The Experts below are selected from a list of 684 Experts worldwide ranked by ideXlab platform
Giovanni Bittante - One of the best experts on this subject based on the ideXlab platform.
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Comparison 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
Journal of Dairy Science, 2014Co-Authors: Giovanni Bittante, A. Ferragina, C. Cipolat-gotet, Alessio CecchinatoAbstract:Cheese yield is an important technological trait in the dairy industry.\nThe aim of this study was to infer the genetic parameters of some cheese\nyield-related traits predicted using Fourier-transform infrared (FTIR)\nspectral analysis and compare the results with those obtained using an\nindividual model cheese-producing procedure. A total of 1,264 model\ncheeses were produced using 1,500-mL Milk samples collected from\nindividual Brown Swiss cows, and individual measurements were taken for\n10 traits: 3 cheese yield traits (fresh curd, curd total solids, and\ncurd water as a percent of the weight of the processed Milk), 4 Milk\nnutrient recovery traits (fat, protein, total solids, and energy of the\ncurd as a percent of the same nutrient in the processed Milk), and 3\ndaily cheese production traits per cow (fresh curd, total solids, and\nwater weight of the curd). Each Unprocessed Milk sample was analyzed\nusing a MilkoScan FT6000 (Foss, Hillerod, Denmark) over the spectral\nrange, from 5,000 to 900 wavenumber x cm(-1). The FTIR spectrum-based\nprediction models for the previously mentioned traits were developed\nusing modified partial least-square regression. Cross-validation of the\nwhole data set yielded coefficients of determination between the\npredicted and measured values in cross-validation of 0.65 to 0.95 for\nall traits, except for the recovery of fat (0.41). A 3-fold external\nvalidation was also used, in which the available data were partitioned\ninto 2 subsets: a training set (one-third of the herds) and a testing\nset (two-thirds). The training set was used to develop calibration\nequations, whereas the testing subsets were used for external validation\nof the calibration equations and to estimate the heritabilities and\ngenetic correlations of the measured and FTIR-predicted phenotypes. The\ncoefficients of determination between the predicted and measured values\nin cross-validation results obtained from the training sets were very\nsimilar to those obtained from the whole data set, but the coefficient\nof determination of validation values for the external validation sets\nwere much lower for all traits (0.30 to 0.73), and particularly for fat\nrecovery (0.05 to 0.18), for the training sets compared with the full\ndata set. For each testing subset, the (co)variance components for the\nmeasured and FTIR-predicted phenotypes were estimated using bivariate\nBayesian analyses and linear models. The intraherd heritabilities for\nthe predicted traits obtained from our internal cross-validation using\nthe whole data set ranged from 0.085 for daily yield of curd solids to\n0.576 for protein recovery, and were similar to those obtained from the\nmeasured traits (0.079 to 0.586, respectively). The heritabilities\nestimated from the testing data set used for external validation were\nmore variable but similar (on average) to the corresponding values\nobtained from the whole data set. Moreover, the genetic correlations\nbetween the predicted and measured traits were high in general (0.791 to\n0.996), and they were always higher than the corresponding phenotypic\ncorrelations (0.383 to 0.995), especially for the external validation\nsubset. In conclusion, we herein report that application of the\ncross-validation technique to the whole data set tended to overestimate\nthe predictive ability of FTIR spectra, give more precise phenotypic\npredictions than the calibrations obtained using smaller data sets, and\nyield genetic correlations similar to those obtained from the measured\ntraits.\nCollectively, our findings indicate that FTIR predictions have the\npotential to be used as indicator traits for the rapid and inexpensive\nselection of dairy populations for improvement of cheese yield, Milk\nnutrient recovery in curd, and daily cheese production per cow.
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The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from Unprocessed bovine Milk samples
Journal of Dairy Science, 2013Co-Authors: A. Ferragina, C. Cipolat-gotet, Alessio Cecchinato, Giovanni BittanteAbstract:Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh Unprocessed Milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL Milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed Milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the Milk. All individual Milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard Milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and Milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and Milk nutrient recovery, potentially allowing these traits to be enhanced through selection.
Alessio Cecchinato - One of the best experts on this subject based on the ideXlab platform.
