This study was part of a larger project that aimed to understand the causes for increasing variation in cheese ripening in a cheese-producing region in northern Sweden. The influence of different ...on-farm factors on raw milk composition and properties was investigated and is described in this paper, whereas the monthly variation in the milk quality traits during 1 yr is described in our companion paper. The dairy farming systems on a total of 42 dairy farms were characterized through a questionnaire and farm visits. Milk from farm tanks was sampled monthly over 1 yr and analyzed for quality attributes important for cheese making. On applying principal component analyses to evaluate the variation in on-farm factors, different types of farms were distinguished. Farms with loose housing and automatic milking system (AMS) or milking parlor had a higher number of lactating cows, and predominantly Swedish Holstein (SH) breed. Farms associated with tiestalls had a lower number of lactating cows and breeds other than SH. Applying principal component analyses to study the variation in composition and properties of tank milk samples from farms revealed a tendency for the formation of 2 clusters: milk from farms with AMS or a milking parlor, and milk from farms with tiestall milking. The interaction between the milking system, housing system, and breed probably contributed to this grouping. Other factors that were used in the characterization of the farming systems only showed a minor influence on raw milk quality. Despite the interaction, milk from tiestall farms with various cow breeds had higher concentrations (g/100 g of milk) of fat (4.74) and protein (3.63), and lower lactose concentrations (4.67) than milk from farms with predominantly SH cows and AMS (4.32, 3.47, and 4.74 g/100 g of milk, respectively) or a milking parlor (4.47, 3.54, and 4.79 g/100 g of milk, respectively). Higher somatic cell count (195 × 103/mL) and lower free fatty acid concentration (0.75 mmol/100 g of fat) were observed in milk from farms with AMS than in milk from tiestall systems (150 × 103/mL and 0.83 mmol/100 g of fat, respectively). Type of farm influenced milk gel strength, with milk from farms with predominantly SH cows showing the lowest gel strength (65.0 Pa), but not a longer rennet coagulation time. Effects of dairy farming system (e.g., dominant breed, milking system, housing, and herd size) on milk quality attributes indicate a need for further studies to evaluate the in-depth effects of farm-related factors on milk quality attributes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered ...by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm−1 was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to investigate the relationships between somatic cell count (SCC) in milk and several milk technological traits at the individual cow level. In particular, we determined the ...effects of very low to very high SCC on traits related to (1) milk yield and composition; (2) coagulation properties, including the traditional milk coagulation properties (MCP) and the new curd firming model parameters; and (3) cheese yield and recovery of milk nutrients in the curd (or loss in the whey). Milk samples from 1,271 Brown Swiss cows from 85 herds were used. Nine coagulation traits were measured: 3 traditional MCP rennet coagulation time (RCT, min), curd firming rate (k20, min), and curd firmness after 30 min (a30, mm) and 6 new curd firming and syneresis traits potential asymptotic curd firmness at infinite time (CFP, mm), curd firming instant rate constant (kCF, % × min−1), syneresis instant rate constant (kSR, % × min−1), rennet coagulation time estimated using the equation (RCTeq, min), maximum curd firmness achieved within 45 min (CFmax, mm), and time at achievement of CFmax (tmax, min). The observed cheese-making traits included 3 cheese yield traits (%CYCURD, %CYSOLIDS, and %CYWATER, which represented the weights of curd, total solids, and water, respectively, as a percentage of the weight of the processed milk) and 4 nutrient recoveries in the curd (RECFAT, RECPROTEIN, RECSOLIDS, and RECENERGY, which each represented the percentage ratio between the nutrient in the curd and milk). Data were analyzed using a linear mixed model with the fixed effects of days in milk, parity, and somatic cell score (SCS), and the random effect of herd-date. Somatic cell score had strong influences on casein number and lactose, and also affected pH; these were traits characterized by a quadratic pattern of the data. The results also showed a negative linear relationship between SCS and milk yield. Somatic cell score influenced almost all of the tested coagulation traits (both traditional and modeled), with the exceptions of k20, CFP, and kSR. Gelation was delayed when the SCS decreased (slightly) and when it increased (strongly) with respect to a value of 2, as confirmed by the quadratic patterns observed for both RCT and RCTeq. The SCS effect on a30 showed a quadratic pattern almost opposite to that observed for RCT. With respect to the CFt parameters, kCF decreased linearly as SCS increased, resulting in a linear decrease of CFmax and a quadratic pattern for tmax. Milk SCS attained significance for %CYCURD, %CYWATER, and RECPROTEIN. As the SCS increased beyond 3, we observed a progressive quadratic decrease of the water retained in the curd (%CYWATER), which caused a parallel decrease in %CYCURD. With respect to RECPROTEIN, the negative effect of SCS was almost linear. Recovery of fat and (consequently) RECENERGY was characterized by a more evident quadratic trend, with the most favorable values associated with an intermediate SCS. Together, our results confirmed that high SCS has a negative effect on milk composition and technological traits, highlighting the nonlinear trends of some traits across the different classes of SCS. Moreover, we report that a very low SCS has a negative effect on some technological traits of milk.