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Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

Open AccessPublished:May 12, 2022DOI:https://doi.org/10.3168/jds.2022-21848

      ABSTRACT

      The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

      Key words

      INTRODUCTION

      Approximately 39% of the world dairy goat population is located in high-income countries (
      • FAOSTAT (Food and Agriculture Organization of the United Nations)
      Statistics database: Crops and livestock products.
      ), mainly North America and Europe, where the modern dairy systems have been developed maintaining some traditional approaches (i.e.,
      • Clark S.
      • Mora García M.B.
      A 100-Year Review: Advances in goat milk research.
      ), and are characterized by rearing both local and cosmopolitan dairy breeds (
      • Miller B.A.
      • Lu C.D.
      Current status of global dairy goat production: An overview.
      ). A major part of the goat milk is used to produce cheese; thus, cheese yield (%CY) is a key component for increasing farm profitability. Laboratory cheese-making procedures have been developed in recent years, mimicking cheese manufacture at the individual animal level in controlled and standardized conditions (
      • Jacob M.
      • Jaros D.
      • Rohm H.
      The effect of coagulant type on yield and sensory properties of semihard cheese from laboratory-, pilot- and commercial-scale productions.
      ;
      • Cipolat-Gotet C.
      • Cecchinato A.
      • Stocco G.
      • Bittante G.
      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.
      ), offering the opportunity to observe animal variability and to recover nutrients in the curd. The information acquired with those procedures allow estimation of heritability of measured %CY, which was found to be around 0.19 to 0.27 for bovine (
      • Bittante G.
      • Cipolat-Gotet C.
      • Cecchinato A.
      Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process.
      ;
      • Dadousis C.
      • Cipolat-Gotet C.
      • Bittante G.
      • Cecchinato A.
      Inferring genetic parameters on latent variables underlying milk yield and quality, protein composition, curd firmness and cheese-making traits in dairy cattle.
      ) and about 0.15 to 0.30 for ovine species (
      • Sánchez-Mayor M.
      • Pong-Wong R.
      • Gutiérrez-Gil B.
      • Garzón A.
      • de la Fuente L.F.
      • Arranz J.J.
      Phenotypic and genetic parameter estimates of cheese-making traits and their relationships with milk production, composition and functional traits in Spanish Assaf sheep.
      ;
      • Pelayo R.
      • Gutiérrez-Gil B.
      • Garzón A.
      • Esteban-Blanco C.
      • Marina H.
      • Arranz J.J.
      Estimation of genetic parameters for cheese-making traits in Spanish Churra sheep.
      ). Despite the aforementioned advantages, the collection and processing of milk samples at individual level are still time-consuming and labor-intensive. Therefore, predictions of %CY need to be investigated, although actual measures from laboratory cheese-making are fundamental for their study. Nowadays, the use of predictive formulas for %CY traits based on major milk components is still limited to the use of bulk milk at the dairy industry level. Use of other indirect methods, such as infrared spectroscopy, for measuring %CY traits in goat milk is still under investigation. Hence, that information cannot be used for breeding purposes, and neither it can be applied at population level, as the prediction accuracies are expected to be unsatisfactory, mainly because of the lower variability of the calibration data set used developed at the dairy industry level with respect to the external set (population level). Indeed, as evidenced by recent studies, the coagulation (
      • Pazzola M.
      • Stocco G.
      • Paschino P.
      • Dettori M.L.
      • Cipolat-Gotet C.
      • Bittante G.
      • Vacca G.M.
      Modeling of coagulation, curd firming, and syneresis of goat milk.
      ;
      • Vacca G.M.
      • Stocco G.
      • Dettori M.L.
      • Pira E.
      • Bittante G.
      • Pazzola M.
      Milk yield, quality and coagulation properties of six breeds of goats: environmental and individual variability.
      ) and cheese-making abilities of goat milk (
      • Stocco G.
      • Pazzola M.
      • Dettori M.L.
      • Paschino P.
      • Summer A.
      • Cipolat-Gotet C.
      • Vacca G.M.
      Effects of indirect indicators of udder health on nutrient recovery and cheese yield traits in goat milk.
      ;
      • Vacca G.M.
      • Stocco G.
      • Dettori M.L.
      • Bittante G.
      • Pazzola M.
      Goat cheese yield and recovery of fat, protein, and total solids in curd are affected by milk coagulation properties.
      ) are characterized by large variability, due to factors related to the animal and the breed of goat, mainly related to variations of milk composition. Providing %CY prediction formulas at goat population level could be useful for implementing milk payment systems, and for new selection indices focused on cheese-making ability. Those formulas would be extremely advantageous in indigenous breeds (
      • Vacca G.M.
      • Stocco G.
      • Dettori M.L.
      • Pira E.
      • Bittante G.
      • Pazzola M.
      Milk yield, quality and coagulation properties of six breeds of goats: environmental and individual variability.
      ;
      • Paschino P.
      • Stocco G.
      • Dettori M.L.
      • Pazzola M.
      • Marongiu M.L.
      • Pilo C.E.
      • Cipolat-Gotet C.
      • Vacca G.M.
      Characterization of milk composition, coagulation properties and cheese-making ability of goats reared in extensive farms.
      ), and could pioneer local economies. For those purposes, such models need to be derived from individual milk samples.
      Our objectives were to (1) develop predictive formulas for %CY traits (fresh curd, milk solids, and water retained in the curd) based on milk major components (fat, protein, and casein) and udder health indicators (lactose, pH, and somatic cell and bacterial counts); (2) assess the cheese-making method; and (3) quantify the effect of goat breed on the prediction accuracy of the %CY formulas.

