Search Results (2,206)

Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak containment. This study proposes a veterinary-aware digital traceability framework that embeds herd health data, milk-quality testing, and inspection outcomes directly into batch-level EPCIS event records. By representing veterinary public health controls as structured, machine-actionable traceability elements, the framework enables automatic logging of mandatory control points, systematic compliance verification, and rule-based risk state transitions within standard EPCIS infrastructures. Using regulation-consistent dairy simulations modeling delayed Listeria detection during maturation, we evaluate the operational impact of event-level causal traceability within the proposed architecture. Compared with conventional time-window recall strategies, provenance-based trace-forward queries reduced recall scope under the evaluated synthetic scenarios. Integrating structured veterinary controls into EPCIS-based traceability systems supports automated regulatory evidence generation and more targeted recall decisions, contributing to improved auditability and reduced food waste in dairy supply chains. Full article

In the context of modern ruminant nutrition, increasing attention is being directed toward the valorization of agro-industrial by-products as alternative feed ingredients that enhance nutrient utilization efficiency while supporting the sustainability of animal production systems. Milk thistle (Silybum marianum) oilseed cake, a by-product of oil extraction, has emerged as a resource of growing interest due to its favorable nutritional profile and the presence of bioactive compounds with functional properties. This review critically analyzes recent scientific literature addressing the use of milk thistle oilseed cake in ruminant nutrition, highlighting its potential practical relevance as a functional feed ingredient. The available evidence suggests that milk thistle oilseed cake may support inclusion in ruminant diets at moderate levels; however, controlled in vivo studies remain limited, and several proposed mechanisms are inferred from studies on structurally analogous polyphenol-rich by-products rather than from milk thistle cake itself. Further research is needed before precise inclusion recommendations can be established. Special attention is given to the bioactive fraction dominated by the silymarin complex, which may interact with rumen digestive and fermentative processes, influencing nutrient utilization efficiency and oxidative stability. Overall, the findings suggest that milk thistle oilseed cake represents a promising feed resource that aligns with sustainable and efficiency-oriented feeding strategies in modern ruminant production systems. Full article

Artificial intelligence (AI) in the dairy industry is helping to enhance efficiency and sustainability across various key areas, such as animal health, milk quality, milk production analysis, and the monitoring and improvement of dairy products, such as cheese, yogurt, and butter. Beyond productivity, AI-based tools enable companies to measure their environmental impact and forecast the demand for dairy products—factors that are equally critical to this industry. This review aims to address the use of AI from the animal to the final product, within the context of its environmental impact, and to identify areas that require further attention due to their vulnerability in terms of robustness and effectiveness. Full article

Somatic cell count (SCC) in milk is widely used as an indicator of intramammary infections in dairy sheep and is routinely monitored by the dairy industry as a marker of milk quality. This study aimed to evaluate the effect of SCC levels on milk production, composition, colour, coagulation properties, and cheese-making ability in Manchega dairy sheep. A total of 752 individual milk samples were analysed. To normalise SCC distribution, the somatic cell score (SCS) was calculated and samples were classified into SCS classes. Increasing SCS significantly reduced daily milk yield and lactose content, increased milk pH, and decreased lightness (L*). Higher SCS was also associated with impaired coagulation properties, including longer rennet clotting time (RCT) and curd firming rate (k₂₀), as well as reduced curd firmness (A₃₀, A₆₀). Similar effects were observed for modelled coagulation parameters, with delayed RCTeq and reduced kCF and CFp. Regarding cheese-making ability, SCS significantly affected curd humidity and protein recovery, whereas no significant effects were detected for dry curd yield or fat recovery. Overall, elevated somatic cell counts were associated with a reduction in the technological quality of Manchega sheep milk, particularly affecting coagulation behaviour and curd characteristics. These results underline the importance of controlling SCC levels in dairy sheep systems for both udder health monitoring and maintaining milk suitability for cheese-making. Full article

