1,721,051 research outputs found
Genetic parameters of different ftir-enabled phenotyping tools derived from milk fatty acid profile for reducing enteric methane emissions in dairy cattle
Full text linkThis study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4 /DM: 0.232–0.317) and methane intensity per kg of corrected milk (CH4 /CM: 0.177–0.279), smaller per kg cheese solids (CH4 /SO: 0.093–0.165), but greater per kg fresh cheese (CH4 /CU: 0.203–0.267) and for methane production (dCH4: 0.195–0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4 /DM (0.45–0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4 /CM, CH4 /CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint
Modeling weight loss of cheese during ripening and the influence of dairy system, parity, stage of lactation, and composition of processed milk
Full text linkThe yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows:%CYt=%CYf+(%CYi−%CYf)×e−kjavax.xml.bind.JAXBElement@4c0b99d6×t, where %CYt is the %CY at ripening time t; %CYi and %CYf are the modeled %CY traits at time 0 d (%CYi = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CYf = final %CY after 60 d ripening in the case of small model cheeses); kCY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although kCY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier-transform infrared spectra
No full textMitigating the dairy chain’s contribution to climate change requires cheap, rapid methods of predicting enteric CH4 emissions (EME) of dairy cows in the field. Such methods may also be useful for genetically improving cows to reduce EME. Our objective was to evaluate different procedures for predicting EME traits from infrared spectra of milk samples taken at routine milk recording of cows. As a reference method, we used EME traits estimated from published equations developed from a meta-analysis of data from respiration chambers through analysis of various fatty acids in milk fat by gas chromatography (FAGC). We analyzed individual milk samples of 1,150 Brown Swiss cows from 85 farms operating different dairy systems (from very traditional to modern), and obtained the cheese yields of individual model cheeses from these samples. We also obtained Fourier-transform infrared absorbance spectra on 1,060 wavelengths (5,000 to 930 waves/cm) from the same samples. Five reference enteric CH4 traits were calculated: CH4 yield (CH4/DMI, g/kg) per unit of dry matter intake (DMI), and CH4 intensity (CH4/CM, g/ kg) per unit of corrected milk (CM) from the FAGC profiles; CH4 intensity per unit of fresh cheese (CH4/ CYCURD, g/kg) and cheese solids (CH4/CYSOLIDS, g/kg) from individual cheese yields (CY); and daily CH4 production (dCH4, g/d). Direct infrared (IR) calibrations were obtained by BayesB modeling; the determination coefficients of cross-validation varied from 0.36 for dCH4 to 0.57 for CH4/CM, and were similar to the coefficient of determination values of the equations based on FAGC used as the reference method (0.47 for CH4/ DMI and 0.54 for CH4/CM). The models allowed us to select the most informative wavelengths for each EME trait and to infer the milk chemical features underlying the predictions. Aside from the 5 direct infrared prediction calibrations, we tested another 8 indirect prediction models. Using IR-predicted informative fatty acids (FAIR) instead of FAGC, we were able to obtain indirect predictions with about the same precision (correlation with reference values) as direct IR predictions of CH4/DMI (0.78 vs. 0.76, respectively) and CH4/CM (0.82 vs. 0.83). The indirect EME predictions based on IR-predicted CY were less precise than the direct IR predictions of both CH4/CYCURD (0.67 vs. 0.81) and CH4/CYSOLIDS (0.62 vs. 0.78). Four indirect dCH4 predictions were obtained by multiplying the measured or IR-predicted daily CM production by the direct or indirect CH4/CM. Combining recorded daily CM and predicted CH4/CM greatly increased precision over direct dCH4 predictions (0.96–0.96 vs. 0.68). The estimates obtained from the majority of direct and indirect IR-based prediction models exhibited herd and individual cow variability and effects of the main sources of variation (dairy system, parity, days in milk) similar to the reference data. Some rapid, cheap, direct and indirect IR prediction models appear to be useful for monitoring EME in the field and possibly for genetic/genomic selection, but future studies directly measuring CH4 with different breeds and dairy systems are needed to validate our findings
Short communication: Dietary protein restriction and conjugated linoleic acid supplementation in dairy cows affect milk composition, the cheese-making process, and cheese quality
Full text linkWe used 20 mid-lactating Holstein cows, housed in 4 pens according to a Latin square design, to evaluate the effects of dietary protein restriction (crude protein: 12.3 vs. 15.0% dry matter) and conjugated linoleic acid supplementation (CLA: 6.34 g/d of C18:2cis-9,trans-11 and 6.14 g/d of C18:2trans-10,cis-12) on milk composition, coagulation, curd firming and syneresis modeling, and cheese yield and quality (96 small cheeses). Dietary crude protein restriction, suggested as a way to reduce N excretion in farming, caused a reduction in milk protein content (−4%,), milk casein (−3.8%), lactose (−1%), cheese soluble protein (−16.8%), and the cheese maturation index (−15%), and a correlated increase in cheese fat content (+7.5%) and the fat to protein ratio (+18%). A modest reduction (−0.9%) in milk fat recovery in the curd did not affect cheese yield. The addition