      MATERIALS AND METHODS

      Farm Characteristics and Milk Sampling

      Milk samples from 600 goats reared in 36 farms on the island of Sardinia (Italy) were collected (1 sampling day per farm; 16–40 animals per farm). About 200 mL per goat was collected during afternoon milking. Sampled goats belonged to 6 breeds, 2 representing the Alpine type, namely Saanen (Sa, 99 goats) and Camosciata delle Alpi (CA, 98 goats), and 4 breeds of the Mediterranean type, Murciano-Granadina (MG, 89 goats), Maltese (Ma, 104 goats), Sarda (Sr, 126 goats), and Sarda Primitiva (SP, 84 goats). Farms were characterized by 3 types of management system: traditional (or extensive, n = 14), intermediate (or semi-extensive, n = 11), and modern (or semi-intensive, n = 11). Details of these systems were previously reported by
      • Vacca G.M.
      • Stocco G.
      • Dettori M.L.
      • Pira E.
      • Bittante G.
      • Pazzola M.
      Milk yield, quality and coagulation properties of six breeds of goats: environmental and individual variability.
      .

      Analysis of Milk Composition

      Immediately after collection, individual milk samples were stored at 4°C and then analyzed within 24 h. All samples were analyzed for fat, protein, casein, lactose, total solids, and pH with a MilkoScan FT6000 infrared analyzer (Foss Electric A/S) calibrated in accordance with the related reference methods [ISO 9622/IDF 141 (
      • ISO-IDF (International Organization for Standardization and International Dairy Federation)
      Milk and liquid milk products: Determination of fat, protein, casein, lactose and pH content. International Standard ISO 9622 and IDF 141:2013.
      ) for fat, protein, casein, lactose, and pH; ISO 6731/IDF 21 (
      • ISO-IDF (International Organization for Standardization and International Dairy Federation)
      Milk and liquid milk products: Determination of total solids content. International Standard ISO 6731 and IDF 21:2010a.
      ) for TS]. Somatic cell count was determined by a Fossomatic 5000 somatic cell counter (Foss Electric A/S) and transformed into the logarithmic SCS [log2(SCC × 10−5) +3] (
      • Ali A.
      • Shook G.
      An optimum transformation for somatic cell concentration in milk.
      ). Total bacterial count was measured by a BactoScan FC150 analyzer (Foss Electric A/S) and transformed into the logarithmic bacterial count [LBC = log10(total bacterial count/1,000)].

      Individual Cheese-Making Procedure

      The 9-mL laboratory cheese-making method (9-MilCA) was adopted to measure individual %CY traits for each milk sample, as described in
      • Cipolat-Gotet C.
      • Cecchinato A.
      • Stocco G.
      • Bittante G.
      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.
      , processing 2 replicates per animal (9 mL per each replicate), for a total of 600 goats and 1,200 observations, respectively. In brief, each milk replicate was transferred into a glass tube (9 mL), inserted into the modified sample rack of the lactodynamograph instrument, heated to 35°C for 15 min, and mixed with 0.2 mL of a rennet solution [Hansen Standard 215, with 80 ± 5% chymosin and 20 ± 5% pepsin; 215 international milk clotting units per milliliter; Pacovis Amrein AG; diluted to 1.2% (wt/vol) in distilled water]. The sample rack was then transferred from the heater to the lactodynamograph (30 min duration test). Coagulation occurred at 35°C. At the end of this phase, coagulated milk samples were manually cut using a stainless-steel spatula, and the rack was moved to the heater for the 30-min curd-cooking phase (55°C). At 15 min after the beginning of the cooking phase, each sample was subjected to a second manual cutting. Further, each glass tube was removed from the sample rack, and the curd was separated from the whey. The curd was slightly pressed to aid expulsion of whey, and the curd was suspended above the whey at room temperature (15 min). The obtained curds and whey were weighed using a precision scale. Then, the whey of the 2 replicates of each milk sample was pooled and analyzed for chemical composition using an infrared spectrophotometer (MilkoScan FT2, Foss Electric). The measured %CY traits [the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER)] were calculated as follows:
      %CYCURD=weightofcurd(g)weightofmilk(g)×100;


      %CYSOLIDS=milkTS(g)-wheyTS(g)weightofmilk(g)×100;


      %CYWATER=milkwater(g)-wheywater(g)weightofmilk(g)×100.


      Statistical Analysis

      Editing.

      Before statistical analysis, all traits (milk composition and %CY measures) showing values outside the interval of the mean ± 3 standard deviations (SD) were excluded as outliers.

      Regression Models.

      A series of linear regression models were applied for %CYCURD, %CYSOLIDS, and %CYWATER, separately. Milk fat, protein, and casein were used as predictors either one at a time or in combinations. The best predictive model derived included fat and casein and was further extended including predictors related to udder health (udder health indicators, UHI: lactose, SCS, pH, and LBC), selected on the basis of their technological roles and effects on cheese production (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ;
      • Pazzola M.
      • Stocco G.
      • Dettori M.L.
      • Bittante G.
      • Vacca G.M.
      Effect of goat milk composition on cheesemaking traits and daily cheese production.
      ;
      • Stocco G.
      • Pazzola M.
      • Dettori M.L.
      • Cipolat-Gotet C.
      • Summer A.
      • Vacca G.M.
      Variation in caprine milk composition and coagulation as affected by udder health indicators.
      ). Multicollinearity of all predictors was also checked by evaluation of tolerance, variance inflation factor, eigenvalues, and condition index (Supplemental Table S1, https://figshare.com/articles/dataset/Supplemental_Table_S1/19694800) before using them in the combination models. The results obtained from those tests evidenced the absence of multicollinearity among predictors. Therefore, the 2 groups of predictive models were tested as follows:
      • (1)
        Basic composition—that is, fat, protein, or casein, tested individually and in combination (see Table 2);
      • (2)
        Basic composition combined with UHI—that is, fat + casein + combinations of lactose level, SCS, pH, and LBC (see Table 3).
      For all the %CY measures, we tested regression models with or without intercept. Fitting statistics between the 2 models were comparable (data not shown). Thus, results from models with the intercept are not reported, as our goal was to quantify the actual contribution of each of the predictors to %CY.

      Validation Procedures.