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Comparison 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
Journal of Dairy Science, 2014Co-Authors: Giovanni Bittante, A. Ferragina, C. Cipolat-gotet, Alessio CecchinatoAbstract:Cheese yield is an important technological trait in the dairy industry.\nThe aim of this study was to infer the genetic parameters of some cheese\nyield-related traits predicted using Fourier-transform infrared (FTIR)\nspectral analysis and compare the results with those obtained using an\nindividual model cheese-producing procedure. A total of 1,264 model\ncheeses were produced using 1,500-mL Milk samples collected from\nindividual Brown Swiss cows, and individual measurements were taken for\n10 traits: 3 cheese yield traits (fresh curd, curd total solids, and\ncurd water as a percent of the weight of the processed Milk), 4 Milk\nnutrient recovery traits (fat, protein, total solids, and energy of the\ncurd as a percent of the same nutrient in the processed Milk), and 3\ndaily cheese production traits per cow (fresh curd, total solids, and\nwater weight of the curd). Each Unprocessed Milk sample was analyzed\nusing a MilkoScan FT6000 (Foss, Hillerod, Denmark) over the spectral\nrange, from 5,000 to 900 wavenumber x cm(-1). The FTIR spectrum-based\nprediction models for the previously mentioned traits were developed\nusing modified partial least-square regression. Cross-validation of the\nwhole data set yielded coefficients of determination between the\npredicted and measured values in cross-validation of 0.65 to 0.95 for\nall traits, except for the recovery of fat (0.41). A 3-fold external\nvalidation was also used, in which the available data were partitioned\ninto 2 subsets: a training set (one-third of the herds) and a testing\nset (two-thirds). The training set was used to develop calibration\nequations, whereas the testing subsets were used for external validation\nof the calibration equations and to estimate the heritabilities and\ngenetic correlations of the measured and FTIR-predicted phenotypes. The\ncoefficients of determination between the predicted and measured values\nin cross-validation results obtained from the training sets were very\nsimilar to those obtained from the whole data set, but the coefficient\nof determination of validation values for the external validation sets\nwere much lower for all traits (0.30 to 0.73), and particularly for fat\nrecovery (0.05 to 0.18), for the training sets compared with the full\ndata set. For each testing subset, the (co)variance components for the\nmeasured and FTIR-predicted phenotypes were estimated using bivariate\nBayesian analyses and linear models. The intraherd heritabilities for\nthe predicted traits obtained from our internal cross-validation using\nthe whole data set ranged from 0.085 for daily yield of curd solids to\n0.576 for protein recovery, and were similar to those obtained from the\nmeasured traits (0.079 to 0.586, respectively). The heritabilities\nestimated from the testing data set used for external validation were\nmore variable but similar (on average) to the corresponding values\nobtained from the whole data set. Moreover, the genetic correlations\nbetween the predicted and measured traits were high in general (0.791 to\n0.996), and they were always higher than the corresponding phenotypic\ncorrelations (0.383 to 0.995), especially for the external validation\nsubset. In conclusion, we herein report that application of the\ncross-validation technique to the whole data set tended to overestimate\nthe predictive ability of FTIR spectra, give more precise phenotypic\npredictions than the calibrations obtained using smaller data sets, and\nyield genetic correlations similar to those obtained from the measured\ntraits.\nCollectively, our findings indicate that FTIR predictions have the\npotential to be used as indicator traits for the rapid and inexpensive\nselection of dairy populations for improvement of cheese yield, Milk\nnutrient recovery in curd, and daily cheese production per cow.
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The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from Unprocessed bovine Milk samples
Journal of Dairy Science, 2013Co-Authors: A. Ferragina, C. Cipolat-gotet, Alessio Cecchinato, Giovanni BittanteAbstract:Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh Unprocessed Milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL Milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed Milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the Milk. All individual Milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard Milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and Milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and Milk nutrient recovery, potentially allowing these traits to be enhanced through selection.