In this study, a low-cost AS7265x-based multispectral electronic tongue system was developed for estimating milk composition and adulteration indicators and supported with an explainable artificial intelligence (XAI) framework. Experimental analyses were conducted on 190 augmented commercial milk samples, where fat, protein, solids-not-fat (SNF), density, freezing point, and added water ratio were treated as target variables. Sensor data were modeled as RAW, DERIVED, and FUSION feature sets, and regression performance was compared using Random Forest, Gradient Boosting, AdaBoost, KNN, and XGBoost. Model validation was carried out with both five-fold cross-validation and Leave-One-Out (LOO) strategies to assess field-level generalizability. Results showed that a narrow-band, low-cost optical sensor platform can estimate not only fat and protein but also SNF, density, and freezing point with high accuracy. Within the XAI framework, permutation-based importance analysis and SHAP were used to identify critical spectral bands for each target parameter, enabling data-driven recommendations for band-oriented sensor design optimization. The study presents a scalable methodology that integrates low-cost sensor design, multi-parameter quality estimation, and explainable modeling beyond traditional fat–protein-focused approaches. Across all six targets, the XAI analysis consistently identified the near-infrared channel at 860 nm (asIR_3) as the most informative band, reflecting the combined effect of water absorption and Mie scattering by fat globules; the visible channel at 680 nm (asVIS_4) emerged as a secondary band, reflecting dissolved-matter scattering. These bands are therefore the natural starting point for cost-reduced versions of the sensor. Among the compared feature sets (RAW, DERIVED, FUSION), the 18-band RAW configuration provided the most balanced performance across all six targets. Full article

Milk color reflects the optical output of a complex colloidal system governed by protein micelles, fat globules, and serum phase interactions. In this study, we evaluated whether CIE Lab* color parameters can explain variation in milk composition and somatic cell count (SCC) using Lasso-based multiple linear regression and Extreme Gradient Boosting (XGBoost). A total of 119 Holstein milk samples were analyzed for fat, protein, lactose, dry matter, electrical conductivity, freezing point, and SCC, and five color indices (L*, a*, b*, Hue, and Chroma) were used as predictors. Model robustness was evaluated using 10-fold cross-validation and an independent 80/20 train–test split. In regression analyses, Lasso explained 32.7% of protein variation (R² = 0.327), 26.3% of dry matter (R² = 0.263), 22.8% of lactose (R² = 0.228), and 19.1% of fat (R² = 0.191). Spectral tone parameters (a*, Hue, and Chroma) were consistently retained as key predictors, whereas L* showed a limited contribution. SCC exhibited weak direct associations with color traits but was significantly related to electrical conductivity (p < 0.05), indicating inflammation-driven ionic changes rather than pigment effects. In classification analysis (SCC ≥ 200,000 cells/mL), the XGBoost model achieved 74% accuracy and an AUC of 0.69 in the independent test set, with Chroma and electrical conductivity identified as the most influential features. These findings suggest that, among the evaluated color variables, Chroma provided the most relevant information for discriminating SCC status, whereas the overall contribution of milk color traits to compositional prediction remained moderate. Therefore, color-derived measurements should be interpreted as instrument-based optical indicators that may complement, but not replace, conventional milk quality assessments. Full article

Background/Objectives: Breast engorgement and associated pain, often resulting from milk stasis, impaired circulation, and tissue edema, may adversely affect maternal and neonatal health, as well as maternal mental health and quality of life in early postpartum women. Despite the availability of massage-based methods, the evidence supporting structured self-care programs remains limited. This study aims to develop and evaluate a natural breast self-circulation program and assess its effectiveness in improving breast engorgement, mental health, and quality of life among early postpartum women. Methods: The sample for this quasi-experimental study, comprising both an experimental group (n = 36) and a control group (n = 36), consisted of pregnant women at or before 37 weeks of gestation who intended to breastfeed. Breast engorgement and circumference, stress, anxiety, and EQ-5D-3L scores were measured before and after implementation of the intervention (the natural breast self-circulation program). Results: The program significantly reduced breast engorgement (F = 33.97, p < 0.001, partial η² = 0.327), breast circumference (F = 105.52, p < 0.001, partial η² = 0.601), and anxiety (F = 37.43, p < 0.001, partial η² = 0.348) in women during the early postpartum period. Conclusions: These findings demonstrate that an early postpartum natural breast self-care program can alleviate breast engorgement and maternal anxiety. They provide a rationale for implementing self-managed breast care. Active implementation of this program may help alleviate physical and emotional difficulties and enhance confidence in breast care among pregnant women. Full article

Modeling milk yield in dairy cows is essential for improving management decisions, but traditional lactation curve models often fail to capture individual variability. Machine learning approaches offer greater flexibility; however, their performance in small, within-herd datasets and their reliance on explicit cow identification remain unclear, particularly in grazing systems. This study aimed to evaluate whether routinely measured biological traits can substitute for cow identification in machine learning models for predicting daily milk yield within a herd under limited data conditions. The dataset comprised 62 lactations from 48 Holstein–Friesian cows in a grazing system. Two machine learning models were developed: one including cow identification (With ID) and another excluding cow identification but incorporating milk quality traits, body weight, and body condition score (Without ID). Both models were compared with the Wood lactation model fitted to individual cows. The With ID and Without ID models achieved $R^2$ values of 0.97 and 0.93 and RMSE values of 1.2 and 1.6 ${\mathrm{kg} \mathrm{d}}^{-1}$, respectively. Both machine learning models outperformed the Wood model fitted individually to each cow ($R^2$ < 0.90; RMSE > 2.03 ${\mathrm{kg} \mathrm{d}}^{-1}$), which represents an implicitly cow-specific approach. The model including cow identification therefore served as a machine learning analogue to this benchmark. Importantly, the trait-based model closely matched the performance of the cow-specific model. These results demonstrate that machine learning models based on routinely measured traits provide a practical approach for predicting within-herd milk yield from small datasets, while retaining much of the accuracy of cow-specific models. Full article