      The accuracy of the %CY predictive formulas was assessed by different procedures: (a) a random cross-validation (CrV) scheme with 10 replicates was adopted to address the first objective, that is, to quantify the effects of the major milk components and those related to UHI on %CY traits, where data were split into a training set (80% of the total records), used to build the model, and a testing set (20% of the total records), used as validation; (b) a within-animal validation procedure was used to assess the effect of laboratory cheese-making method on the accuracy of the %CY predictive formulas (second objective), where a training data set composed of the first measurement within animal was used to build the predictive models and the second measurement within animal was used in the testing set; and (c) a stratified CrV (SCrV) for the third objective (i.e., to quantify the effect of goat breed on the prediction accuracy of the %CY predictive formulas), evaluating each breed separately (testing set) by using the records from the other 5 breeds in the training set. To further investigate the within-breed relationships between each milk component and the %CY measures, regression models were used testing the predictors individually (Supplemental Figure S1, https://figshare.com/articles/figure/Supplemental_Figure_S1/19694806).

      Assessment of Prediction Accuracy.

      Model assessment was based on coefficient of determination of validation (R2VAL), the root mean square error of validation (RMSEVAL), and the ratio performance deviation, calculated as the ratio between SD and RMSEVAL. In the case of CrV, R2VAL, RMSEVAL, and ratio performance deviation, values were averaged over the 10 replicates.

      RESULTS AND DISCUSSION

      Milk Composition of Individual Goat Milk Samples

      The descriptive statistics of milk composition and %CY traits of individual goat milk samples are given in Table 1. The average contents of fat, protein, and lactose were 4.48%, 3.57%, and 4.68%, respectively, with fat showing the highest coefficient of variation (29%). It is well known that variability of milk composition is a major factor affecting cheese-making efficiency (
      • Vacca G.M.
      • Cipolat-Gotet C.
      • Paschino P.
      • Casu S.
      • Usai M.G.
      • Bittante G.
      • Pazzola M.
      Variation of milk technological properties in sheep milk: Relationships among composition, coagulation and cheese-making traits.
      ). In this study, the use of individual samples showed high variability of milk composition that affected that of %CY traits. Our results showed that, following the 9-MilCA method, the average %CYCURD was 15.5%, with approximately equal contributions of %CYSOLIDS and %CYWATER (mean values of 7.6% and 7.9%, respectively).
      Table 1Descriptive statistics of milk composition and cheese yield traits of individual goat milk samples
      TraitNMeanSDCV, %
      Milk composition
       Fat, %5834.481.2929
       Protein, %5833.570.5014
       Casein, %5832.810.4817
       Lactose, %5834.680.265
       TS, %58313.631.6813
       pH5796.730.101
       SCS5835.601.9234
       LBC
      LBC = logarithmic bacterial count.
      5821.720.8449
      Cheese yield trait,
      Cheese yield traits represent the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      %
       %CYCURD1,05215.52.617
       %CYSOLIDS1,1527.61.621
       %CYWATER1,0807.91.417
      1 LBC = logarithmic bacterial count.
      2 Cheese yield traits represent the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).

      Prediction of Cheese Yield Traits Based on Milk Fat, Protein, or Casein Percentage

      Milk Fat Percentage.

      When milk fat percentage was used as unique predictor in a simple linear regression model for %CY traits (Table 2), regression coefficients (βˆ) varied from 3.31 (%CYCURD) to 1.67 (for both %CYSOLIDS and %CYWATER). The R2VAL of those models was high for %CYSOLIDS (0.89), intermediate for %CYCURD (0.60), and low for %CYWATER (0.17). As is known, the addition of rennet triggers the coagulation process and causes the casein micelles to aggregate, entrapping the majority of the fat globules in the network. Given that fat accounts for the major part of cheese solids in full-fat cheeses and that lipids are hydrophobic (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ), it is not surprising that the validation accuracy of the fat-based model predicting %CYSOLIDS and %CYWATER had opposite values. Compared with a similar analysis on the use of individual predictive formulas in dairy cattle, βˆ of fat were found to be higher for all 3 %CY traits, but R2VAL were lower compared with our results (R2VAL = 0.29, 0.57, and 0.06 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively;
      • Mariani E.
      • Cipolat-Gotet C.
      • Summer A.
      • Malacarne M.
      • Cecchinato A.
      • Bittante G.
      Formulas to predict cheese-yield traits from Brown Swiss milk to improve dairy chain sustainability. Page 222 in Book of Abstract of the 71st Annual Meeting of the European Association for Animal Production, Virtual Meeting.
      ). This could be attributed to the differences in the physicochemical structure and composition of fats between goat and cow milk. For example, goat milk consists of smaller fat globules, compared with bovine milk, which make better dispersion and a more homogeneous mixture in milk, and hence provide a greater surface of fat for lipases to act (
      • Park Y.W.
      • Haenlein G.F.W.
      Handbook of Milk of Non-Bovine Mammals.
      ). Small fat globules behave as pseudo-protein particles, with a greater ability to become part of the gel network (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ). Moreover, in a pathway-based genome-wide association analysis of milk coagulation and cheese-making properties in dairy cattle, the phosphatidylinositol signaling pathways have been proven to be strictly associated with milk technological properties of milk (
      • Dadousis C.
      • Pegolo S.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle.
      ). Phosphatidylinositol represents a small fraction of the phospholipid components of milk, and phospholipids are mainly present on the surface of milk fat globules. The biological explanation of the connection between phosphatidylinositol pathway and coagulation properties can be found in the close association between fat globule size and phospholipid contents, with higher amounts of phospholipids in small globules compared with the large ones, likely affecting the technological properties of milk (
      • Dadousis C.
      • Pegolo S.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle.
      ). These characteristics of milk fat globules might explain the high predictive ability of fat for %CYCURD, and especially %CYSOLIDS. Our results are in agreement with previous research studies, which have clearly evidenced the overall positive and linear effect of goat milk fat on %CYCURD and %CYSOLIDS, and on the recovery of the nutrients (fat and TS) in the curd (
      • Pazzola M.
      • Stocco G.
      • Dettori M.L.
      • Bittante G.
      • Vacca G.M.
      Effect of goat milk composition on cheesemaking traits and daily cheese production.
      ).
      Table 2Regression coefficients ( β^; related SE in parentheses) and validation performance parameters
      Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      from the models of the cross-validation procedure for cheese yield traits
      Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      in fresh cheese based on single nutrients (fat, protein, or casein) and on their combinations
      ItemModels with single nutrientModels with combinations of nutrients
      FatProteinCaseinFat + ProteinFat + Casein
      %CYCURD
       Regression coefficient (β^)
      Fat3.31 (0.00)1.01 (0.02)1.07 (0.02)
      Protein4.32 (0.00)3.05 (0.01)
      Casein5.47 (0.00)3.76 (0.03)
       Validation
      N112112112112112
      R2VAL0.600.410.430.620.61
      RMSEVAL3.041.922.091.611.80
      RPDVAL1.320.830.900.700.78
      SDVAL2.322.322.322.322.32
      %CYSOLIDS
       Regression coefficient (β^)
      Fat1.67 (0.00)0.93 (0.00)0.92 (0.00)
      Protein2.14 (0.00)0.97 (0.00)
      Casein4.09 (0.00)1.25 (0.00)
       Validation
      N115115114115115
      R2VAL0.890.570.540.960.95
      RMSEVAL0.851.061.490.330.35
      RPDVAL0.540.670.740.210.22
      SDVAL1.591.591.971.591.59
      %CYWATER
       Regression coefficient (β^)
      Fat1.67 (0.00)0.27 (0.01)0.34 (0.01)
      Protein2.20 (0.00)1.85 (0.01)
      Casein2.77 (0.00)2.23 (0.02)
       Validation trait
      N113113113113113
      R2VAL0.170.130.130.170.16
      RMSEVAL, %2.021.321.450.331.42
      RPDVAL1.671.091.190.211.17
      SDVAL1.221.221.221.591.22
      1 Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      2 Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).

