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Dairy, Volume 5, Issue 2 (June 2024) – 5 articles

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13 pages, 533 KiB  
Article
Good Handling Practices Have Positive Impacts on Dairy Calf Welfare
by Lívia C. M. Silva-Antunes, Maria Camila Ceballos, João A. Negrão and Mateus J. R. Paranhos da Costa
Dairy 2024, 5(2), 295-307; https://doi.org/10.3390/dairy5020024 - 15 May 2024
Viewed by 240
Abstract
The objective was to evaluate the effects of good handling practices on dairy calf welfare. Forty-eight crossbred dairy calves were assigned to two treatments: conventional handling (CH): calves kept in individual pens, fed milk replacer in buckets without nipples and abruptly weaned; or [...] Read more.
The objective was to evaluate the effects of good handling practices on dairy calf welfare. Forty-eight crossbred dairy calves were assigned to two treatments: conventional handling (CH): calves kept in individual pens, fed milk replacer in buckets without nipples and abruptly weaned; or good handling practices (GHP): calves kept in group pens, fed milk replacer in buckets with nipples, given daily tactile stimulation during feeding, and progressive weaning. Calf welfare was assessed from birth to 120 days of age, based on: health (plasma concentrations of glucose and IgG, and occurrences of diarrhea, pneumonia, tick-borne disease, or death); physiology (heart rate [HR], respiratory rate [RR], and rectal temperature [RT]); behavior (flight distance [FD], latencies for first movement [LM] and to hold the calf in a pen corner [LH], and total time a calf allowed touching [TTT]); and performance indicators (body weight, average daily gain, and weaning success at 70 days of age). Calves in the GHP treatment had a lower HR at 30 days of age, shorter FD and LH, longer TTT, and lower RR and RT than CH (p < 0.05). However, health, deaths and performance indicators did not differ (p > 0.05) between treatments. Based on various indicators, GHP improved dairy calf welfare. Full article
(This article belongs to the Section Dairy Animal Health)
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8 pages, 809 KiB  
Communication
Chlorate and Trichloromethane Residues in Bulk Tank Milk Produced in the Republic of Ireland before and after Chlorine was Prohibited as a Cleaning Agent on Farms
by Lorna Twomey, Ambrose Furey, Bernadette O’Brien, Tom Beresford, Paula Reid, Martin Danaher and David Gleeson
Dairy 2024, 5(2), 287-294; https://doi.org/10.3390/dairy5020023 - 9 May 2024
Viewed by 260
Abstract
In an effort to reduce the occurrence of chlorine derived residues such as chlorate and trichloromethane (TCM) in milk and ultimately in dairy products, ‘chlorine-free’ cleaning of milking equipment became compulsory in the Republic of Ireland (ROI) from January 2021. While data exists [...] Read more.
In an effort to reduce the occurrence of chlorine derived residues such as chlorate and trichloromethane (TCM) in milk and ultimately in dairy products, ‘chlorine-free’ cleaning of milking equipment became compulsory in the Republic of Ireland (ROI) from January 2021. While data exists on TCM levels in bulk tank milk, little is known about the prominence and typical levels (mg/kg) of chlorate residue in bulk tank milk. To address this, 3625 bulk milk samples were collected from six milk processors and were analysed for chlorate and TCM residues across 2020 and 2021, with 2020 representing a period before chlorine-free cleaning was introduced and 2021 being the period after chlorine removal. In 2020, 15% of the samples analysed had detectable levels of chlorate (0.0020–1.6 mg/kg), but this reduced to 8% in 2021 (0.0020–3.9 mg/kg), following the introduction of ‘chlorine-free’ cleaning. Chlorate and TCM residues have not been totally eliminated because sources of residue other than cleaning chemicals exist, i.e., chlorinated water. Full article
(This article belongs to the Section Dairy Microbiota)
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16 pages, 1543 KiB  
Article
Nutritional Composition and Chemical Safety of Wagashi Gassirè Cheese Sold in the Southern Benin Markets
by Alphonse Wanignon Dossou, Baké Marie Thérèse Seko Orou, Gwladys Komagbe, Philippe Sessou, Abdou Karim Issaka Youssao, Souaïbou Farougou, Joseph Djidjoho Hounhouigan, Jacques Mahillon, Roch Mongbo, Marc Poncelet, Samiha Boutaleb, Sylvie Gobert, Yann Eméric Madode, Paulin Azokpota, Antoine Clinquart, Marie-Louise Scippo and Caroline Douny
Dairy 2024, 5(2), 271-286; https://doi.org/10.3390/dairy5020022 - 30 Apr 2024
Viewed by 369
Abstract
In this study, the nutritional composition and the chemical safety of Wagashi Gassirè (WG) cheese sold in southern Benin markets were assessed. For this purpose, 15 WG were analysed for fatty acids, essential minerals, and chemical hazards (dioxins, aflatoxin M1 (AFM1), biogenic amines, [...] Read more.
