Hasil untuk "Dairy processing. Dairy products"

D. O'sullivan, L. Giblin, P. McSweeney et al.

The microbial profile of cheese is a primary determinant of cheese quality. Microorganisms can contribute to aroma and taste defects, form biogenic amines, cause gas and secondary fermentation defects, and can contribute to cheese pinking and mineral deposition issues. These defects may be as a result of seasonality and the variability in the composition of the milk supplied, variations in cheese processing parameters, as well as the nature and number of the non-starter microorganisms which come from the milk or other environmental sources. Such defects can be responsible for production and product recall costs and thus represent a significant economic burden for the dairy industry worldwide. Traditional non-molecular approaches are often considered biased and have inherently slow turnaround times. Molecular techniques can provide early and rapid detection of defects that result from the presence of specific spoilage microbes and, ultimately, assist in enhancing cheese quality and reducing costs. Here we review the DNA-based methods that are available to detect/quantify spoilage bacteria, and relevant metabolic pathways in cheeses and, in the process, highlight how these strategies can be employed to improve cheese quality and reduce the associated economic burden on cheese processors.

P. Pingali

L. Vuyst, E. Vandamme

S. F. Chin, W. Liu, J. Storkson et al.

G. Haenlein

Gurusamy Kutralam-Muniasamy, F. Pérez-Guevara, I. Elizalde-Martínez et al.

The widespread dispersal of microplastic (plastic particle <5 mm) contamination in human food chain is gaining more attention in the public arena and scientific community. Better assessment of diversified consumer products is a key for combating problems related to microplastic contamination. To the best of our knowledge, no study has been conducted on dairy milk products, and the current research status of microplastics is lacking. Here, a total of 23 milk samples (22 adult and 1 kid) from 5 international and 3 national brands of Mexico was collected and tested for the occurrence of microplastics. Results confirmed the ubiquity of microplastics in the analyzed samples and showed variability ranging between 3 ± 2-11 ± 3.54 particles L-1 with an overall average of 6.5 ± 2.3 particles L-1 which are lower than any reported levels in liquid food products. Microplastic particles exhibited variety of colors (blue, brown, red and pink), shapes (fibers and fragments) and sizes (0.1-5 mm). Among which, blue colored fibers (<0.5 mm) were predominant. Micro-Raman identification results revealed that thermoplastic sulfone polymers (polyethersulfone and polysulfone) were common types of microplastics in milk samples, which are highly used membrane materials in dairy processes. Thus, this study findings developed a baseline outlook for microplastics contamination in dairy products and posed a great deal to take necessary controls and preventive measures to avoid them.

Harsh Mathur, T. Beresford, P. Cotter

Consuming fermented foods has been reported to result in improvements in a range of health parameters. These positive effects can be exerted by a combination of the live microorganisms that the fermented foods contain, as well as the bioactive components released into the foods as by-products of the fermentation process. In many instances, and particularly in dairy fermented foods, the microorganisms involved in the fermentation process belong to the lactic acid group of bacteria (LAB). An alternative approach to making some of the health benefits that have been attributed to fermented foods available is through the production of ‘fermentates’. The term ‘fermentate’ generally relates to a powdered preparation, derived from a fermented product and which can contain the fermenting microorganisms, components of these microorganisms, culture supernatants, fermented substrates, and a range of metabolites and bioactive components with potential health benefits. Here, we provide a brief overview of a selection of in vitro and in vivo studies and patents exclusively reporting the health benefits of LAB ‘fermentates’. Typically, in such studies, the potential health benefits have been attributed to the bioactive metabolites present in the crude fermentates and/or culture supernatants rather than the direct effects of the LAB strain(s) involved.

K. Fenster, B. Freeburg, Chris Hollard et al.

To successfully deliver probiotic benefits to the consumer, several criteria must be met. Here, we discuss the often-forgotten challenges in manufacturing the strains and incorporating them in consumer products that provide the required dose at the end of shelf life. For manufacturing, an intricate production process is required that ensures both high yield and stability and must also be able to meet requirements such as the absence of specific allergens, which precludes some obvious culture media ingredients. Reproducibility is important to ensure constant high performance and quality. To ensure this, quality control throughout the whole production process, from raw materials to the final product, is essential, as is the documentation of this quality control. Consumer product formulation requires extensive skill and experience. Traditionally, probiotic lactic acid bacteria and bifidobacteria have been incorporated in fermented dairy products, with limited shelf life and refrigerated storage. Currently, probiotics may be incorporated in dietary supplements and other “dry” food matrices which are expected to have up to 24 months of stability at ambient temperature and humidity. With the right choice of production process, product formulation, and strains, high-quality probiotics can be successfully included in a wide variety of delivery formats to suit consumer requirements.