A. Ferragina - One of the best experts on this subject based on the ideXlab platform.
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Comparison 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
Journal of Dairy Science, 2014Co-Authors: Giovanni Bittante, A. Ferragina, C. Cipolat-gotet, Alessio CecchinatoAbstract:Cheese yield is an important technological trait in the dairy industry.\nThe aim of this study was to infer the genetic parameters of some cheese\nyield-related traits predicted using Fourier-transform infrared (FTIR)\nspectral analysis and compare the results with those obtained using an\nindividual model cheese-producing procedure. A total of 1,264 model\ncheeses were produced using 1,500-mL Milk samples collected from\nindividual Brown Swiss cows, and individual measurements were taken for\n10 traits: 3 cheese yield traits (fresh curd, curd total solids, and\ncurd water as a percent of the weight of the processed Milk), 4 Milk\nnutrient recovery traits (fat, protein, total solids, and energy of the\ncurd as a percent of the same nutrient in the processed Milk), and 3\ndaily cheese production traits per cow (fresh curd, total solids, and\nwater weight of the curd). Each Unprocessed Milk sample was analyzed\nusing a MilkoScan FT6000 (Foss, Hillerod, Denmark) over the spectral\nrange, from 5,000 to 900 wavenumber x cm(-1). The FTIR spectrum-based\nprediction models for the previously mentioned traits were developed\nusing modified partial least-square regression. Cross-validation of the\nwhole data set yielded coefficients of determination between the\npredicted and measured values in cross-validation of 0.65 to 0.95 for\nall traits, except for the recovery of fat (0.41). A 3-fold external\nvalidation was also used, in which the available data were partitioned\ninto 2 subsets: a training set (one-third of the herds) and a testing\nset (two-thirds). The training set was used to develop calibration\nequations, whereas the testing subsets were used for external validation\nof the calibration equations and to estimate the heritabilities and\ngenetic correlations of the measured and FTIR-predicted phenotypes. The\ncoefficients of determination between the predicted and measured values\nin cross-validation results obtained from the training sets were very\nsimilar to those obtained from the whole data set, but the coefficient\nof determination of validation values for the external validation sets\nwere much lower for all traits (0.30 to 0.73), and particularly for fat\nrecovery (0.05 to 0.18), for the training sets compared with the full\ndata set. For each testing subset, the (co)variance components for the\nmeasured and FTIR-predicted phenotypes were estimated using bivariate\nBayesian analyses and linear models. The intraherd heritabilities for\nthe predicted traits obtained from our internal cross-validation using\nthe whole data set ranged from 0.085 for daily yield of curd solids to\n0.576 for protein recovery, and were similar to those obtained from the\nmeasured traits (0.079 to 0.586, respectively). The heritabilities\nestimated from the testing data set used for external validation were\nmore variable but similar (on average) to the corresponding values\nobtained from the whole data set. Moreover, the genetic correlations\nbetween the predicted and measured traits were high in general (0.791 to\n0.996), and they were always higher than the corresponding phenotypic\ncorrelations (0.383 to 0.995), especially for the external validation\nsubset. In conclusion, we herein report that application of the\ncross-validation technique to the whole data set tended to overestimate\nthe predictive ability of FTIR spectra, give more precise phenotypic\npredictions than the calibrations obtained using smaller data sets, and\nyield genetic correlations similar to those obtained from the measured\ntraits.\nCollectively, our findings indicate that FTIR predictions have the\npotential to be used as indicator traits for the rapid and inexpensive\nselection of dairy populations for improvement of cheese yield, Milk\nnutrient recovery in curd, and daily cheese production per cow.
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The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from Unprocessed bovine Milk samples
Journal of Dairy Science, 2013Co-Authors: A. Ferragina, C. Cipolat-gotet, Alessio Cecchinato, Giovanni BittanteAbstract:Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh Unprocessed Milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL Milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed Milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the Milk. All individual Milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard Milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and Milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and Milk nutrient recovery, potentially allowing these traits to be enhanced through selection.
Carlos Augusto Fernandes De Oliveira - One of the best experts on this subject based on the ideXlab platform.
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characterization of staphylococcus aureus isolates in Milk and the Milking environment from small scale dairy farms of sao paulo brazil using pulsed field gel electrophoresis
Journal of Dairy Science, 2012Co-Authors: C H Camargo, Juliano Leonel Goncalves, Adriano G Cruz, B T Sartori, M B Machado, Carlos Augusto Fernandes De OliveiraAbstract:Abstract This research aimed to evaluate the occurrence of Staphylococcus aureus isolates in Milk and in the Milking environment of 10 small-scale farms ( Staph. aureus strains were isolated from 849 analyzed samples (6.6%): 12 (5.5%) from Milk samples of individual cows, 26 (21.7%) from samples of bulk tank Milk, 14 (3.6%) from samples collected from equipment and utensils, and 4 (3.3%) from samples from Milkers' hands. Pulsed-field gel electrophoresis typing of the 56 Staph. aureus isolates by Sma I restriction enzyme resulted in 31 profiles (pulsotypes) arranged in 12 major clusters. Results of this study indicate a low incidence, but wide distribution of Staph. aureus strains isolated from raw Milk collected from individual cows and surfaces of Milkers' hands and Milking equipment in the small-scale dairy farms evaluated. However, the high percentage of bulk Milk samples found with Staph. aureus is of public health concern because raw, Unprocessed Milk is regularly consumed by the Brazilian population.
