Dairy Research Articles

Solvent-Based and Matrix-Matched Calibration Methods on Analysis of Ceftiofur Residues in Milk and Pharmaceutical Samples Using a Novel HPLC Method.

The matrix effect is accepted as one of the critical factors that affect trueness, and it is especially important in the analysis of critical compounds, such as drugs, toxins and pesticides, in complex matrices. Ceftiofur (CEF) is a leading drug used in dairy industry for pulmonary infections. Because milk has a complex matrix, which is rich in fats, proteins and many other compounds, the determination of CEF in milk products deals with issues of trueness. On the other hand, pharmaceuticals also have a complex composition as they contain not only the active pharmaceutical ingredient but also many excipients, such as preservatives and pH modifiers. Due to its frequent use, its residues can be found in meat products or exist as an excreted compound in urine, milk and so on. The aim of this study was to investigate the effect of different calibration methods on quantitative estimation of CEF in milk and pharmaceutical samples. CEF was analysed using a new HPLC method, in which separation was achieved on a C18 bonded fused-core silica particle column by using isocratic elution; the method was optimized by testing different chromatographic conditions and validated according to the ICH Q2(R1) and United States Pharmacopeia guidelines. Interestingly, it was found that CEF signals in milk samples were higher than the ones at the same level prepared in calibration solutions with a ratio of 11.28:1. This finding reveals the necessity of matrix-matched calibration for accurate quantitative determination of CEF in milk samples. Moreover, the milk in the market and the most used pharmaceutical formulations were analysed using the current method.

Nanotechnology and Nanostructures in Food Science: Advancements & Applications

Nanotechnology is a field of science that deals with materials at a nano-scale i.e. within the range of 1 to 100 nano meters. Nanotechnology and nanostructures are revolutionizing food science by providing an innovative, effective, and sustainable approach. Nanotechnology is the science of materials which are extremely small, typically ranging from 1-100 nanometers, which finds various applications in diverse fields including but not limited to food science. Different industries like food industry, dairy industry, fruits & vegetables industry, and wine industry would benefit by implementing and integrating nanotechnology. The advancements and applications of nanotechnology and nanostructures in food science have not been explored thoroughly and require a lot more research. However, public concern about the safety of nanomaterials, toxicological aspect of nanomaterials, and food safety concerns still remains a hot topic for further research. This article provides a brief overview of the applications and advancements of nanotechnology in food science.

Got milk? Got cybersecurity risks! Unraveling ransomware threats in the German dairy industry

PurposeThis study aims to explore the vulnerabilities of the dairy industry to ransomware threats, focusing particularly on the upstream supply chain and applying routine activity theory (RAT) to understand the evolving dynamics of cybercrime in critical infrastructure sectors.Design/methodology/approachUtilizing expert interviews and network analysis, this research investigates the exploitation of complex supply chain vulnerabilities by motivated offenders. It delves into the intricate interplay between digital threats and physical supply continuity.FindingsThe study uncovers that ransomware threats transcend digital boundaries, manifesting in disruptions to physical operations and presenting significant risks to food security. It underscores the threat posed by the convergence of information technology (IT) and operational technology (OT), emphasizing the urgent need for heightened awareness and robust defenses against this substantial menace.Practical implicationsAddressing cyber vulnerabilities in critical sectors like dairy ensures not only the security of operations but also safeguards broader societal interests such as food security. Collaboration and proactive measures are essential to mitigate potential social and economic disruptions caused by cyber incidents.Originality/valueThis research fills a knowledge gap by shedding light on the nexus between cyber threats and supply chain resilience. It emphasizes the need for industries to adapt traditional defense mechanisms in the face of sophisticated digital adversaries.