Sayed Pouria Talebi, Clive Cheong Took

Numerous attempts have been made to replicate the success of complex-valued algebra in engineering and science to other hypercomplex domains such as quaternions, tessarines, biquaternions, and octonions. Perhaps, none have matched the success of quaternions. The most useful feature of quaternions lies in their ability to model three-dimensional rotations which, in turn, have found various industrial applications such as in aeronautics and computergraphics. Recently, we have witnessed a renaissance of quaternions due to the rise of machine learning. To equip the reader to contribute to this emerging research area, this chapter lays down the foundation for: - augmented statistics for modelling quaternion-valued random processes, - widely linear models to exploit such advanced statistics, - quaternion calculus and algebra for algorithmic derivations, - mean square estimation for practical considerations. For ease of exposure, several examples are offered to facilitate the learning, understanding, and(hopefully) the adoption of this multidimensional domain.

Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby et al.

Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume.

Michael Negosanti, Lorenzo Pucci, Andrea Giorgetti

This work investigates the performance of an integrated sensing and communication (ISAC) system exploiting inverse virtual aperture (IVA) for imaging moving extended targets in vehicular scenarios. A base station (BS) operates as a monostatic sensor using MIMO-OFDM waveforms. Echoes reflected by the target are processed through motion-compensation techniques to form an IVA range-Doppler (cross-range) image. A case study considers a 5G NR waveform in the upper mid-band, with the target model defined in 3GPP Release 19, representing a vehicle as a set of spatially distributed scatterers. Performance is evaluated in terms of image contrast (IC) and the root mean squared error (RMSE) of the estimated target-centroid range. Finally, the trade-off between sensing accuracy and communication efficiency is examined by varying the subcarrier allocation for IVA imaging. The results provide insights for designing effective sensing strategies in next-generation radio networks.

Qi Zhu, R. Gooneratne, M. Hussain

Listeria monocytogenes, a member of the genus Listeria, is widely distributed in agricultural environments, such as soil, manure and water. This organism is a recognized foodborne pathogenic bacterium that causes many diseases, from mild gastroenteritis to severe blood and/or central nervous system infections, as well as abortion in pregnant women. Generally, processed ready-to-eat and cold-stored meat and dairy products are considered high-risk foods for L. monocytogenes infections that cause human illness (listeriosis). However, recently, several listeriosis outbreaks have been linked to fresh produce contamination around the world. Additionally, many studies have detected L. monocytogenes in fresh produce samples and even in some minimally processed vegetables. Thus L. monocytogenes may contaminate fresh produce if present in the growing environment (soil and water). Prevention of biofilm formation is an important control measure to reduce the prevalence and survival of L. monocytogenes in growing environments and on fresh produce. This article specifically focuses on fresh produce–associated listeriosis outbreaks, prevalence in growing environments, contamination levels of fresh produce, and associated fresh produce safety challenges.