Milk production in tropical smallholder systems is constrained by limited high-quality roughage during the hot–dry season. Sweet sorghum silage is drought-tolerant and may replace corn stover silage. Twelve Holstein–Friesian crossbred cows were assigned to the same commercial concentrate plus either corn stover silage or sweet sorghum silage as the primary roughage source (n = 6 per diet). Intake, apparent digestibility, milk yield and composition, and feed-use efficiency were evaluated on day 15 and 30 and analyzed using linear mixed-effects models with cow as a random effect. Compared with corn stover silage, sweet sorghum silage increased dry matter intake (p < 0.05) and improved the digestibility of fibre fractions, including crude fibre, NDF and ADF (p ≤ 0.003), while crude protein- and nitrogen-free extract digestibility were not different (p > 0.05). Milk yield, 4% fat-corrected milk, energy-corrected milk, and feed-use efficiency indices were unaffected by silage source (p > 0.05). Milk protein concentration was higher with sweet sorghum silage (treatment effect p < 0.05), whereas milk fat and lactose were unchanged. Sweet sorghum silage can therefore replace corn stover silage in tropical dairy diets, improving intake and fibre utilization without compromising milk output. Full article

Raw milk dairy products, an integral part of Italian food heritage, are the primary products of small-scale farms in mountain regions where pasture is seasonal. While raw milk dairy products offer potential health benefits, their physicochemical properties make them susceptible to foodborne pathogens. Long-term surveillance of these products is essential to safeguard consumer health. Here, we present a fourteen-year microbiological surveillance of raw milk dairy products and intermediate matrices from northeastern Italy’s alpine areas, analyzing coagulase-positive Staphylococci (CPS), β-glucuronidase-positive Escherichia coli, Listeria monocytogenes, and Shiga toxin-producing E. coli (STEC). The most frequently detected pathogens were CPS and β-glucuronidase-positive E. coli, with up to 19.6% and 51.7% of samples exceeding regulatory limits, respectively. Butter, curd, and fresh cream were the most contaminated matrices. Detection rates of staphylococcal enterotoxins, L. monocytogenes, and STEC aligned with European detection averages (6.7%, 2.6%, and 2.1%, respectively). These findings underscore the necessity of Good Hygiene and Management Practices, together with regular microbiological monitoring to mitigate contamination risks, supporting the safety and quality of traditional raw milk dairy products in alpine regions. Full article

This study aimed to investigate the effects of pterostilbene (PTE) on the intestinal barrier function, antioxidant capacity, lipid metabolism, and microbial and metabolite homeostasis of suckling piglets via its action on breast milk. Findings indicate that PTE supplementation enhanced the antioxidant status of mature milk and strengthened intestinal barrier function in piglets. Specifically, PTE enhanced intestinal antioxidant status and fatty acid β-oxidation in piglets by regulating the PI3K-AKT and SIRT1-Nrf2/Keap1 signaling pathways. 16S rDNA sequencing and Liquid Chromatography–Mass Spectroscopy (LC–MS) identified breast milk and gut microbiota and their metabolites, respectively. Results indicate that PTE significantly elevated levels of amino acid derivatives in colostrum (Glutathione Reducedform (GSH) and N-acetyl-L-glutamate (NAG)), whilst concurrently reducing levels of glycerophospholipid-related metabolites in both colostrum and mature milk (p < 0.05). Moreover, PTE supplementation markedly altered the composition of the colonic mucosal microbiota in piglets, with Faecalibacterium, Mucispirillum and Ruminococcus identified as key beneficial microbial markers of the colonic mucosa. Combined multi-omics revealed strong correlations in microbial community composition between mature milk and the colon, identifying glycerophospholipid metabolism as a key metabolic pathway that may be associated with the regulatory effects of PTE on milk and the piglet colon. In conclusion, the PTE supplement can improve the quality of breast milk and have a positive impact on the intestinal homeostasis of the offspring. Full article