      Milk Protein and Casein Percentages.

      Milk protein percentage, as predictor of %CY traits (Table 2), provided consistently higher βˆ in all cases compared with fat (4.32, 2.14, and 2.20) but a lower R2VAL (0.41, 0.57, and 0.13) for %CYCURD, %CYSOLIDS, and %CYWATER, respectively. Those results are probably related to the higher variability of fat with respect to the other milk compounds (Table 1). Compared with protein, casein concentration as a unique predictor of the 3 %CY traits showed higher βˆ and almost doubled for %CYSOLIDS (4.09 for casein vs. 2.19 for protein percentage). When casein was tested as individual predictor, the R2VAL was higher for %CYCURD and similar for %CYWATER compared with protein (Table 2), with more profound difference found for %CYSOLIDS (0.54 vs. 0.89, for casein and protein percentage, respectively). Although the quality criteria of goat milk used in most of the milk payment systems are still based on total protein concentration, caseins should be considered as well, because they are essential for the cheese-making process.
      It is true that milk proteins play an active role during coagulation, but the functionality varies based on their size and the actual proportions of casein and whey proteins fractions (
      • Brule G.
      • Lenoir J.
      • Remeuf F.
      The casein micelle and milk coagulation.
      ). For example, in goat milk, the lower casein concentration, different ratios among casein fractions, and higher casein micelle size can explain the weak curd firmness compared with milk of other ruminants (
      • Park Y.W.
      • Juarez M.
      • Ramos M.
      • Haenlein F.W.
      Physico-chemical characteristics of goat and sheep milk.
      ). Also, the number of hydrophobic sites on the protein surface is one of the most important factors affecting the functional properties of protein and caseins during coagulation of milk (
      • Fox P.F.
      • McSweeney P.L.H.
      Dairy Chemistry and Biochemistry.
      ;
      • Hiller B.
      • Lorenzen P.C.
      Surface hydrophobicity of physicochemically and enzymatically treated milk proteins in relation to techno-functional properties.
      ;
      • Yildirim S.
      • Erdem Y.K.
      A tool for explaining the differences on renneting characteristics of milks from different origins: The surface hydrophobicity approach.
      ). In goat milk, the hydrophobic sites on the protein surface are found in lower numbers than in cow milk, combined with lower protein surface binding affinity (
      • Yildirim S.
      • Erdem Y.K.
      A tool for explaining the differences on renneting characteristics of milks from different origins: The surface hydrophobicity approach.
      ). This could partly explain why the contribution of protein and casein (in terms of βˆ) to %CY traits found here was high. However, R2VAL for %CYWATER was more than double in bovine (R2VAL = 0.31 and 0.33 for protein and casein, respectively;
      • Mariani E.
      • Cipolat-Gotet C.
      • Summer A.
      • Malacarne M.
      • Cecchinato A.
      • Bittante G.
      Formulas to predict cheese-yield traits from Brown Swiss milk to improve dairy chain sustainability. Page 222 in Book of Abstract of the 71st Annual Meeting of the European Association for Animal Production, Virtual Meeting.
      ) compared with the caprine values found in this study. This could be attributed to the higher water-holding capacity of bovine proteins than caprine (
      • Yildirim S.
      • Erdem Y.K.
      A tool for explaining the differences on renneting characteristics of milks from different origins: The surface hydrophobicity approach.
      ).