C. Cipolat-gotet - One of the best experts on this subject based on the ideXlab platform.
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Comparison 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
Journal of Dairy Science, 2014Co-Authors: Giovanni Bittante, A. Ferragina, C. Cipolat-gotet, Alessio CecchinatoAbstract:Cheese yield is an important technological trait in the dairy industry.\nThe aim of this study was to infer the genetic parameters of some cheese\nyield-related traits predicted using Fourier-transform infrared (FTIR)\nspectral analysis and compare the results with those obtained using an\nindividual model cheese-producing procedure. A total of 1,264 model\ncheeses were produced using 1,500-mL Milk samples collected from\nindividual Brown Swiss cows, and individual measurements were taken for\n10 traits: 3 cheese yield traits (fresh curd, curd total solids, and\ncurd water as a percent of the weight of the processed Milk), 4 Milk\nnutrient recovery traits (fat, protein, total solids, and energy of the\ncurd as a percent of the same nutrient in the processed Milk), and 3\ndaily cheese production traits per cow (fresh curd, total solids, and\nwater weight of the curd). Each Unprocessed Milk sample was analyzed\nusing a MilkoScan FT6000 (Foss, Hillerod, Denmark) over the spectral\nrange, from 5,000 to 900 wavenumber x cm(-1). The FTIR spectrum-based\nprediction models for the previously mentioned traits were developed\nusing modified partial least-square regression. Cross-validation of the\nwhole data set yielded coefficients of determination between the\npredicted and measured values in cross-validation of 0.65 to 0.95 for\nall traits, except for the recovery of fat (0.41). A 3-fold external\nvalidation was also used, in which the available data were partitioned\ninto 2 subsets: a training set (one-third of the herds) and a testing\nset (two-thirds). The training set was used to develop calibration\nequations, whereas the testing subsets were used for external validation\nof the calibration equations and to estimate the heritabilities and\ngenetic correlations of the measured and FTIR-predicted phenotypes. The\ncoefficients of determination between the predicted and measured values\nin cross-validation results obtained from the training sets were very\nsimilar to those obtained from the whole data set, but the coefficient\nof determination of validation values for the external validation sets\nwere much lower for all traits (0.30 to 0.73), and particularly for fat\nrecovery (0.05 to 0.18), for the training sets compared with the full\ndata set. For each testing subset, the (co)variance components for the\nmeasured and FTIR-predicted phenotypes were estimated using bivariate\nBayesian analyses and linear models. The intraherd heritabilities for\nthe predicted traits obtained from our internal cross-validation using\nthe whole data set ranged from 0.085 for daily yield of curd solids to\n0.576 for protein recovery, and were similar to those obtained from the\nmeasured traits (0.079 to 0.586, respectively). The heritabilities\nestimated from the testing data set used for external validation were\nmore variable but similar (on average) to the corresponding values\nobtained from the whole data set. Moreover, the genetic correlations\nbetween the predicted and measured traits were high in general (0.791 to\n0.996), and they were always higher than the corresponding phenotypic\ncorrelations (0.383 to 0.995), especially for the external validation\nsubset. In conclusion, we herein report that application of the\ncross-validation technique to the whole data set tended to overestimate\nthe predictive ability of FTIR spectra, give more precise phenotypic\npredictions than the calibrations obtained using smaller data sets, and\nyield genetic correlations similar to those obtained from the measured\ntraits.\nCollectively, our findings indicate that FTIR predictions have the\npotential to be used as indicator traits for the rapid and inexpensive\nselection of dairy populations for improvement of cheese yield, Milk\nnutrient recovery in curd, and daily cheese production per cow.
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The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from Unprocessed bovine Milk samples
Journal of Dairy Science, 2013Co-Authors: A. Ferragina, C. Cipolat-gotet, Alessio Cecchinato, Giovanni BittanteAbstract:Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh Unprocessed Milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL Milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed Milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the Milk. All individual Milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard Milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and Milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and Milk nutrient recovery, potentially allowing these traits to be enhanced through selection.