P. dos S. Silva, Y. Butenko, G. Kra et al.

ABSTRACT: Activation of the endocannabinoid system (ECS) elicits negative effects on the reproductive system in mammals. Supplementation with n-3 fatty acid (FA) lowers ECS activation and has anti-inflammatory effects. Thus, we hypothesized that supplementing cows with n-3 FA will downregulate components of the ECS and immune system in preovulatory follicles and in the endometrium. Twenty-four multiparous Holstein dairy cows were supplemented from d 256 of pregnancy to d 70 postpartum as follows: (1) control (CTL; n = 12), prepartum with 250 g/d per cow calcium salts of FA and postpartum at 1.6% of the diet (DM basis); or (2) extruded flaxseed (FLX; n = 12) supplement rich in α-linolenic acid (C18:3n-3), prepartum with 700 g/d per cow and postpartum at 6.4% of diet (DM basis). Ovaries were monitored at 30 DIM, and following estrous cycle synchronization we aspirated the follicular fluids (FF) of follicles ≥7 mm, separated the granulosa cells (GRC), and performed endometrium biopsies at 58 ± 5 DIM. The FF were analyzed for concentrations of estradiol (E2) and progesterone (P4), and E2-active follicles were declared when E2/P4 was >1. The FA and endocannabinoid (eCB) profiles were determined in plasma and in the reproductive tissues. Proteomic analyses and mRNA abundances were determined in GRC and endometrium. Supplementation of n-3 FA increased the proportion of total n-3 FA and decreased the ratio of n-6 to n-3 ratio in plasma, FF and GRC compared with CTL. In plasma and FF, n-3 FA supplementation decreased the proportion of the n-6 FA eCB precursor arachidonic acid (ARA; C20:4n-6), and increased the abundance of the n-3 FA-derived eCB eicosapentaenoyl ethanolamide compared with CTL. In the endometrium, n-3 FA supplementation reduced the abundance of the n-6 FA-derived eCB 2-arachidonoylglycerol (2-AG) compared with CTL. Proteomic analysis of GRC showed that n-3 FA supplementation increased the abundance of FA-binding-protein-5, which is involved in intracellular transport of eCB, as well as the abundances of the cytokine receptor like factor-2 and glutathione-S-transferase-LANCL1, whereas it reduced the abundances of several complement proteins: complement factors I, D, H, complement components C7 chain and C8 β chain, and complement component 1 Q subcomponent-binding protein, mitochondrial (C1QBP). In addition, the abundance of superoxide dismutase (SOD3) was lower in FLX GRC than in CTL. In the endometrium, n-3 FA supplementation decreased the abundance of a few immune-related proteins. In the GRC, n-3 FA supplementation reduced the relative mRNA abundances of cannabinoid receptors 1 and 2 compared with CTL. Across treatments, a positive correlation was found between the relative abundance in FF of the eCB anandamide with C7, C1QBP, and SOD3 in GRC, whereas FF 2-AG had a negative correlation with them. Overall, in line with our premise, dietary n-3 FA supplementation attenuated the levels of some eCB and reduced the expression of several proteins and genes related to the ECS and immune system in the preovulatory follicle and in the endometrium, which may be part of the etiology of the positive effects of n-3 FA on the reproductive system in dairy cows.

David B. Sheedy, Helen M. Golder, Sergio C. Garcia et al.

ABSTRACT: Body condition score and BW yield insights into body tissue reserves and diet, and serum biochemical measures reflect the metabolic status of cows. Associations between body composition measures and biochemistry are unclear and investigation may reveal important information on the metabolic and physiological status of cattle with varying levels of labile tissue reserves. Cohorts of 739 nonlactating, late-pregnancy, dry cows (26.9 d prepartum, SD = 12.4) and 690 peak-milk cows (58.0 DIM, SD = 14.5) were selected by stratified (parity: 1, 2, 3, >3) random sampling from 30 farms (15 pasture, 15 TMR) in this cross-sectional study. A single serum, BCS (1–5 scale), BW, and milk-production datum was collected per cow, per cohort between November 2022 and July 2023. Eleven analytes were collected, analyzed, and standardized within group (cohort/breed per farm). Mixed linear models for BCS and BW were specified, with the random effect of group. A 6-point, unordered, categorical body-group classification that combined BCS (greater, equal to, or less than group median; as high, median, or low BCS) and BW (greater or less than group median; as high or low BW) was analyzed by polytomous logistic regression. Effect sizes are listed for a 1 SD increase in the specified analyte, keeping other covariables at their mean value. Dry BCS was positively associated with albumin (0.075 BCS ± 0.014 SE), urea (0.038 BCS ± 0.014 SE), and glucose (0.052 BCS ± 0.014 SE), and negatively with the interaction between cholesterol and days precalving. Dry BW positively associated with albumin (11.03 kg ± 2.48 SE) and negatively with cholesterol (−8.47 kg ± 2.57 SE). Peak-milk BCS was positively associated with albumin (0.47 BCS ± 0.015 SE), BHB (0.048 BCS ± 0.015 SE), and glucose (0.051 BCS ± 0.015 SE). Peak-milk BW was positively associated with albumin (6.94 kg ± 2.35 SE) and negatively with Ca (−7.02 kg ± 2.33 SE). Increasing BW and decreasing BCS was associated with increasing parity, except in dry second-parity cows that had low BCS. The dry polytomous model associated a 1 SD increase in albumin with a 4.89% ± 1.56 SE decreased risk of being low BCS/low BW and 5.87% ± 1.46 SE increased risk of high BCS/high BW. Risk change associated with 1 SD of glucose was −5.61% ± 1.58 SE for low BCS/high BW and 3.17% ± 1.58 SE for high BCS/high BW. For the peak-milk cohort, change in risk was associated with albumin for low BCS/low BW −3.67% ± 1.56 SE, low BCS/high BW −3.22% ± 1.53 SE. Risk change with 1 SD of BHB was −3.36% ± 1.47 SE for median BCS/low BW, 2.86% ± 1.44 SE for high BCS/low BW, and 2.69% ± 1.37 SE for high BCS/high BW. Risk of low BCS/low BW was greatest in second-parity cows, and high BCS/high BW was greatest in dry cows with greater than third parity and third-parity cows in peak milk. There were no interactions between parity and analytes. Albumin was consistently associated with BCS and BW, potentially reflecting innate differences in protein metabolism of cows.