      Prediction of Cheese Yield Traits Based on Fat and Protein, or Fat and Casein Percentage

      In general, predictive formulas for %CY traits built upon the combination of milk components (fat and protein or fat and casein) were, on average, more accurate than the single-nutrient formulas (Table 2). Indeed, the R2VAL for the %CY traits were always higher and the RMSEVAL lower when fat was fitted together with either protein or casein. Overall, these results were expected, as
      • Pazzola M.
      • Stocco G.
      • Dettori M.L.
      • Bittante G.
      • Vacca G.M.
      Effect of goat milk composition on cheesemaking traits and daily cheese production.
      reported that in caprine milk these 3 components together represent the major factors affecting cheese-making process and contributors for %CY.
      The βˆ of fat on the %CYCURD formulas were slightly higher (1.01 with protein, 1.07 with casein) compared with those for %CYSOLIDS (0.93 and 0.92 with protein or casein, respectively), and consistent with the smallest regression coefficients obtained for %CYWATER (0.27 and 0.34 with protein or casein, respectively). This indicates that, although fat on its own has little water-holding capacity, its presence in the paracasein network affects the degree of contraction of the matrix and hence moisture content and %CY. The occluded fat globules physically limit the contraction and hence the aggregation of the surrounding paracasein network; therefore they also reduce the extent of syneresis (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ). Although in goats any significant effect of fat has been reported on syneresis, the expulsion of whey is reduced in milk samples with high fat content (
      • Stocco G.
      • Pazzola M.
      • Dettori M.L.
      • Paschino P.
      • Bittante G.
      • Vacca G.M.
      Effect of composition on coagulation, curd firming, and syneresis of goat milk.
      ). The βˆ of protein was always higher than that of fat (Table 2). This is not surprising, considering that the majority of other solids retained in the curd, especially hydrophilic solids (lactose, soluble salts, and others), are proportional to the quantity of whey retained, which in turn is much more proportional to protein (i.e., whey proteins) than fat (
      • Emmons D.B.
      • Ernstrom C.A.
      • Lacroix C.
      • Verret P.
      Predictive formulas for yield of cheese from composition of milk: A review.
      ). Moreover, the βˆ of casein for %CY traits, when combined with fat, were consistently higher than those of protein combined with fat (Table 2), reflecting its direct role during coagulation, as it forms the continuous paracasein network, acting like a sponge, which occludes the fat and moisture (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ).

      Prediction of Cheese Yield Traits Based on Fat and Casein and Udder Health Indicators

      The inclusion in the statistical model of the UHI traits slightly increased the prediction accuracy of the %CY formulas (Table 3), especially if compared with the fat + protein or fat + casein formulas (Table 2). However, the βˆ gained for the other milk components are useful for increasing our knowledge about the relationships between these traits and the efficiency of the cheese-making process in goats. It is widely recognized that lactose, SCS, milk pH, and LBC are associated in different ways with the udder health status of dairy goats (
      • Leitner G.
      • Merin U.
      • Silanikove N.
      Changes in milk composition as affected by subclinical mastitis in goats.
      ;
      • Pirisi A.
      • Lauret A.
      • Dubeuf J.P.
      Basic and incentive payments for goat and sheep milk in relation to quality.
      ;
      • Bagnicka E.
      • Winnicka A.
      • Jóźwik A.
      • Rzewuska M.
      • Strzałkowska N.
      • Kościuczuk E.
      • Prusak B.
      • Kaba J.
      • Horbańczuk J.
      • Krzyżewski J.
      Relationship between somatic cell count and bacterial pathogens in goat's milk.
      ). Somatic and bacterial counts are of further importance, as they are fundamental parameters for establishing the hygienic quality of raw milk, and are currently used in different milk payment systems (
      • Pirisi A.
      • Lauret A.
      • Dubeuf J.P.
      Basic and incentive payments for goat and sheep milk in relation to quality.
      ).
      Table 3Regression coefficients ( β^; related SE in parentheses) and validation performance parameters
      Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      from the models of the cross-validation procedure for cheese yield traits,
      Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      based on fat, casein, and combinations of udder health indicators
      Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).
      of individual goat milk samples
      ItemModel combination
      Fat + casein + lactoseFat + casein + SCSFat + casein + pHFat + casein + LBCFat + casein + lactose + SCSFat + casein + lactose + SCS + pHFat + casein + lactose + SCS + pH + LBC
      %CYCURD
       Regression coefficient (β^)
      Fat, %/%1.14 (0.01)1.05 (0.02)1.11 (0.01)1.09 (0.02)1.13 (0.01)1.12 (0.01)1.11 (0.01)
      Casein, %/%1.34 (0.03)3.59 (0.03)1.46 (0.02)3.61 (0.03)1.29 (0.03)1.38 (0.02)1.38 (0.03)
      Lactose, %/%1.43 (0.01)1.41 (0.01)0.52 (0.05)0.51 (0.04)
      SCS0.11 (0.01)0.04 (0.01)0.00 (0.01)0.00 (0.03)
      pH0.97 (0.01)0.64 (0.03)0.66 (0.00)
      LBC0.21 (0.01)−0.07 (0.01)
       Validation
      N112112112112112112112
      R2VAL0.650.610.640.610.650.640.65
      RMSEVAL1.381.791.391.791.381.401.39
      RPDVAL0.600.780.600.770.600.600.59
      SDVAL2.322.322.322.352.322.322.35
      %CYSOLIDS
       Regression coefficient (β^)
      Fat, %/%0.92 (0.00)0.92 (0.00)0.93 (0.00)0.92 (0.00)0.92 (0.00)0.93 (0.00)0.93 (0.00)
      Casein, %/%1.09 (0.01)1.23 (0.00)1.07 (0.00)1.25 (0.00)1.08 (0.01)1.09 (0.01)1.09 (0.00)
      Lactose, %/%0.09 (0.00)0.09 (0.00)−0.08 (0.02)−0.09 (0.01)
      SCS0.01 (0.00)0.01 (0.00)0.00 (0.00)0.00 (0.01)
      pH0.07 (0.00)0.12 (0.01)0.13 (0.00)
      LBC0.00 (0.00)−0.02 (0.00)
       Validation
      N115115114115115114114
      R2VAL0.950.950.960.950.950.960.96
      RMSEVAL0.350.360.340.350.350.340.34
      RPDVAL0.220.220.210.220.220.210.21
      SDVAL1.591.591.621.581.591.621.62
      %CYWATER
       Regression coefficient (β^)
      Fat, %/%0.38 (0.01)0.32 (0.01)0.36 (0.01)0.35 (0.01)0.38 (0.01)0.37 (0.01)0.36 (0.01)
      Casein, %/%0.23 (0.02)2.08 (0.02)0.33 (0.02)2.16 (0.02)0.19 (0.02)0.23 (0.02)0.25 (0.02)
      Lactose, %/%1.19 (0.01)1.18 (0.01)0.71 (0.04)0.75 (0.04)
      SCS0.09 (0.00)0.03 (0.00)0.00 (0.00)0.01 (0.03)
      pH0.80 (0.01)0.34 (0.03)0.34 (0.00)
      LBC0.10 (0.01)−0.13 (0.01)
       Validation trait
      N113113112113113112112
      R2VAL0.220.160.200.170.220.210.23
      RMSEVAL1.091.421.121.411.091.111.12
      RPDVAL0.891.170.901.150.890.890.88
      SDVAL1.221.221.251.231.221.251.27
      1 Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      2 Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      3 Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).