of CLA to the cows' diet, suggested as a way to improve N use efficiency and the nutritional value of dairy products, caused substantial alterations to the milk composition, cheese-making process, and cheese quality. The CLA reduced the fat (−12.3%), protein (−2%), casein (−2.2%), lactose (−1.0), and total solids (−4%) contents of milk, tended to delay coagulation, and weakened curd firming. The CLA reduced the fresh cheese yield (−7.5%) and cheese solids (−8.2%) because of the lower nutrient content of the milk, but also because of a lower recovery of milk protein in the curd (−0.9%) and lower total solids (−4.5%). It also reduced the fat content in the ripened cheese (−11.8%), as well as the fat to protein ratio (−19.4%) as a result of having increased the protein content (+9.3%). Last, it increased the lightness of the paste of the ripened cheeses (+3.3%), and especially the shear force (+16.3%). Dietary crude protein restriction, and CLA addition in particular, substantially altered the milk composition, cheese-making process, and cheese quality, and therefore needs to be carefully evaluated. Further studies are required to shed light on the causes of these modifications
Effect of somatic cell count in coagulation properties, cheese yield and nutrients recovery of individual milk of Brown Swiss cows
No full textComparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy
No full textCheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerà ̧d, Denmark) over the spectral range, from 5,000 to 900 wavenumberÃcm-1. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow
The 9-MilCA method as a rapid, partly automated protocol for simultaneously recording milk coagulation, curd firming, syneresis, cheese yield, and curd nutrients recovery or whey loss
No full textAbstractThe aim of this study was to propose and test a new laboratory cheesemaking procedure [9-mL milk cheesemaking assessment (9-MilCA)], which records 15 traits related to milk coagulation, curd firming, syneresis, cheese yield, and curd nutrients recovery or whey loss. This procedure involves instruments found in many laboratories (i.e., heaters and lacto-dynamographs), with an easy modification of the sample rack for the insertion of 10-mL glass tubes. Four trials were carried out to test the 9-MilCA procedure. The first trial compared 8 coagulation and curd firming traits obtained using regular or modified sample racks to process milk samples from 60 cows belonging to 5 breeds and 3 farms (480 tests). The obtained patterns exhibited significant but irrelevant between-procedure differences, with better repeatability seen for 9-MilCA. The second trial tested the reproducibility and repeatability of the 7 cheesemaking traits obtained using the 9-MilCA procedure on individual samples from 60 cows tested in duplicate in 2 instruments (232 tests). The method yielded very repeatable outcomes for all 7 tested cheese yield and nutrient recovery traits (repeatability >98%), with the exception of the fresh cheese yield (84%), which was affected by the lower repeatability (67%) of the water retained in the curd. In the third trial (96 tests), we found that using centrifugation in place of curd cooking and draining (as adopted in several published studies) reduced the efficiency of whey separation, overestimated all traits, and worsened the repeatability. The fourth trial compared 9-MilCA with a more complex model cheese-manufacturing process that mimics industry practices, using 1,500-mL milk samples (72 cows, 216 tests). The average results obtained from 9-MilCA were similar to those obtained from the model cheeses, with between-method correlations ranging from 78 to 99%, except for the water retained in the curd (r=54%). Our results indicate that new 9-MilCA method is a powerful research tool that allows the rapid, inexpensive, and partly automated analysis processing 40 samples per day with 2 replicates each, using 1 lacto-dynamograph, 2 heaters, and 3 modified sample racks, and yields a complete picture of the cheesemaking process (e.g., milk gelation, curd firming, syneresis, and whey expulsion) as well as the cheese yield and the efficiency of energy or nutrients retention in the cheese or loss in the whey
Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties
Full text linkDriven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCTeq), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV80). To assess model performance, coefficient of determination (R2VAL) and the root mean squared error of validation were recorded. The R2VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCTeq, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV80, the maximum R2VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCTeq (6%). Further investigation evidenced important variability among farms, with R2VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats
Genetic analysis of coagulation properties, curd firming modeling, milk yield, composition, and acidity in Sarda dairy sheep