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

Retrospective analysis of prevalence and risk factors of bovine lameness in dairy farms in South Africa

Lameness is a crippling problem in the dairy industry, posing a significant welfare challenge and leading to premature culling. The study aimed to determine the seasonal prevalence of lameness in two different provinces of South Africa. The data was collected in two local municipalities (Mpofana and Raymond Mhlaba). However, the computer software systems of four randomly selected dairy farms were used to collect retrospective secondary data for lameness and predisposing factors (2018–2022). The data's targeted parameters include breed, parity, weight, stage of lactation, diseases diagnosed (lameness), age, and milk production trends. The highest prevalence was observed in uMpofana municipality (58.8%) compared to Raymond Mhlaba municipality (41.2%). Among cattle above nine years of age, the prevalence of lameness was the highest (36%), compared to 7-8-year-old (24%), 4-6-year-old (21.7%), and 1-3-year-old (14.3%) cattle. Additionally, the highest prevalence of lameness was observed during the summer season, followed by spring, winter, and autumn. The strongest association is kept for the right front leg (p<0.0001), followed by the left front leg (p<0.001), right back leg (p<0.001), and left back leg (p<0.001). In conclusion, this study provides baseline information on several key factors influencing the prevalence of lameness in dairy cattle in various farms in uMpofana municipality and Raymond Mhlaba municipality. The study findings could be used for farm managerial decision-making and epidemiological interventions. Given the high prevalence of lameness in dairy farms in uMpofana municipality, there is a need for more focused research aimed at identifying the specific causes of lameness in these localities.

Exploring microbial diversity in Kermanshah province’s Kermanshahi oil through DGGE and sequencing analysis

BackgroundGhee, known as “roghane heiwâni,” or “Kermanshahi oil” is a traditional fermented butter-like product highly esteemed for its nutritional value. Ghee is prepared using traditional methods and has substantial potential as a reservoir of probiotic microorganisms. Previous research delved into isolating and identifying lactic acid bacteria (LAB) in Kermanshahi through culture and PCR sequencing. This study seeks to elucidate the microbial profiles and diversity within Kermanshahi using culture, Denaturing Gradient Gel Electrophoresis (DGGE), and sequencing methodologies.MethodsTwenty samples of Kermanshahi oil were meticulously gathered from diverse locales across Kermanshah province. These samples were cultivated under specialized conditions in MRS and M17 environments spanning 24 to 72 h. Following DNA extraction, amplification of the 16SrRNA gene sequences was performed, culminating in sequencing for conclusive identification of the isolates. Furthermore, the DGGE technique was directly employed to separate and identify various species present in the oil samples utilizing bioinformatics software.ResultsSequencing outcomes revealed a diverse array of microorganisms among the isolates, with Lactobacillus constituting 43%, Streptococcus comprising 27.6%, Enterococcus at 4.61%, and yeasts at 7.6%. Other species exhibited lower frequencies, encompassing Rhizobium, Bacillus coagulans, and Staphylococcus hominis.ConclusionsThe isolation of a diverse spectrum of probiotic microorganisms underscores their potential utility in the realm of industrial dairy product production. These findings allude to the possibility of integrating these valuable microorganisms, which have historically been associated with traditional products, into the contemporary dairy industry. As consumer interest in probiotic-enriched products surges, the insights gained from this study pave the way for harnessing the benefits of Kermanshahi-derived probiotics.

Quantitative and qualitative responses of dairy cattle herds’ production to proper feeding

The primary objectives include the development of the agro-industrial complex of the Republic of Moldova and ensuring the population has access to quality products. The assessment of qualitative and quantitative indicators of milk production and processing in the country, reflects the dynamics of the dairy industry’s development and highlights the main issues that need solutions; hence, research on this matter remains relevant. The aim of this study was to analyze both the quantitative and qualitative indicators of milk production, at the educational dairy farm of the Technical University in Moldova. Additionally, a review of research on the development of recipes based on feedstock and an examination of factors influencing the quality of dairy products were conducted. It was concluded that overall, the quality of milk at the educational dairy farm, in terms of fat and density content, and organoleptic properties, is good.

Chromatography and Spectroscopic Technique-Based Rapid Characterization of Nano-Carrier Pharmaceuticals.