      Lactose.

      When lactose was used as predictor for %CY traits together with fat and casein, it provided βˆ values of 1.43, 0.09, and 1.19 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively (Table 3). These values are higher compared with those in bovine milk for %CYCURD and %CYWATER, and very similar for %CYSOLIDS (
      • Mariani E.
      • Cipolat-Gotet C.
      • Summer A.
      • Malacarne M.
      • Cecchinato A.
      • Bittante G.
      Formulas to predict cheese-yield traits from Brown Swiss milk to improve dairy chain sustainability. Page 222 in Book of Abstract of the 71st Annual Meeting of the European Association for Animal Production, Virtual Meeting.
      ). It is known that about 98% of the lactose in milk is lost in the whey during cheese-making (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ), and the remaining part is bound to the water in fresh curd. This explains why lactose had a minimal part in the formulas used to predict %CYSOLIDS, but it largely contributed to %CYWATER, even after the inclusion of all the other UHI (Table 3). These characteristics influenced the precision of the predictive formulas for %CYCURD. Although a direct effect of lactose on cheese-making is not evident, the fermentation of the small part remaining in the fresh curd has a significant effect on cheese quality (
      • Fox P.F.
      • Guinee T.P.
      • Cogan T.M.
      • McSweeney P.L.H.
      Fundamentals of cheese science.
      ).

      SCS.

      When SCS was used as predictor of %CY traits, together with fat and casein, it contributed 0.11, 0.01, and 0.09 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively (Table 3), providing lower (in the case of %CYCURD and %CYWATER) or equal (%CYSOLIDS) R2VAL values to those observed when lactose was included as predictor. In a previous study, it was reported that high SCS was associated with high amount of moisture retained in the curd, resulting in a nonlinear increase of %CYCURD, but with a lower recovery of milk protein in the curd (
      • Stocco G.
      • Pazzola M.
      • Dettori M.L.
      • Paschino P.
      • Summer A.
      • Cipolat-Gotet C.
      • Vacca G.M.
      Effects of indirect indicators of udder health on nutrient recovery and cheese yield traits in goat milk.
      ). Hence, we further tested the linear and quadratic regressions for the effect of SCS on %CY traits, but no differences were observed in the fitting statistics with respect to models including SCS as linear predictor (data not shown). The low βˆ found here confirmed that high values of somatic cells in goat milk should not necessarily be associated with mastitic milk (
      • Contreras A.
      • Sierra D.
      • Sanchez A.
      • Corrales J.C.
      • Marco J.C.
      • Paape M.J.
      • Gonzalo C.
      Mastitis in small ruminants.
      ), and that the contribution of SCS to %CY traits is negligible. When combined with other UHI (i.e., + lactose, or + lactose + pH, or + lactose + pH + LBC) the βˆ of SCS reduced to zero, and the fitting statistics marginally improved.

      pH.

      When milk pH was included in the predictive formulas with fat and casein, this resulted in βˆ <1 for all %CY traits and close to zero for %CYSOLIDS (Table 3). As for lactose, the predictive performance of the model with pH slightly outperformed the models with SCS. The contribution of pH on water retention was higher than that of fat and casein. Indeed, pH has a strong influence on whey expulsion, but, in particular, the change in pH leads to different conformations in goat milk proteins and distribution of hydrophobic groups inside and outside the molecule, resulting in changes in the surface hydrophobicity of the protein during heating (
      • Lam R.S.H.
      • Nickerson M.T.
      The effect of pH and temperature pre-treatments on the physicochemical and emulsifying properties of whey protein isolate.
      ). When pH was included with the other UHI, the regression coefficient of lactose reduced by almost 3 times compared with the model with fat, casein, and lactose (1.43 vs. 0.52) in predicting %CYCURD; the sign changed (0.09 vs. −0.08) in the case of %CYSOLIDS; and the regression coefficient almost halved (1.19 vs. 0.71) in the prediction of %CYWATER (Table 3). Although the βˆ of pH reduced in the case of %CYCURD, moving from the model with fat, casein, and pH to the comprehensive model (0.97 vs. 0.66), it almost doubled in %CYSOLIDS (0.07 vs. 0.13), and more than halved (0.80 vs. 0.34) in the case of %CYWATER. These changes in the βˆ values of each milk component among different groups of predictive formulas describe the effective role they have during coagulation and cheese-making. When considered alone, each component was not fully able to describe its real contribution, as the variability of the βˆ values within milk component and across predictive formulas was very high, especially for %CYCURD and %CYWATER. This could be due to the fact that they carried the indirect effects of the other nonincluded components, even though the prediction accuracies were already high using only fat and casein, as well as in combinations with lactose level, pH, and SCS, especially in the case of %CYSOLIDS.

      LBC.