This study was conducted to evaluate the effect of varying dietary energy levels on milk production, feed intake, nutrient digestion and metabolism, and antioxidation function of lactating donkeys, and integrating 16S rRNA gene sequencing, metabolomics, and proteomics to comprehensively reveal the underlying regulatory networks. A single-factor, completely randomized design was used in this study. Twenty-four Dezhou donkeys with similar milk yield (3.25 ± 0.46 kg/d), lactation days (29 ± 4.34 d), parities (4.17 ± 1.17), and body weight (256 ± 34 kg) were randomly divided into three dietary treatments (n = 8), and either a fed high-energy diet (DE = 13.1 MJ/kg, HED), medium-energy diet (DE = 12.4 MJ/kg, MED), and low-energy diet (DE = 11.7 MJ/kg, LED). The experiment period included 2 weeks for adaptation and 8 weeks for data and sample collection. Orthogonal polynomial contrasts were used to evaluate the linear and quadratic effects of increasing dietary energy. There were no significant interaction effects between dietary energy level and lactation week on any milk production and quality variables (p > 0.05). Increasing dietary energy level increased DMI, milk production, milk production efficiency, and milk components (linear and quadratic; p < 0.05). Increasing dietary energy improved the digestibility of DM and neutral detergent fiber (linear; p < 0.05), and crude protein digestibility, energy digestibility and metabolism, and nitrogen metabolism (quadratic; p < 0.05). However, it decreased BHBA and NEFA concentrations (linear; p < 0.05). Furthermore, increasing dietary energy first increased then decreased the activities of GSH-PX, SOD, and T-AOC (linear and quadratic; p < 0.05), while increasing the MDA content (linear; p < 0.05). Compared with HED and MED, LED increased the relative abundance of the genera unclassified_f_Syntrophomonadaceae, Christensenellaceae_R-7_group and Treponema_2. Compared with HED, MED increased the relative abundance of the genera Ruminiclostridium_5, Ruminiclostridium_1, Family_XIII_UCG-001, unclassified_o__Clostridiales and norank_f__PL-11B10. Thyroid hormone synthesis, tyrosine metabolism, and glutathione metabolism pathways are critical metabolic routes; these pathways can enhance energy metabolism and antioxidant function, thereby improving the milk production performance of lactating donkeys. In conclusion, the digestible energy of 12.40 MJ/kg was optimal for the milk performance of lactating donkeys, whereas excessively high dietary energy (13.1 MJ/kg) may reduce milk performance. Full article

Coconut milk-based Filipino foods provide a favorable environment for microbial growth and are highly susceptible to spoilage. Traditionally, spoilage in such foods has been assessed through subjective sensory evaluation, a method that often lacks consistency and accuracy. The present study introduces an electronic nose system employing Support Vector Machine (SVM) algorithms to objectively and quantitatively determine spoilage in coconut milk-based Filipino foods, including Bicol Express, Ginataang Langka, Laing, Bilo-bilo, Maja Blanca, and Ginumis. The developed system integrates six MQ gas sensors connected to an Arduino Nano and a Raspberry Pi 4B to detect and process volatile organic compounds emitted from the foods. The SVM algorithm was selected for its effectiveness in high-dimensional spaces and its ability to construct a binary classifier capable of distinguishing between spoiled and fresh samples. Dimensionality reduction in sensor data was achieved using Principal Component Analysis, which further enhanced classifier performance. System evaluation results demonstrated a high classification accuracy of approximately 98.95%, indicating the robustness of the proposed approach. The utilization of this technology offers significant benefits, not only for individuals with impaired olfactory function but also for the food industry, providing a reliable tool for food quality control and safety. Moreover, the outcomes suggest broader applicability to other perishable food products, with potential contributions to improved global food safety and storage practices. Full article

Cryoprotectants are used to protect biological cells from damage caused by freezing. This study aimed to determine the effect of various types of cryoprotectant on the survival rate of freeze-dried Limosilactobacillus frumenti (L. frumenti) used as a starter culture for cucumber fermentation. Mixtures of freeze-dried L. frumenti with cryoprotectants were prepared using two different ratios (1:2 and 1:10). The survival rate of L. frumenti was determined by viable cell counts (CFU/mL) after freeze-drying, and fermentation performance was evaluated in terms of physicochemical quality and sensory evaluation. Skim milk proved to be the most effective cryoprotectant, yielding a survival rate of approximately 70% (70.07% for the 1:10 ratio and 70.01% for the 1:2 ratio) after 24 h of storage at 4 °C. Sensory evaluation indicated that cucumber fermentation prepared with freeze-dried L. frumenti mixed with skim milk (ratio 1:10) was the most preferred by panelists. Full article