Full text linkSheep milk is an important source of food, especially in Mediterranean countries, and is used in large part for cheese production. Milk technological traits are important for the sheep dairy industry, but research is lacking into the genetic variation of such traits. Therefore the aim of this study was to estimate the heritability of traditional milk coagulation properties and curd firmness modeled on time t (CFt) parameters, and their genetic relationships with test-day milk yield, composition (fat, protein, and casein content), and acidity in Sarda dairy sheep. Milk samples from 1,121 Sarda ewes from 23 flocks were analyzed for 5 traditional coagulation properties by lactodynamographic tests conducted for up to 60 min: rennet coagulation time (min), curd-firming time (k20, min), and 3 measures of curd firmness (a30, a45, and a60, mm). The 240 curd firmness observations (1 every 15 s) from each milk sample were recorded, and 4 parameters for each individual sample equation were estimated: rennet coagulation time estimated from the equation (RCTeq), the asymptotic potential curd firmness (CFP), the curd firming instant rate constant (kCF), and the syneresis instant rate constant (kSR). Two other derived traits were also calculated (CFmax, the maximum curd firmness value; and tmax, the attainment time). Multivariate analyses using Bayesian methodology were performed to estimate the genetic relationships of milk coagulation properties and CFt with the other traits; statistical inference was based on the marginal posterior distributions of the parameters of concern. The marginal posterior distribution of heritability estimates of milk yield (0.16 ± 0.07) and composition (0.21 ± 0.11 to 0.28 ± 0.10) of Sarda ewes was similar to those often obtained for bovine species. The heritability of rennet coagulation time as a single point trait was also similar to that frequently obtained for cow milk (0.19 ± 0.09), whereas the same trait calculated as an individual equation parameter exhibited larger genetic variation and a higher heritability estimate (0.32 ± 0.11). The other curd firming and syneresis traits, whether as traditional single point observations or as individual equation parameters and derived traits, were characterized by heritability estimates lower than for coagulation time and for the corresponding bovine milk traits (0.06 to 0.14). Phenotypic and additive genetic correlations among the 11 technological traits contribute to describing the interdependencies and meanings of different traits. The additive genetic relationships of these technological traits with the single test-day milk yield and composition were variable and showed milk yield to have unfavorable effects on all measures of curd firmness (a30, a45, a60, CFP, and CFmax) and tmax, but favorable effects on both instant rate constants (kCF and kSR). Milk fat content had a positive effect on curd firmness traits, especially on those obtained from CFt equations, whereas the negative effects on both coagulation time traits were attributed to the milk protein and casein contents. Finally, in view of the estimated heritabilities and additive genetic correlations, enhancement of technological traits of sheep milk through selective breeding could be feasible in this population
Effects of dairy system, herd within dairy system, and individual cow characteristics on the volatile organic compound profile of ripened model cheeses
No full textThe objective of this work was to study the effect of dairy system, herd within dairy system, and characteristics of individual cows (parity, days in milk and daily milk yield) on the volatile organic compound profile of model cheeses produced under controlled conditions from the milk of individual cows of the Brown Swiss breed. One hundred and fifty model cheeses were selected from a total of 1,272 produced for a wider study of the phenotypic and genetic variability of Brown Swiss cows. In our study, we selected 30 herds representing 5 different dairy systems. The cows sampled presented different milk yields (12.3-43.2 kg×d-1), stages of lactation (10-412 days in milk) and parity (1-7). A total of 55 volatile compounds were detected by solid phase microextraction gas chromatography-mass spectrometry, including 14 alcohols, 13 esters, 11 free fatty acids, 8 ketones, 4 aldehydes, 3 lactones, 1 terpene and 1 pyrazine. The most important sources of variation in the volatile organic profiles of model cheeses were dairy system (18 compounds) and days in milk (10 compounds), followed by parity (3 compounds) and milk yield (5 compounds). The model cheeses produced from the milk of tied cows reared on traditional farms had lower quantities of 3-methyl-1-butanol, 6-pentyloxan-2-one, 2-phenylethanol, and dihydrofuran-2(3H)-one than those reared in free stalls on modern farms. Of these, milk from farms using total mixed ration had higher a content of alcohols (hexan-1-ol, octan-1-ol) and esters (ethyl butanoate, ethyl pentanoate, ethyl hexanoate and ethyl octanoate) and a lower content of acetic acid than those using separate feeds. Moreover, the dairy systems that added silage to the total mixed ration produced cheeses with lower levels of volatile organic compounds, in particular alcohols (butan-1-ol, pentan-1-ol, heptan-1-ol), than those that did not. The amounts of butan-2-ol, butanoic acid, ethyl-2-methylpropanoate, ethyl-3-methylbutanoate, and 6-propyloxan-2-one increased linearly during lactation, while octan-1-ol, 3-methyl-3-buten-1-ol, 2-butoxyethanol, 6-pentyloxan-2-one, and 2,6-dimethylpyrazine showed a more complex pattern during lactation. The effect of the number of lactations (parity) was significant for octan-1-ol, butanoic acid and heptanoic acid. Lastly, octan-1-ol, 2-phenylethanol, pentanoic acid, and heptanoic acid increased with increasing daily milk yield, whereas dihydrofuran-2(3H)-one decreased. In conclusion, the volatile organic compound profile of model cheeses from the milk of individual cows was affected by dairy farming system and stage of lactation, and to smaller extent by parity and daily milk yiel
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