      When LBC was used as predictor for %CY traits, together with fat and casein, it had βˆ of 0.21, 0.00, and 0.10 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively (Table 3), whereas R2VAL were comparable with the model of SCS. However, the technological meaning of LBC became clearer when combined in a model with all the other components. For instance, it showed negative βˆ for all 3 %CY measures, which agrees with previous studies on the effect of LBC on goat milk coagulation properties and cheese-making traits (
      • Stocco G.
      • Pazzola M.
      • Dettori M.L.
      • Paschino P.
      • Summer A.
      • Cipolat-Gotet C.
      • Vacca G.M.
      Effects of indirect indicators of udder health on nutrient recovery and cheese yield traits in goat milk.
      ). Moreover, in the formula considering all the components tested in the present study, lactose also displayed a negative coefficient, whereas the effect of SCS was negligible (0.00).

      Accuracy of the Cheese-Making Method

      Table 4 reports the βˆ and the validation performance measures (R2VAL, RMSEVAL, ratio performance deviation, and SD) from the within-animal validation procedure performed on the predictive formulas for the 3%CY traits based on fat, casein, and UHI of individual goat milk samples. The βˆ values were slightly different compared with those of the same combination formula in CrV, only in terms of fat, casein, and lactose, in predicting %CYCURD and %CYWATER (Table 4). Moreover, compared with the CrV procedure, the R2VAL values for %CYCURD (0.76) and %CYWATER (0.27) increased but still remained low for %CYWATER. The %CYSOLIDS held the highest prediction accuracy (R2VAL = 0.96). This procedure allowed us to obtain an indirect estimation of repeatability of the 9-MilCA method, as this validation procedure uses the first %CY measure as calibration set and the second as validation set. Estimates of the repeatability of the %CY measures are limited in the literature, as the laboratory procedures at the individual animal level usually do not provide analyses of the cheese-making in duplicate, due to the quantity of milk needed and the workload required. Nevertheless, the efficiency of the 9-MilCA method has been previously demonstrated, with this method being a powerful research tool for a rapid and inexpensive analysis of a large number of milk samples in duplicate, yielding in a complete picture of the cheese-making process (
      • Cipolat-Gotet C.
      • Cecchinato A.
      • Stocco G.
      • Bittante G.
      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.
      ). In goats, previous studies have reported the repeatability of the %CY measures expressed as the ratio of the sum of the variances of the random effects included in the model to the sum of the total variance (
      • Paschino P.
      • Stocco G.
      • Dettori M.L.
      • Pazzola M.
      • Marongiu M.L.
      • Pilo C.E.
      • Cipolat-Gotet C.
      • Vacca G.M.
      Characterization of milk composition, coagulation properties and cheese-making ability of goats reared in extensive farms.
      ). Those authors reported repeatability values of 93.3, 99.9, and 89.7%, respectively, for %CYCURD, %CYSOLIDS, and %CYWATER. Similarly, in bovines,
      • Cipolat-Gotet C.
      • Cecchinato A.
      • Stocco G.
      • Bittante G.
      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.
      reported repeatability values of 83.8, 99.5, and 67.3%, respectively, for %CYCURD, %CYSOLIDS, and %CYWATER.
      Table 4Regression coefficients (β^) and validation performance traits
      Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      from the models of the within-animal validation procedure for cheese yield traits
      Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      based on fat, casein, and udder health indicators (UHI)
      Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).
      of individual goat milk samples
      ItemModel with fat, casein, and UHI
      %CYCURD%CYSOLIDS%CYWATER
      Regression coefficient (β^)
       Fat, %/%1.340.920.35
       Casein, %/%1.351.090.40
       Lactose, %/%0.60−0.070.66
       SCS−0.010.00−0.02
       pH0.470.130.37
       LBC−0.11−0.02−0.16
      Validation trait
       N522571536
       R2VAL0.760.960.27
       RMSEVAL1.300.341.19
       RPDVAL0.490.210.86
       SDVAL2.631.611.39
      1 Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      2 Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      3 Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).