C. Dezetter, F. Bidan, L. Delaby et al.

ABSTRACT: Body condition dynamics are known to affect the different steps of reproduction in cattle (cyclicity, estrus expression, fertilization, embryo development). This has led to a widespread idea that there is an ideal-target optimal body condition, but no clear profile has yet been identified. Here we investigated the relationships between BCS profiles and reproductive performance in dairy cows. Data were from Holstein or Normande herds in 6 French experimental farms. In the Holstein breed, we discriminated 4 BCS profiles based on combining BCS at calving (Low indicates BCS ∼2.6 points at calving; High indicates BCS ∼3.3 points at calving) with BCS loss after calving (Moderate [M] indicates BCS loss of ≤1.0 points at calving; Severe [S] indicates BCS loss of >1.0 points at calving). The Low-S profile mostly included multiparous cows with higher milk yield and lower reproductive performance than cows in the 3 other profiles. Low-S cows that experienced abnormal ovarian activity had lower reproductive performance than their profile-mates. Moreover, 67% of Low-S cows kept the same profile at the following lactation. The High-M profile mostly included primiparous cows with lower milk yield and higher reproductive performance than cows in other profiles. In High-M cows, higher milk yields correlated with higher risk of failure to calf on first insemination. Moreover, 38% of High-M cows kept the same profile at the following lactation, and none changed to Low-S. The other 2 BCS profiles (Low-M and High-S) were intermediate in terms of milk yield and reproductive performance. In Normande, we discriminated 3 BCS profiles based on combining BCS at calving (Low: ∼2.6 points; High: ∼3.5 points) with BCS loss after calving (Flat [F]: flat with no loss; M: ∼0.5 points; or S: ∼1.0 point). The Low-M and High-S profiles included cows with similar performance, even though High-S-profile cows showed better but not significantly different milk yield and reproduction performance. The High-F profile included cows that were more likely to experience abnormal ovarian activity and fail at first insemination than cows in other profiles. More than 50% of Normande cows with 2 successive lactations kept in the same BCS profile at the next lactation. Even though a low BCS at calving combined with severe BCS loss (more than 1 point) after calving was found to increase reproductive failure, there was no evidence of an optimal BCS profile for reproduction in dairy cows, and reproductive success or failure is multifactorial.

Philip Wiese, Victor Kartsch, Marco Guermandi et al.

The widespread adoption of Internet of Things (IoT) technologies has significantly advanced environmental monitoring (EM) by enabling cost-effective and scalable sensing solutions. Concurrently, machine learning (ML) and artificial intelligence (AI) are introducing powerful tools for the efficient and accurate analysis of complex environmental data. However, current IoT platforms for environmental sensing are typically limited to a narrow set of sensors, preventing a comprehensive assessment of environmental conditions and lacking sufficient computational capabilities to support the deployment of advanced ML and AI algorithms on the edge. To overcome these limitations, we introduce a compact (17x38 mm2), multi-modal, MCU-based environmental IoT node integrating 11 sensors, including CO2 concentration, volatile organic compounds (VOCs), light intensity, UV radiation, pressure, temperature, humidity, visual sensing via an RGB camera, and precise geolocation through a GNSS module. It features GAP9, a parallel ultra-low-power system-on-chip, enabling real-time, energy-efficient edge processing of advanced ML models directly on-device. We implemented a YOLOv5-based occupancy detection pipeline (0.3 M parameters, 42 MOP per inference), demonstrating 42% energy savings over raw data streaming. Additionally, we present a smart indoor air quality (IAQ) monitoring setup that combines occupancy detection with adaptive sample rates, achieving operational times of up to 143 h on a single compact 600 mAh, 3.7 V battery. Our platform lays the groundwork for innovative applications such as predictive indoor IAQ, enabling efficient AI-driven on-edge forecasting for energy-efficient and autonomous, proactive pollution-mitigation control strategies

