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Molle A, Cipolat-Gotet C, Stocco G, Ferragina A, Berzaghi P, Summer A. The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese. J Dairy Sci 2024; 107:1967-1979. [PMID: 37863286 DOI: 10.3168/jds.2023-23827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/03/2023] [Indexed: 10/22/2023]
Abstract
The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.
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Affiliation(s)
- Arnaud Molle
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | | | - Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
| | - Alessandro Ferragina
- Food Quality and Sensory Science Department, Teagasc Food Research Centre, D15 KN3K, Ireland
| | - Paolo Berzaghi
- University of Padova, Department of Animal Medicine, Production and Health, Padova, Italy 35020
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
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Mariani E, Malacarne M, Cipolat-Gotet C, Cecchinato A, Bittante G, Summer A. Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows. Front Vet Sci 2022; 9:1012251. [PMID: 36311669 PMCID: PMC9606222 DOI: 10.3389/fvets.2022.1012251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/04/2022] Open
Abstract
The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.
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Affiliation(s)
- Elena Mariani
- Department of Veterinary Science, University of Parma, Parma, Italy
| | | | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Parma, Italy,*Correspondence: Claudio Cipolat-Gotet
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Legnaro, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Legnaro, Italy
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, Parma, Italy
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Stocco G, Dadousis C, Vacca GM, Pazzola M, Summer A, Dettori ML, Cipolat-Gotet C. Predictive formulas for different measures of cheese yield using milk composition from individual goat samples. J Dairy Sci 2022; 105:5610-5621. [PMID: 35570042 DOI: 10.3168/jds.2022-21848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/14/2022] [Indexed: 11/19/2022]
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.
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Affiliation(s)
- Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Christos Dadousis
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Giuseppe M Vacca
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy.
| | - Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Maria L Dettori
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
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D'Incecco P, Hogenboom JA, Rosi V, Cabassi G, Pellegrino L. Effects of microbial coagulants from Rhyzomucor miehei on composition, sensory and textural characteristics of long-ripened hard cheeses. Food Chem 2022; 370:131043. [PMID: 34509948 DOI: 10.1016/j.foodchem.2021.131043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/31/2021] [Accepted: 08/31/2021] [Indexed: 11/18/2022]
Abstract
The increasing use of rennet substitutes entails evaluating their performances on different types of cheese. The production of hard cheese using either microbial coagulants from Rhyzomucor miehei (MC) or calf rennet (CR) from different manufacturers was investigated in parallel cheese makings at three industrial dairies. Cheeses were analysed after 9, 12, 16 and 18 months of ripening. Minor differences in cheese composition were found between treatments, principally related to fat content. Cheeses produced with one out of the three MC showed slower primary proteolysis on both αs1- and αs0-casein, compared to the corresponding CR cheeses, indicating a different activity of this coagulant. The same cheeses also had significantly different sensory profiles at 9 months of ripening. Treatments did not differ in free amino acid composition nor in rheological parameters, regardless of ripening period. The long ripening of hard cheeses thus smooths possible differences attributable to MC.
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Affiliation(s)
- Paolo D'Incecco
- Department of Food, Environmental and Nutritional Sciences, University of Milano, Milan, Italy.