A nanocarrier is a novel colloidal system whose particle size ranges between 1-100 nm. It is extensively utilized in drug delivery and various other sectors, such as the pharmaceutical, food, and dairy industries. The nanocarrier systems, including solid lipid nanoparticles, micelles, liposomes, and other encapsulated compounds, have improved stability, solubility, bioavailability, and quality. Nanocarriers offer therapeutic effectiveness with low toxicity because of their biocompatibility and ability to cross body barriers. Various analytical techniques, such as chromatography and spectroscopy, are crucial in qualitative and quantitative analysis of nanocarrier-based formulations. Molecular identification and drug content determination require chromatographic techniques, particularly HPLC. Spectroscopic techniques such as LC-MS, NMR, GC-MS, CE-MS, Raman, and IR are used to analyze the interaction and molecular structure of the sample. Nanocarriers have several benefits but face various challenges like stability, drug loading, regulatory standards, and biocompatibility. Future surface engineering and nanocarrier design advancements could improve targeted drug delivery and sustained diagnostic applications, significantly impacting healthcare.

Emulsion Structural Remodeling in Milk and Its Gelling Products: A Review.

The fat covered by fat globule membrane is scattered in a water phase rich in lactose and milky protein, forming the original emulsion structure of milk. In order to develop low-fat milk products with good performance or dairy products with nutritional reinforcement, the original emulsion structure of milk can be restructured. According to the type of lipid and emulsion structure in milk, the remolded emulsion structure can be divided into three types: restructured single emulsion structure, mixed emulsion structure, and double emulsion structure. The restructured single emulsion structure refers to the introduction of another kind of lipid to skim milk, and the mixed emulsion structure refers to adding another type of oil or oil-in-water (O/W) emulsion to milk containing certain levels of milk fat, whose final emulsion structure is still O/W emulsion. In contrast, the double emulsion structure of milk is a more complicated structural remodeling method, which is usually performed by introducing W/O emulsion into skim milk (W2) to obtain milk containing (water-in-oil-in-water) W1/O/W2 emulsion structure in order to encapsulate more diverse nutrients. Causal statistical analysis was used in this review, based on previous studies on remodeling the emulsion structures in milk and its gelling products. In addition, some common processing technologies (including heat treatment, high-pressure treatment, homogenization, ultrasonic treatment, micro-fluidization, freezing and membrane emulsification) may also have a certain impact on the microstructure and properties of milk and its gelling products with four different emulsion structures. These processing technologies can change the size of the dispersed phase of milk, the composition and structure of the interfacial layer, and the composition and morphology of the aqueous phase substance, so as to regulate the shelf-life, stability, and sensory properties of the final milk products. This research on the restructuring of the emulsion structure of milk is not only a cutting-edge topic in the field of food science, but also a powerful driving force in promoting the transformation and upgrading of the dairy industry to achieve high-quality and multi-functional dairy products, in order to meet the diversified needs of consumers for health and taste.

Enhancing physico-chemical water quality in recycled dairy effluent through microbial consortium treatment

The dairy industry, notorious by generating wastewater rich in organic and nitrogenous content, necessitates sustainable recycling solutions. Biological treatment emerges as a cost-effective and chemical-free alternative. This study delves into the potential of microbial consortium, a microbial consortium, for recycling dairy effluent, aiming at water reclamation and environmental sustainability. Effluent samples from Madurai's Dairy Industry underwent microbial consortium treatment in a recycling prototype, with treatment efficacy assessed through physicochemical parameters and contaminant removal efficiency.Guided by a biodegradability index of 4.51, the study showcased EM's impact, revealing a notable decrease in pH levels, fostering an alkaline environment (2.35 ± 0.06 ppt). Dissolved oxygen increased significantly to 4.50 ppm, indicating improved aerobic conditions. EM treatment led to substantial reductions in calcium (53 %), magnesium (95 %), nitrogen (22 %), sulfate (79 %), phosphate (86 %), BOD (78 %), and COD (82 %). In contrast, dairy effluent treated without microbial consortium during the sludge activation process exhibited negligible water quality improvement.These findings underscore microbial consortium efficacy in advancing biological treatment of dairy effluent, demonstrating a significant reduction in contaminants and showcasing its potential for sustainable water reclamation. Improved alkalinity, dissolved oxygen, and nutrient content further signify positive impacts on ecosystem health. Microbial consortium emerges as a promising avenue for recycling dairy effluent, offering an economically viable and environmentally friendly solution. The study emphasizes the crucial role of microbial treatments in achieving efficient water reclamation, contributing to a cleaner and sustainable environment. Future research and broader implementation of microbial consortium in dairy industry wastewater management are recommended for enhanced environmental benefits.