C. Hogan, T. Lawton, M. Beecher

ABSTRACT: Herd size expansion, combined with the reduced availability of people to work on farms, has led to an increased focus on techniques that can improve dairy farm social sustainability. Effective work organization is one such entity, which could influence farm social sustainability, and focuses on having a productive, flexible, and standardized farm workload. The objective of this study was to examine the factors that contribute to better workplaces for the farmer using a survey of representative pasture-based dairy farms in Ireland. Potential contributing factors to better workplaces for farmers were identified, namely farm and farmer characteristics, working day structure, farmer attitudes, farm facilities, labor-efficient practices, and human resource management practices. A survey was completed by 313 Irish dairy farmers between November 20 and January 3, 2019, to capture relevant information. One proxy indicator was selected to represent productivity, flexibility, and standardization within the workplace, and each of the 313 farms were categorized into quartiles based on their ranking for these 3 indicators (1 = most effective quartile to 4 = least effective quartile). The average farmer that completed the survey was 51 yr old, milked 125 cows, reported to work 69.6 h/wk, took 10.3 d of holidays per year, and had a finish time of 19:52 (h:min) in the spring. The quartile of farms with the most effective farmer workplace reported reduced hours worked per week (58.6 vs. 82.6 h/wk), more holiday days (16.6 vs. 5.1 d) and weekends off (8.3 vs. 2.4) per year, and earlier finish times (18:41 vs. 21:14 [h:min] in the spring) compared with the least effective quartile. Similarly, the most effective farms reported better facilities and greater implementation of labor-efficient and human resource management practices compared with the least effective farms. The most effective quartile for farmer workplace effectiveness was more positive about the industry's potential to offer an effective work-life balance, would be more likely to encourage young people to pursue careers in dairy, and had more positive attitudes toward attracting and retaining workers compared with the least effective quartile. This study highlighted the range of factors contributing to more effective workplaces for farmers, indicating scope for improvement on many farms and challenges across all farms when compared with other industries in the case of some indicators (e.g., time off). The results can support the continued extension of concepts regarding work organization to assist farms in alleviating social sustainability challenges, highlighting the differentiating factors between the most and least effective farmer workplaces.

N. Taechachokevivat, B. Kou, T. Zhang et al.

ABSTRACT: The growing use of automated systems in the dairy industry generates a vast amount of cow-level data daily, creating opportunities for using these data to support real-time decision-making. Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. Long short-term memory (LSTM) models are machine learning models capable of learning temporal patterns and making predictions based on time series data. The objective of our study was to evaluate the ability of LSTM models to identify a health alert associated with a ketosis diagnosis (HAK) using deviations of daily milk yield, milk fat-to-protein ratio (FPR), number of successful milkings, rumination time, and activity index from the herd median by parity and DIM, considering various time series lengths and numbers of days before HAK. Additionally, we aimed to use Explainable Artificial Intelligence method to understand the relationships between input variables and model outputs. Data on daily milk yield, milk FPR, number of successful milkings, rumination time, activity, and health events during 0 to 21 DIM were retrospectively obtained from a commercial Holstein dairy farm in northern Indiana from February 2020 to January 2023. A total of 1,743 cows were included in the analysis (non-HAK = 1,550; HAK = 193). Variables were transformed based on deviations from the herd median by parity and DIM. Six LSTM models were developed to identify HAK 1, 2, and 3 d before farm diagnosis using historic cow-level data with varying time series lengths. Model performance was assessed using repeated stratified 10-fold cross-validation for 20 repeats. The Shapley additive explanations framework (SHAP) was used for model explanation. Model accuracy was 83%, 74%, and 70%; balanced error rate was 17% to 18%, 26% to 28%, and 34%; sensitivity was 81% to 83%, 71% to 74%, and 62%; specificity was 83%, 74%, and 71%; positive predictive value was 38%, 25% to 27%, and 21%; negative predictive value was 97% to 98%, 95% to 96%, and 94%; and area under the curve was 0.89 to 0.90, 0.80 to 0.81, and 0.72 for models identifying HAK 1, 2, and 3 d before diagnosis, respectively. Performance declined as the time interval between identification and farm diagnosis increased, and extending the time series length did not improve model performance. Model explanation revealed that cows with lower milk yield, number of successful milkings, rumination time, and activity, and higher milk FPR compared with herdmates of the same parity and DIM were more likely to be classified as HAK. Our results demonstrate the potential of LSTM models in identifying HAK using deviations of daily milk production variables, rumination time, and activity index from the herd median by parity and DIM. Future studies are needed to evaluate the performance of health alerts using LSTM models controlling for herd-specific metrics against commercial built-in algorithms in multiple farms and for other disorders.