      Prediction of Cheese Yield Traits Across Goat Breeds

      Based on the results of the first procedure, a stratified CrV was applied using the best model identified in CrV. Table 5 summarizes the βˆ and validation performance parameters from the models of the SCrV procedure for %CYCURD, %CYSOLIDS, and %CYWATER based on fat, casein, and UHI of individual goat milk samples. As regards the βˆ provided by the milk components, some differences were noticed across breeds, in particular for lactose, pH, and LBC in predicting all 3 %CY measures. Larger differences were evidenced in R2VAL, with the ranking of the breeds related to the %CY considered. For example, MG had the highest R2VAL (0.68), followed by Sr (0.54), SP (0.48), CA (0.39), Sa (0.30), and Ma (0.19) in predicting %CYCURD (Table 5). However, the RMSEVAL did not follow the pattern of R2VAL. Less differences across breeds were found for the βˆ values for %CYSOLIDS, with R2VAL varying from 0.87 (CA) to 0.96 (Sr). The largest differences were observed for %CYWATER, where Sa and Sr had R2VAL close to zero (0.02 and 0.06, respectively), and CA and SP had 0.10, followed by Ma (0.19) and MG (0.36). A previous study investigating the effect of 4 breeds of goat on the prediction accuracy of Fourier-transform infrared spectroscopy on milk coagulation traits clearly evidenced the importance of adopting a SCrV procedure, whose results were strongly influenced by breed, and the general low prediction accuracies restricted practical application (
      • Stocco G.
      • Dadousis C.
      • Vacca G.M.
      • Pazzola M.
      • Paschino P.
      • Dettori M.L.
      • Ferragina A.
      • Cipolat-Gotet C.
      Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy.
      ). In our study, the promising results achieved with high prediction accuracies (≥0.87) for %CYSOLIDS in all breeds were not confirmed for %CYCURD and %CYWATER. Therefore, the existing differences among breeds have to be further investigated. For example, as depicted in Supplemental Figure S1 (https://figshare.com/articles/figure/Supplemental_Figure_S1/19694806), reporting the regression plots of each component considered individually for prediction of the %CY traits per each breed, the relationship of each component with the %CY measures differed by breed. Values of βˆ are reported subsequently only for large differences among breeds.
      Table 5Regression coefficients (β^) and validation performance traits
      Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      from the models of the stratified cross-validation procedure for cheese yield traits
      Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      based on fat, casein, and udder health indicators
      Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).
      of individual goat milk samples across breeds
      Breed of goat: Sa = Saanen; CA = Camosciata delle Alpi; MG = Murciano-Granadina; Ma = Maltese; Sr = Sarda; SP = Sarda Primitiva.
      Item%CYCURD%CYSOLIDS%CYWATER
      SaCAMGMaSrSPSaCAMGMaSrSPSaCAMGMaSrSP
      Regression coefficient (β^)
       Fat, %/%1.031.121.071.141.181.100.910.930.920.930.930.930.320.360.350.360.400.37
       Casein, %/%1.451.291.301.431.351.241.091.061.091.111.081.140.320.260.120.350.290.19
       Lactose, %/%0.370.580.580.790.220.74−0.11−0.06−0.090.02−0.08−0.130.620.750.830.740.530.93
       SCS0.000.000.01−0.010.000.01−0.010.000.000.000.00−0.010.010.010.01−0.020.010.02
       pH0.810.650.690.410.800.560.160.130.140.030.120.140.450.340.370.310.420.23
       LBC−0.12−0.06−0.17−0.04−0.02−0.08−0.02−0.01−0.050.02−0.03−0.02−0.18−0.13−0.17−0.08−0.07−0.15
      Validation trait
       N9595859311874939489100122759395869712071
       R2VAL0.300.390.680.290.540.480.880.870.940.950.960.940.020.100.360.190.060.13
       RMSEVAL1.491.211.471.571.311.550.350.360.410.350.300.381.200.861.071.391.070.99
       RPDVAL0.950.870.600.920.800.770.400.410.270.340.210.261.050.960.870.981.030.95
       SDVAL1.571.402.461.701.632.010.880.901.531.021.411.441.150.891.231.431.031.04
      1 Validation performance traits: R2VAL = coefficient of determination in validation; RMSEVAL = root mean square error of validation; RPDVAL = ratio performance deviation; SDVAL = SD of the validation set.
      2 Cheese yield traits: the ratios between the weight of the milk processed and the weight of the curd (%CYCURD), the curd TS (%CYSOLIDS), and the water retained in the curd (%CYWATER).
      3 Udder health indicators include lactose level, SCS, pH, and logarithmic bacterial count (LBC).
      4 Breed of goat: Sa = Saanen; CA = Camosciata delle Alpi; MG = Murciano-Granadina; Ma = Maltese; Sr = Sarda; SP = Sarda Primitiva.
      Those differences were mostly related to the βˆ values of the UHI predictors, probably because of their lower importance in predicting %CY traits with respect to the other milk components. Indeed, when lactose was used to predict %CYWATER, Ma and SP showed extreme values ( βˆ = 1.70 and −0.71, respectively, for Ma and SP). Moving to %CYSOLIDS, Sa and MG had almost the opposite βˆ values for lactose (1.01 and −1.84, respectively). These breeds again showed extreme values for SCS predicting %CYWATER (0.20 and −0.09, respectively, for MG and Sa), whereas for %CYSOLIDS CA showed the lowest βˆ value (0.32 and −0.02, respectively, for MG and CA). Regarding milk pH, Sr and MG displayed opposite βˆ values for %CYWATER (5.33 and 0.88, respectively), but in the case of %CYSOLIDS, Sa showed the highest and positive (2.69) and MG the lowest and negative (−3.52) βˆ values.
      Regarding UHI predictors, casein and fat βˆ values were more consistent among breeds for all the %CY traits. The only negative association with casein—%CYWATER, in Sr goats ( βˆ = −0.21; Supplemental Figure S1)—suggests the greater ability of the casein network in this breed to contract during coagulation and to expel whey from the curd, thus reducing the overall moisture content. This also confirms the fundamental role of caseins in the final outcome of the cheese-making, with single casein fractions differently linked to the water TS components of the curd (
      • Cipolat-Gotet C.
      • Cecchinato A.
      • Malacarne M.
      • Bittante G.
      • Summer A.
      Variations in milk protein fractions affect the efficiency of the cheese-making process.
      ).

      CONCLUSIONS

      In this study we directly quantified the effects of major milk components on %CY traits, in terms of fresh cheese, milk solids, and water retained in the curd, and of the most important indicators of udder health in milk. The large number and variability of individual samples, and direct measurements of %CY traits, allowed us to collect information on accuracies of prediction for application at the dairy goat population level. Knowledge about the relationships between UHI and efficiency of the cheese-making process could be used together with data provided by the standard composition. Overall, the results gave a much more detailed understanding of the mechanisms that determine cheese yield in goats. The different accuracy of the %CY predictive formulas within the 9-MilCA method leads us to speculate that the control of fresh curd, and especially moisture retention in the curd, is under multifactorial control, which must be considered to increase the reliability of the measure of this trait. Findings arising from the differences among breeds confirmed that the SCrV approach is more appropriate than CrV, in particular when different breeds are sampled, and to create within-breed formulas.

      ACKNOWLEDGMENTS

      This research was supported by the Regional Government of Sardinia (Cagliari, Italy; Progetto Strategico Sulcis; CUP J73C17000070007). The authors thank the Provincial Farmers Associations of Sardinia (Cagliari, Italy), the firms Società Agricola is Crabaxius (Fluminimaggiore, Italy), Azienda Agricola Murgia Antonello (Sant'Anna Arresi, Italy), Latteria Sociale Santadi (Santadi, Italy), Azienda Agricola F.lli Secci s.s. (Iglesias, Italy), and Agricola Allevatori Tallaroga, Soc. Coop. (Villamassargia, Italy) for their support in sample collection, and the Regional Farmers Association of Sardinia (Cagliari, Italy) for support in chemical milk analysis. The authors have not stated any conflicts of interest.