Cutting-Edge Applications of Cellulose-Based Membranes in Drug and Organic Contaminant Removal: Recent Advances and Innovations

The increasing environmental challenges caused by pharmaceutical waste, especially antibiotics and contaminants, necessitate sustainable solutions. Cellulose-based membranes are considered advanced tools and show great potential as effective materials for the removal of drugs and organic contaminants. This review introduces an environmentally friendly composite membrane for the elimination of antibiotics and dye contaminants from water and food, without the use of toxic additives. The potential of cellulose-based membranes in reducing the impact on water quality and promoting environmental sustainability is emphasized. Additionally, the benefits of using biobased cellulose membranes in membrane biological reactors for the removal of antibiotics from pharmaceutical waste and milk are explored, presenting an innovative approach to achieving a circular economy. This review provides recent and comprehensive insights into membrane bioreactor technology, making it a valuable resource for researchers seeking efficient methods to break down antibiotics in industrial wastewater, particularly in the pharmaceutical and dairy industries.

Current state and prospects of dihydroquercetin application in food industry

This review is a detailed analysis of the results of the experimental studies carried out by scientists from different countries and devoted to the practical application of dihydroquercetin in food products. The main attention is paid to the antioxidant and functional properties of the bioflavonoid in the composition of milk and dairy products. Currently, dihydroquercetin is considered the most powerful natural antioxidant, which use prolongs the shelf-life of products and has a beneficial effect on the human body. Although much time has passed since dihydroquercetin discovery by Russian scientists, today a significant part of research of the bioflavonoid is focused on the pharmaceutical sphere, while its use in the food industry is at the initial stage of the development. In the article, studies carried out by Russian, Chinese, Japanese, European, American and other foreign authors are systemized, which allowed describing quite thoroughly trends in the use of dihydroquercetin in the global industry. Having focused on the influence of the antioxidant on the characteristics of dairy products, scientists found that it increases the stability of acidity and pH, inhibits the development of the pathogenic microflora, favorably affects taste and aroma. Prospect directions for its use have been revealed, and directions for further research of the use of this antioxidant in the dairy industry have been identified. It is concluded that the addition of dihydroquercetin into a recipe will allow obtaining new functional (parapharmaceutical) food products with the antioxidant stability to prevent socially significant diseases and improve the health of the population.

Molecular Identification of Genes Resistant to Antibiotics in Staphylococcus aureus Isolated from Sub-clinical Animals Affected by Mastitis

Background and Aim: Mastitis is a prevalent and costly disease that significantly impacts the dairy industry, affecting both sheep's health and milk quality. In order to determine the bacterial causes of subclinical mastitis in Iraq and its susceptibility to antibiotics, the study aims to molecularly detect resistant Staphylococcus aureus isolated from sheep infected with mastitis. Methods: Molecular assay and phylogenetic study to detect Staph aureus isolates by using 16 Sr RNA and resistance virulence genes (aac-aph, tetk) by the conventional method of PCR. Two hundred samples were collected from the subclinical mastitis of infected ewes, Samples were reserved on an icebox and transported to the laboratories. Milk samples were cultured on Blood Agar and Mannitol Salt Agar (7.5%) plates. The culture plates were then incubated at 37°C for 24 hours. Results: The PCR assay revealed amplification of the 16S rRNA gene in all 20 isolates, amplification of the aac-aph gene in 19 isolates, and the tetK gene in all 20 isolates. The PCR products of 20 positive samples for (16 Sr RNA) target genes were sequenced, evaluated, and dropped on the Genbank-NCBI under the accession number, which became a reference to Iraq and the world. According to the current study, Staphylococcus aureus is commonly found in milk. To combat drug resistance, antimicrobial drug use should be normalized, and antimicrobial resistance surveillance should be conducted regularly. Conclusion: PCR-product of 8 positive samples for 16sr RNA target gene were sequenced, analyzed, and deposited on the Genbank-NCBI under the accession number and became a reference to Iraq and the Middle East and the world.