| | - Johannes A Hogenboom
- Department of Food, Environmental and Nutritional Sciences, University of Milano, Milan, Italy
| | - Veronica Rosi
- Department of Food, Environmental and Nutritional Sciences, University of Milano, Milan, Italy
| | - Giovanni Cabassi
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria, CREA-ZA Via A, Lombardo 11, Lodi 26900, Italy
| | - Luisa Pellegrino
- Department of Food, Environmental and Nutritional Sciences, University of Milano, Milan, Italy
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Soltani M, Saremnezhad S, Faraji A, Hayaloglu A. Perspectives and recent innovations on white cheese produced by conventional methods or ultrafiltration technique. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2021.105232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Smith JR, Carr AJ, Golding M, Reid D. Mozzarella Cheese – A Review of the Structural Development During Processing. FOOD BIOPHYS 2017. [DOI: 10.1007/s11483-017-9511-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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O'Brien E, Mills S, Dobson A, Serrano LM, Hannon J, Ryan SP, Kilcawley KN, Brandsma JB, Meijer WC, Hill C, Ross RP. Contribution of the novel sulfur-producing adjunct Lactobacillus nodensis to flavor development in Gouda cheese. J Dairy Sci 2017; 100:4322-4334. [DOI: 10.3168/jds.2016-11726] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 01/23/2017] [Indexed: 11/19/2022]
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Vélez MA, Bergamini CV, Ramonda MB, Candioti MC, Hynes ER, Perotti MC. Influence of cheese making technologies on plasmin and coagulant associated proteolysis. Lebensm Wiss Technol 2015. [DOI: 10.1016/j.lwt.2015.05.053] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Suzzi G, Sacchetti G, Patrignani F, Corsetti A, Tofalo R, Schirone M, Fasoli G, Gardini F, Perpetuini G, Lanciotti R. Influence of pig rennet on fatty acid composition, volatile molecule profile, texture and sensory properties of Pecorino di Farindola cheese. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2015; 95:2252-2263. [PMID: 25271150 DOI: 10.1002/jsfa.6944] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 09/23/2014] [Accepted: 09/26/2014] [Indexed: 06/03/2023]
Abstract
BACKGROUND Pig rennet is traditionally used in Pecorino di Farindola cheese. In this study, different Pecorino cheeses obtained using calf, kid and pig rennets were compared in terms of fatty acids, volatile molecule profile, texture and sensory properties during ripening. RESULTS The rennet type influenced the fatty acid composition of cheeses, though palmitic, myristic and oleic acids were always predominant. The analysis of volatiles by SPME-GC/MS showed that Pecorino from calf rennet, at the end of ripening, was the least 'evolved' in terms of volatile profile. SPME-GC/MS analysis revealed that cheeses from calf rennet showed the slowest accumulation of free fatty acids over ripening time. Volatile data permitted the differentiation of cheese samples ripened from 30 to 180 days according to the rennet used. Texture analysis differentiated cheeses made with pig and calf rennet from those made with kid rennet, which were less hard and more elastic than the former. Also sensory analysis differentiated cheese samples on the basis of rennet type, and cheeses made with pig rennet showed the lowest elasticity, bitter taste and fruity and hay flavour intensities. CONCLUSION Pig rennet is fundamental to determine the quality parameters of Pecorino di Farindola cheese and could be used to impart peculiar quality features to ewe's milk cheeses.
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Affiliation(s)
- Giovanna Suzzi
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Giampiero Sacchetti
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Francesca Patrignani
- Department of Agricultural and Food Sciences, University of Bologna, P.zza Goidanich 60, I-47521, Cesena (FC), Italy
| | - Aldo Corsetti
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Rosanna Tofalo
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Maria Schirone
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Giuseppe Fasoli
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Fausto Gardini
- Department of Agricultural and Food Sciences, University of Bologna, P.zza Goidanich 60, I-47521, Cesena (FC), Italy
| | - Giorgia Perpetuini
- Faculty of Bioscience and Agro-Food and Environmental Technology, University of Teramo, Via Carlo R. Lerici 1, I-64023, Mosciano Sant'Angelo (TE), Italy
| | - Rosalba Lanciotti
- Department of Agricultural and Food Sciences, University of Bologna, P.zza Goidanich 60, I-47521, Cesena (FC), Italy
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Cipolat-Gotet C, Cecchinato A, De Marchi M, Bittante G. Factors affecting variation of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process. J Dairy Sci 2013; 96:7952-65. [DOI: 10.3168/jds.2012-6516] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 07/29/2013] [Indexed: 11/19/2022]
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Pretto D, Kaart T, Vallas M, Jõudu I, Henno M, Ancilotto L, Cassandro M, Pärna E. Relationships between milk coagulation property traits analyzed with different methodologies. J Dairy Sci 2011; 94:4336-46. [DOI: 10.3168/jds.2011-4267] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2011] [Accepted: 04/29/2011] [Indexed: 11/19/2022]
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BENKERROUM NOREDDINE, DEHHAOUI MOHAMMED, EL FAYQ ABDELMALEK, TLAIHA RACHIDA. The effect of concentration of chymosin on the yield and sensory properties of camel cheese and on its microbiological quality. INT J DAIRY TECHNOL 2011. [DOI: 10.1111/j.1471-0307.2010.00662.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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