries (E, H, and I), Capra hircus (D and G), Bubalus bubalis (E, F) and Camelidae (A, C, and D). Similarly, allele-specific splicing events have been described at the CSN1S2 locus in B. taurus. (CSN1S2 D), C. hircus (CSN1S2 D), B. bubalis (CSN1S2 B, B1, and B2), Equus asinus (CSN1S2 I B), and Camelidae. This review highlights that mutations affecting canonical splice sites, particularly donor sites, are significant sources of genetic variation impacting the casein production of the main dairy livestock species. Currently, a key limitation on this topic is the lack of detailed functional and proteomic studies. Future research should leverage advanced omics technologies like long-read transcriptomics and allele-resolved RNA sequencing to characterize these splicing mechanisms, guiding precision breeding strategies. Full article

Computer vision is rapidly transforming the field of dairy farm management by enabling automated, non-invasive monitoring of animal health, behavior, and productivity. This review provides a comprehensive overview of recent applications of computer vision in dairy farming management operations, including cattle identification and tracking, and consequently the assessment of feeding and rumination behavior, body condition score, lameness and lying behavior, mastitis and milk yield, and social behavior and oestrus. By synthesizing findings from recent studies, we highlight how computer vision systems contribute to improving animal welfare and enhancing productivity and reproductive performance. The paper also discusses current technological limitations, such as variability in environmental conditions and data integration challenges, as well as opportunities for future development, particularly through the integration of artificial intelligence and machine learning. This review aims to guide researchers and practitioners toward more effective adoption of vision-based technologies in precision livestock farming. Full article

While the Ankom RF system facilitates efficient high-throughput in vitro fermentation studies, its high cost and limited flexibility constrain its broader applicability. To address these limitations, we developed and validated a low-cost, modular gas monitoring system (FerME), assembled from commercially available components. To evaluate its performance and reproducibility relative to the Ankom RF system (Ankom Technology, Macedon, NY, USA), in vitro rumen fermentation experiments were conducted under strictly controlled and identical conditions. Whole rumen contents were collected approximately 2 h post-feeding from individual mid- or late-lactation dairy cows and immediately transported to the laboratory. Each fermenter received 50 mL of processed rumen fluid, 100 mL of anaerobically prepared artificial saliva buffer, and 1.2 g of the donor cow’s diet. Bottles were sealed with the respective system’s pressure sensors, flushed with CO₂, and incubated in a 50 L water bath maintained at 39 °C. FerME (New Taipei City, Taiwan) and Ankom RF fermenters were placed side-by-side to ensure uniform thermal conditions. To assess the effect of filter bag use, an additional trial employed Ankom F57 filter bags (Ankom Technology, Macedon, NY, USA; 25 μm pore size). Trial 1 revealed no significant differences in cumulative gas production, volatile fatty acids (VFAs), NH₃-N, or pH between systems (p > 0.05). However, the use of filter bags reduced gas output and increased propionate concentrations (p < 0.05). Trial 2, which employed filter bags in both systems, confirmed comparable results, with the FerME system demonstrating improved precision (CV: 4.8% vs. 13.2%). Gas composition (CH₄ + CO₂: 76–82%) and fermentation parameters remained consistent across systems (p > 0.05). Importantly, with 12 pressure sensors, the total cost of FerME was about half that of the Ankom RF system. Collectively, these findings demonstrate that FerME is a reliable, low-cost alternative for real-time rumen fermentation monitoring and could be suitable for studies in animal nutrition, methane mitigation, and related applications. Full article

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

Virtual fencing (VF) technology represents an innovative approach to livestock management, utilizing GPS-enabled collars to establish invisible boundaries through auditory and mild electrical stimuli. While VF offers potential benefits such as enhanced pasture management flexibility and reduced labor costs, its widespread adoption faces challenges including high initial investment costs, connectivity issues, GPS accuracy limitations, potential device durability concerns, and individual animal variability in learning and response. Furthermore, despite studies showing rapid learning and generally minimal long-term welfare impacts, questions remain regarding optimizing training protocols, addressing occasional short-term behavioral disruptions and collar abrasions, assessing long-term welfare effects across diverse systems (especially intensive and dairy), and improving scalability. To comprehensively assess the potential and limitations of this technology and guide its future development and implementation, a review integrating existing knowledge on the efficacy, welfare implications, and practical applications of VF in cattle production systems is essential. This review examines the efficacy, welfare implications, and practical applications of VF in cattle production systems. Studies demonstrate that cattle rapidly learn to associate auditory cues with electrical pulses, achieving high containment rates (≥90%) within days, with minimal long-term welfare impacts as indicated by stable cortisol levels. However, short-term behavioral disruptions and occasional collar-related abrasions have been reported, particularly in dairy cattle. While VF enhances pasture management flexibility and reduces labor costs, challenges such as connectivity issues, individual animal variability, and high initial investment costs limit its widespread adoption. The findings suggest that VF is a promising tool for precision livestock farming, though further research is needed to optimize training protocols, assess long-term welfare effects, and improve scalability across diverse farming systems. Full article

As a core species in the global livestock industry, cattle play an irreplaceable role in human food security and economic development. Beef cattle and dairy cattle meet the dietary needs of billions of people around the world by providing high-quality protein and dairy products, respectively. With the growth in population and the intensification of the pressure of climate change, traditional breeding techniques may be unsuitable to meet the increasingly growing demands for sustainable and highly adaptable processes. In recent years, the rapid development of genomics, bioinformatics, and gene-editing technologies has provided unprecedented tools and perspectives for the genetic improvement of cattle, driving the precise design and efficient development of new cattle breeds. However, the development of new cattle breeds still faces multiple bottlenecks pertaining to scientific, ethical, and industrialization aspects, which can be addressed through interdisciplinary collaboration. In this review, we will systematically assess the technological progress in the genetic breeding of beef cattle and dairy cattle, analyze the integration path of traditional breeding and modern biotechnology, and explore the future directions of cattle breeding research under the sustainable development goals, with the aim of providing theoretical support for cattle breeding. Full article