Molecular Identification of Genes Resistant to Antibiotics in Staphylococcus aureus Isolated from Sub-clinical Animals Affected by Mastitis

Background and Aim: Mastitis is a prevalent and costly disease that significantly impacts the dairy industry, affecting both sheep's health and milk quality. In order to determine the bacterial causes of subclinical mastitis in Iraq and its susceptibility to antibiotics, the study aims to molecularly detect resistant Staphylococcus aureus isolated from sheep infected with mastitis. Methods: Molecular assay and phylogenetic study to detect Staph aureus isolates by using 16 Sr RNA and resistance virulence genes (aac-aph, tetk) by the conventional method of PCR. Two hundred samples were collected from the subclinical mastitis of infected ewes, Samples were reserved on an icebox and transported to the laboratories. Milk samples were cultured on Blood Agar and Mannitol Salt Agar (7.5%) plates. The culture plates were then incubated at 37°C for 24 hours. Results: The PCR assay revealed amplification of the 16S rRNA gene in all 20 isolates, amplification of the aac-aph gene in 19 isolates, and the tetK gene in all 20 isolates. The PCR products of 20 positive samples for (16 Sr RNA) target genes were sequenced, evaluated, and dropped on the Genbank-NCBI under the accession number, which became a reference to Iraq and the world. According to the current study, Staphylococcus aureus is commonly found in milk. To combat drug resistance, antimicrobial drug use should be normalized, and antimicrobial resistance surveillance should be conducted regularly. Conclusion: PCR-product of 8 positive samples for 16sr RNA target gene were sequenced, analyzed, and deposited on the Genbank-NCBI under the accession number and became a reference to Iraq and the Middle East and the world.

Temperature correction of near-infrared spectra of raw milk

Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.

Navigating integration challenges: Insights from migrant dairy farm workers in New Zealand

As the global demand for milk products continues to increase and dairy sectors become more concentrated, a pressing question arises: who will milk the cows and where will they come from? Mirroring global trends, the New Zealand dairy industry has experienced significant structural changes, with a shift from family-run dairy farms to larger commercially orientated operations. At the same time, domestic socio-economic and demographic changes have resulted in rural to urban migration. These factors have contributed to a shortage of skilled labour in some regions. To meet its labour demands, the New Zealand dairy sector has relied increasingly heavily on temporary migrant workers, who face significant challenges integrating into their new communities. This study uses data from in-depth interviews with 25 migrant dairy farm workers in Canterbury, New Zealand, and a modified conceptual framework for integration developed by Ager and Strang to enhance our understanding of how skilled migrants in the dairy sector adapt to unfamiliar host communities. Through thematic analysis we identify the factors that either facilitate or hinder the successful integration of this important but vulnerable group. Key themes include motivations for moving to New Zealand, pathways to obtaining their positions, and the primary challenges that they experience in attempting to integrate. Challenges include physical and social isolation, discrimination, and a lack of access to key services. As the agricultural sector continues to grow, it is essential to provide migrant farm workers with adequate support to ensure their successful social, cultural, and economic integration.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

Colostrum-derived extracellular vesicles: potential multifunctional nanomedicine for alleviating mastitis