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo ...milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity pH, titratable acidity (TA), and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to investigate the effect of pregnancy stage on milk yield, composition traits, and milk coagulation properties in Italian Holstein cattle. The data set included 25,729 ...records from 3,995 first-parity cows calving between August 2010 and August 2013 in 167 herds. The traits analyzed were milk yield (kg/d), fat (%), protein (%), casein (%), and lactose (%) contents, pH, somatic cell score, rennet coagulation time (min), and curd firmness (mm). To better understand the effect of gestation on the aforementioned traits, each record was assigned to one of the following classes of pregnancy stage: (1) nonpregnant, (2) pregnant from 1 to 120d, (3) pregnant from 121 to 210d, and (4) pregnant from 211 to 310d. Gestation stage significantly influenced all studied traits with the exception of somatic cell score. Milk production decreased and milk quality improved from the fourth month of pregnancy onward. For all traits, nonpregnant cows performed very similarly to cows in the first period of gestation. Rennet coagulation time and curd firmness were influenced by pregnancy stage, especially in the last weeks of gestation when milk had better coagulation characteristics; this information should be accounted for to adjust test-day records in genetic evaluation of milk coagulation properties.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study investigated the modeling of curd-firming (CF) over time (CFt) of sheep milk. Milk samples from 1,121 Sarda ewes from 23 flocks were analyzed for coagulation properties. Lactodynamographic ...analyses were conducted for up to 60min, and 240 CF individual observations from each sample were recorded. Individual sample CFt equation parameters (RCTeq, rennet coagulation time; CFP, asymptotic potential value of curd firmness; kCF, curd-firming instant rate constant; and kSR, curd syneresis instant rate constant) were estimated, and the derived traits (CFmax, the point at which CFt attained its maximum level, and tmax, the time at which CFmax was attained) were calculated. The incidence of noncoagulating milk samples was 0.4%. The iterative estimation procedure applied to the individual coagulation data showed a small number of not-converged samples (4.4%), which had late coagulation and an almost linear pattern of the ascending part of the CFt curve that caused a high value of CFP, a low value of kCF, and a high value of kSR. Converged samples were classified on the basis of their CFt curves into no-kSR (18.0%), low-kSR (72.6%), and high-kSR (4.5%). A CFt that was growing continuously because of the lack of the syneresis process characterized the no-kSR samples. The high-kSR samples had a much larger CFP, a smaller kCF, and an anticipation of tmax, whereas the low-kSR samples had a fast kCF and a slower kSR. The part of the average CFt curves that showed an increase was similar among the 3 different syneretic groups, whereas the part that decreased was different because of the expulsion of whey from the curd. The traditional milk coagulation properties recorded within 30min were not able to detect any appreciable differences among the 4 groups of coagulating samples, which could lead to a large underestimation of the maximum CF of all samples (if predicted by a30), with the exception of the no-kSR samples. Large individual variability was found and was likely caused by the effects of the dairy system, such as flock size (on CFmax, tmax, and % ewes with no-kSR milk), flock within flock size (representing 11 to 43% of total variance for % ewes with no-kSR milk and CFmax, respectively), days in milk (on all model parameters and CFmax), parity (on RCTeq, kSR, and CFmax), daily milk yield (on RCTeq and CFmax), and position of the individual pendulum that significantly affected model parameters and derived traits. In conclusion, the results showed that the modeling of coagulation, curd-firming, and syneresis is a suitable tool to achieve a deeper interpretation of the coagulation and curd-firming processes of sheep milk and also to study curd syneresis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to test the modeling of curd-firming (CF) measures and to compare the sheep milk of 3 Alpine breeds supplemented with or without rumen-protected conjugated linoleic acid ...(rpCLA). Twenty-four ewes of the Brogna, Foza, and Lamon breeds were allotted to 6 pens (2 pens/breed) and fed a diet composed of corn grain, corn silage, dried sugar beet pulp, soybean meal, wheat bran, wheat straw, and a vitamin-mineral mixture. The rpCLA supplement (12g/d per ewe plus 4g/d for each lamb older than 30d) was mixed into the diet of 1 pen per sheep breed (3 pens/treatment) to provide an average of 0.945 and 0.915g/d per ewe of the cis-9,trans-11 C18:2 and trans-10,cis-12 C18:2 conjugated linoleic acid isomers, respectively. The trial started at 38±23d after parturition, and individual morning milk samples were collected on d 16, 23, 37, 44, and 59 of the trial. Milk samples were analyzed for composition, and duplicate samples were assessed for milk coagulation properties (MCP). A total of 180 CF measures for each sample (1 every 15s) were recorded. Model parameters were the rennet coagulation time, the asymptotic potential CF, the CF instant rate constant, the syneresis instant rate constant, the maximum CF achieved within 45min (CFmax), and the time at achievement of CFmax. The