In this study, the nutritional composition and the chemical safety of Wagashi Gassirè (WG) cheese sold in southern Benin markets were assessed. For this purpose, 15 WG were analysed for fatty acids, essential minerals, and chemical hazards (dioxins, aflatoxin M1 (AFM1), biogenic amines, metals, antibiotic and pesticide residues). The risks related to arsenic, lead, aluminium, AFM1, histamine, and tyramine were calculated using the methods recommended by the European Food Safety Authority. Oleic, palmitic and stearic acids, calcium, and phosphorus were the main fatty acids and minerals detected. Lead (0.08 ± 0.06 mg/kg) and AFM1 (0.3 ± 0.0 µg/kg) were detected in all samples and exceeded the maximum level set by the international standard. Cadaverine and tyramine were the main biogenic amines found. No pesticide residues were detected using a multi-residue method targeting compounds. Residues of quinolones, tetracyclines, and colistin antibiotics were also detected. The calculated chronic exposure indicated no public health concern for the chemical contaminants targeted. Moreover, the average cancer risk related to AFM1 intake was 3 × 10−4 cases/105 persons/year for the Benin population through WG consumption. This study contributes to the nutritional characterization of WG and identifies lead and AFM1 as the most relevant chemical hazards of this product. Full article
(This article belongs to the Section Milk and Human Health)
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22 pages, 1716 KiB  
Article
Integration of Sensor Fusion to Enhance Quality Assessment of White Brine Cheeses
by Zlatin Zlatev, Tatjana Spahiu, Ira Taneva, Milen Dimov and Miroslav Vasilev
Dairy 2024, 5(2), 249-270; https://doi.org/10.3390/dairy5020021 - 30 Apr 2024
Viewed by 343
Abstract
The article examines the main characteristics of white brine cheeses from different manufacturers and changes in their quality indicators. These characteristics include the active acidity, electrical conductivity, total dissolved solids, oxidation–reduction potential, and organoleptic assessment. In this context, the connection to biomimetics lies [...] Read more.
The article examines the main characteristics of white brine cheeses from different manufacturers and changes in their quality indicators. These characteristics include the active acidity, electrical conductivity, total dissolved solids, oxidation–reduction potential, and organoleptic assessment. In this context, the connection to biomimetics lies in the approach of integrating multiple sensory modalities, similar to how biological systems often use multiple senses to perceive and understand their environment. For this purpose, spectral, ultrasonic, and gas characteristics were used, from which informative indices were extracted, united at a later stage in a vector of features. Based on the classification, it was found that the optical characteristics of cheeses from different manufacturers overlap, thus making it possible to predict the main indicators for each type of cheese. The results show that the use of a multimodal approach combining features from different sensors contributes to a better understanding of the variations in cheese properties, while improving the predictive abilities of the created models. The obtained results give a clear idea of the quality of the cheese, thus enabling adequate decisions to be made during the production process. Full article
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10 pages, 1202 KiB  
Article
First Lactation Milk Yield Predicted by the Heifer’s Growth Curve Derivatives
by Aurelio Guevara-Escobar, Mónica Cervantes-Jiménez, Vicente Lemus-Ramírez, José Guadalupe García-Muñiz and Adolfo Kunio Yabuta Osorio
Dairy 2024, 5(2), 239-248; https://doi.org/10.3390/dairy5020020 - 28 Apr 2024
Viewed by 367
Abstract
Replacement heifers are regularly weighed to assess their health. These data also predict the milk yield in their first lactation (L). The first derivative of the growth curve represents the weight change rate at a given time. It is interesting to use the [...] Read more.
Replacement heifers are regularly weighed to assess their health. These data also predict the milk yield in their first lactation (L). The first derivative of the growth curve represents the weight change rate at a given time. It is interesting to use the higher-order derivatives of one biological process, such as growth, to predict the outcome of another process, like lactation. With 78 records of grazing heifers, machine learning was used to predict the L based on variables calculated during the rearing period, from 3 to 21 months of age, every 3 months: body weight (P), first (1D), and second derivative (2D) of an individually modeled Fourier function. Other variables were the age at effective insemination (AI) and the season of the year when the heifer was born (E). The average deviance of the fitted models represented the goodness of fit. The models were trained using 85% of the records, and the fit was evaluated using the remaining data. The deviance was lower for the models including both derivatives in comparison to the models where the derivatives were not included (p = 0.022). The best models predicted the L using data of heifers at six months of age (r2 = 0.62) and the importance of the variables in the model was 35, 28, 21, and 16% for 1D, AI, 2D, and P, respectively. By utilizing this type of model, it would be possible to select and eliminate excess heifers early on, thereby reducing the financial and environmental costs. Full article
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