Predictive formulae for goat cheese yield based on milk composition |
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| Institution: | 1. E (Kika) de la Garza American Institute for Goat Research, Langston University, P.O. Box 1730, OK 73050, USA;2. Animal and Poultry Division, Desert Research Center, Matareya, Cairo, Egypt;3. Awassa College of Agriculture, Debub University, Awassa, Ethiopia;4. USDA/ARS, Southern Plains Area, Stillwater, OK 74075, USA;1. Department of Applied Biology, Faculty of Exact Sciences and Nature and Life Sciences, University of Larbi Tebessi - Tebessa, 12002, Tebessa, Algeria;2. Laboratoire de Nutrition et Technologie Alimentaire (LNTA), Equipe “TEPA”, INATAA. University of Constantine 1, 25000, Constantine, Algeria;3. Consorzio di Ricerca Lattiero Casearia (CoRFiLaC), 97100, Ragusa, Sicilia, Italy;4. Department of Forest Management, Higher National School of Forests, 40000, Khenchela, Algeria;5. Laboratory of Natural Resources and Management of Sensitive Environments ‘RNAMS’, University of Larbi Ben M''hidi, 04000, Oum-El-Bouaghi, Algeria;6. Laboratoire de Génie Agro-Alimentaire (GeniAAl), INATAA. University of Constantine 1, 25000, Constantine, Algeria;7. Faculty of Exact Sciences and Nature and Life Sciences, University of Larbi Ben M''hidi, 04000, Oum-El-Bouaghi, Algeria;8. Department of Central Inspectorate for Fraud Repression and Quality Protection of the Agri-food Products and Foodstuffs (ICQRF). Laboratory of Perugia, 06128, Perugia, Italy;1. Animal Source Food Technology Department, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11081 Belgrade, Serbia;2. Department of Food Safety and Quality Management, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11081 Belgrade, Serbia;1. Université de Toulouse, Ecole d’Ingénieurs de Purpan, INPT, Toulouse, France;2. Université de Pau et des Pays de l’Adour, E2S UPPA, CNRS, IMT Mines Ales, IPREM, Institut des Sciences Analytiques et de Physicochimie pour l''environnement et les Matériaux, UMR5254, Hélioparc, 2 Avenue Président Angot, 64053, Pau, Cedex 9, France |
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| Abstract: | Prediction of the yield and quality of different types of cheeses that could be produced from a given type and/or amount of goat milk is of great economic benefit to goat milk producers and goat cheese manufacturers. Bulk tank goat milk was used for manufacturing hard, semi-hard and soft cheeses (N = 25, 25 and 24, respectively) to develop predictive formulae of cheese yield based on milk composition. Fat, total solids, total protein and casein contents in milk and moisture-adjusted cheese yield were determined to establish relationships between milk composition and cheese yield. Soft, semi-hard and hard cheeses in this study had moisture contents of 66, 46 and 38%, respectively, which could be used as reference standards. In soft cheese, individual components of goat milk or a combination of two or three components predicted cheese yield with a reasonably high correlation coefficient (R2 = 0.73–0.81). However, correlation coefficients of predictions were lower for both semi-hard and hard cheeses. Overall, total solids of goat milk was the strongest indicator of yield in all three types of cheeses, followed by fat and total protein, while casein was not a good predictor for both semi-hard and hard cheeses. When compared with moisture-adjusted cheese yield, there was no difference (P > 0.05) in predicting yield of semi-hard and hard goat milk cheeses between the developed yield formulae in this study and a standard formula (the Van Slyke formula) commonly used for cow cheese. Future research will include further validation of the yield predictive formulae for hard and semi-hard cheeses of goat milk using larger data sets over several lactations, because of variation in relationships between milk components due to breed, stage of lactation, season, feeding regime, somatic cell count and differences in casein variants. |
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