data were analyzed using a hierarchical model that considered the fixed effects of breed, diet, lamb birth, and initial days in milk, which were tested on individual ewe (random) variance; the fixed effect of sampling day, which was tested on the within-ewe sample (random) variance; and the fixed effect of instrument or cuvette position (only for MCP), which was tested on the residual (replicates within samples) variance. The local Alpine sheep breeds displayed similar milk compositions, traditional MCP, and CF modeling parameters. Supplementation with rpCLA triggered changes in milk composition and worsened MCP (e.g., delayed rennet coagulation time, slower CF instant rate constant, and a doubling of syneresis instant rate constant), but did not influence potential CF. Overall, our results indicate that rpCLA supplementation reduced the actual maximum CF (CFmax) but did not modify the interval between rennet addition and CFmax or time to CFmax.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The economic values (EV) of production traits, rennet coagulation time (RCT, min), and curd firmness (a30, mm) were derived for Italian Holstein-Friesian dairy cattle, based on the Grana Padano ...cheese industry. Three different sets of EV for RCT and a30 were estimated, assuming +2.5% (scenario 1), +5% (scenario 2), and +10% (scenario 3) increment in cheese yield due to the effect of milk coagulation properties (MCP). A model was developed to simulate the transformation of milk into Grana Padano cheese. The EV of RCT and a30 were −€2.213, −€4.426, and −€8.852/min, and €0.877, €1.755, and €3.509/mm for scenarios 1, 2, and 3, respectively. Relative emphasis of traits in the breeding objectives of the Italian Holstein-Friesian dairy cattle population should account for the effect of MCP on cheese yield. Economic values for milk components and MCP were affected by changes of dairy products, whereas variations of feed prices did not influence EV of RCT and a30.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of the present study was to compare milk coagulation properties measured through a traditional mechanical device, the Formagraph (FRM; Foss Electric A/S, Hillerød, Denmark), and a ...near-infrared optical device, the Optigraph (OPT; Ysebaert SA, Frépillon, France). Individual milk samples of 913 Brown Swiss cows from 63 herds located in Trento Province (Italy) were analyzed for rennet coagulation time (RCT, min), curd-firming time (k20, min), and 2 measures of curd firmness (a30 and a45,mm) using the 2 instruments and under identical conditions. The trial was performed in the same laboratory, by the same technician, and following the same procedures. Extending the analysis by either instrument to 90min permitted RCT and k20 values to be obtained even for late-coagulating milk samples. Milk coagulation properties measured using the OPT differed considerably from those obtained using the FRM. The average k20 values varied greatly (8.16 vs. 5.36min for the OPT and the FRM, respectively), as did the a45 figures (41.49 vs. 33.66mm for the OPT and the FRM, respectively). The proportion of noncoagulating samples for which k20 could be estimated differed between instruments, being less for the OPT. The between-instrument correlation coefficients were either moderate (0.48 for a30) or low (0.24 and 0.17 for k20 and a45, respectively) when the same traits were compared. The correlations between k20 and a45, and milk yield varied among instruments, as did the correlations between k20, a30, and a45 and milk composition, and the correlations between a45 and pH. The relative influence of days in milk on k20 and a45 varied, as did the effect of parity on a45 and that of the measuring unit of coagulation meter on k20 and a30. The RCT estimated by the OPT was the only milk coagulation property to show good agreement with the FRM-derived value, although this was not true for the data from late-coagulating samples.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Recently, a general deterioration of milk coagulation properties (MCP) has been observed in Italy; thus, the prediction of noncoagulating (NC) milk, defined as milk not forming a curd within 30min ...from rennet addition, is of immediate interest in the Italian cheese industry. The present study investigated the ability of mid-infrared (MIR) spectroscopy to predict NC milk using individual and bulk samples from Holstein cows. Samples were selected according to MIR analysis to cover the range of coagulation time between 5 and 60min. Milks were then analyzed for MCP through the reference instrument (Formagraph) over an extended testing period of 60min to identify coagulating and NC samples. Measured traits were rennet coagulation time, curd-firming time, and curd firmness 30 and 60min after rennet addition. Results showed no specific spectral information distinguishing NC from coagulating samples. The most accurate prediction model was developed for rennet coagulation time followed by curd-firming time and curd firmness 30min after rennet addition, whereas curd firmness 60min after enzyme addition could not be accurately predicted. Based on these findings, MIR spectroscopy might be proposed in payment systems to reward or penalize milk according to MCP. Moreover, the ability of MIR spectroscopy to predict the MCP of samples that form a curd beyond 30min from enzyme addition may be of interest for genetic improvement of coagulation traits in dairy breeds, because until now most studies have excluded NC information from genetic analysis, leading to possible biases in the estimation of genetic parameters and in the prediction of sire’s merit for MCP.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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