Bovine mastitis is an infectious disease that causes substantial economic losses to the dairy industry worldwide. Current antibiotic therapy faces issues of antibiotic misuse and antimicrobial resistance, which has aroused concerns for both veterinary and human medicine. Thus, this study explored the potential of Colo EVs (bovine colostrum-derived extracellular vesicles) to address mastitis. Using LPS-induced murine mammary epithelial cells (HC11), mouse monocyte macrophages (RAW 264.7), and a murine mastitis model with BALB/C mice, we evaluated the safety and efficacy of Colo EVs, in vivo and in vitro. Colo EVs had favorable biosafety profiles, promoting cell proliferation and migration without inducing pathological changes after injection into murine mammary glands. In LPS-induced murine mastitis, Colo EVs significantly reduced inflammation, improved inflammatory scores, and preserved tight junction proteins while protecting milk production. Additionally, in vitro experiments demonstrated that Colo EVs downregulated inflammatory cytokine expression, reduced inflammatory markers, and attenuated NF-κB pathway activation. In summary, we inferred that Colo EVs have promise as a therapeutic approach for mastitis treatment, owing to their anti-inflammatory properties, potentially mediated through the NF-κB signaling pathway modulation.Graphical

Unveiling the Potential Bioactive Peptides Derived From Goat Casein Hydrolysates Based on In Silico Analyses

ABSTRACTBioactive peptides (BAPs) have attracted considerable interest in scientific research due to their heterogeneity in sequence and structure, which underpins various biological functionalities. In this context, goat casein, an abundant by‐product of the dairy industry, emerges as a valuable source of BAPs. The present study undertook a meticulous evaluation of the bioactive potentials of goat casein‐derived peptides through an integrated approach combining computational simulations, high‐throughput screening, and molecular docking techniques. The initial phase involved the enzymatic digestion of goat milk casein using trypsin, followed by the identification of peptides via liquid chromatography–tandem mass spectrometry (LC–MS/MS), uncovering a total of 597 peptides. Subsequent prioritization using the PeptideRanker algorithm identified 70 peptides exhibiting potential bioactivity, as denoted by scores above 0.8. Advanced screening employing the BIOPEP database and the AutoDock and CAMPR4 tools facilitated the elucidation of 16 antioxidant, 59 hypotensive, 63 hypoglycemic, 70 hypolipidemic, and 25 antimicrobial peptides. Molecular docking studies further elucidated the spontaneous nature of the interactions between the peptides and their respective receptors, predominantly mediated by hydrogen bonding and hydrophobic interactions. Four peptides specifying all activities simultaneously were synthesized, and their activities were verified by in vitro experiments. These results not only highlight an effective strategy for the high‐throughput screening of goat casein‐derived peptides but also underscore the potential of utilizing casein as a viable source of functional food ingredients. This study thereby contributes significantly to the expanding field of functional food research, suggesting a sustainable approach to explore the potential of dairy by‐products.

Vertical back movement of cows during locomotion: detecting lameness with a simple image processing technique.

This research paper proposes a simple image processing technique for automatic lameness detection in dairy cows under farm conditions. Seventy-five cows were selected from a dairy farm and visually assessed for a reference/real lameness score (RLS) as they left the milking parlor, while simultaneously being video-captured. The method employed a designated walking path and video recordings processed through image analysis to derive a new computerized automatic lameness score (ALDS) based on calculated factors from back arch posture. The proposed automatic lameness detection system was calibrated using 12 cows, and the remaining 63 were used to evaluate the diagnostic characteristics of the ALDS. The agreement and correlation between ALDS and RLS were investigated. ALDS demonstrated high diagnostic accuracy with 100% sensitivity and specificity and was found to be 100% accurate with a perfect agreement (ρc = 1) and strong correlation (r = 1, P < 0.001) for lameness detection in binary scores (lame/non-lame). Moreover, the ALDS had a strong agreement (ρc = 0.885) and was highly correlated (r = 0.840; 0.796-1.000 95% confidence interval, P < 0.001) with RLS in ordinal scores (lameness severity; LS1 to LS5). Our findings suggest that the proposed method has the potential to compete with vision-based lameness detection methods in dairy cows in farm conditions.