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Guerra A, Costa A, De Marchi M, Righi F, Simoni M, Manuelian CL. The effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits. J Dairy Sci 2024; 107:2143-2155. [PMID: 37977439 DOI: 10.3168/jds.2023-23989] [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: 07/18/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023]
Abstract
Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.
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Affiliation(s)
- Alberto Guerra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - Angela Costa
- Department of Veterinary Medical Sciences, University of Bologna, 40064 Ozzano dell'Emilia (BO), Italy
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - Federico Righi
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Marica Simoni
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
| | - Carmen L Manuelian
- Group of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Spain
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Lončar B, Pezo L, Iličić M, Kanurić K, Vukić D, Degenek J, Vukić V. Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics. Foods 2024; 13:548. [PMID: 38397525 PMCID: PMC10887540 DOI: 10.3390/foods13040548] [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: 01/19/2024] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.
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Affiliation(s)
- Biljana Lončar
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
| | - Lato Pezo
- Institute of General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, Serbia;
| | - Mirela Iličić
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
| | - Katarina Kanurić
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
| | - Dajana Vukić
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
| | - Jovana Degenek
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
| | - Vladimir Vukić
- Faculty of Technology Novi Sad, University of Novi Sad, Bulevar Cara Lazara 1, 21000 Novi Sad, Serbia; (M.I.); (K.K.); (D.V.); (J.D.); (V.V.)
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Bekele H, Yohannes W, Megersa N. A Highly Selective Analytical Method Based on Salt-Assisted Liquid-Liquid Extraction for Trace-Level Enrichment of Multiclass Pesticide Residues in Cow Milk for Quantitative Liquid Chromatographic Analysis. Int J Anal Chem 2023; 2023:1754956. [PMID: 37810912 PMCID: PMC10558272 DOI: 10.1155/2023/1754956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/09/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
In this study, a simple, inexpensive, selective, and fast salting-out assisted liquid-liquid extraction (SALLE) technique coupled with high-pressure liquid chromatography-diode array detection (HPLC-DAD) was developed for the extraction, preconcentration, and analysis of trace level seven multiclass pesticide residues in pasteurized and raw cow milk samples. The significant factors that affect the extent to which the target analytes are extracted, such as the type of extraction solvent and its volume, the type and concentration of salting-out salts, the pH of the solution, and the extraction time, have been investigated. Under optimum conditions, the correlation coefficient (r2) was obtained within a range of 0.9982-0.9997 for a broad linear range concentration of 2-1500 ng·mL-1. Reliable sensitivity was achieved with limits of detection (LODs) and limits of quantification (LOQs) ranging from 0.58-2.56 ng·mL-1 and 1.95-8.51 ng·mL-1, respectively. While precision with interday and intraday in terms of relative standard deviations (RSDs) was observed in the range of 1.97 - 7.88% and 4.52 - 8.04%, respectively. The results of the precision studies reveal that good repeatability and reproducibility (RSDs <9) were achieved, thus showing a low variability extraction of the developed method. Finally, the proposed and validated approach was effectively used to extract and determine pesticide residues in real milk matrices; however, the target analytes were not detected in all samples.
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Affiliation(s)
- Habtamu Bekele
- Department of Chemistry, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
| | - Weldegebriel Yohannes
- Department of Chemistry, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
| | - Negussie Megersa
- Department of Chemistry, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
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Vigolo V, Visentin E, Ballancin E, Lopez-Villalobos N, Penasa M, De Marchi M. β-Casein A1 and A2: Effects of polymorphism on the cheese-making process. J Dairy Sci 2023; 106:5276-5287. [PMID: 37291039 DOI: 10.3168/jds.2022-23072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/13/2023] [Indexed: 06/10/2023]
Abstract
Of late, "A2 milk" has gained prominence in the dairy sector due to its potential implications in human health. Consequently, the frequency of A2 homozygous animals has considerably increased in many countries. To elucidate the potential implications that beta casein (β-CN) A1 and A2 may have on cheese-making traits, it is fundamental to investigate the relationships between the genetic polymorphisms and cheese-making traits at the dairy plant level. Thus, the aim of the present study was to evaluate the relevance of the β-CN A1/A2 polymorphism on detailed protein profile and cheese-making process in bulk milk. Based on the β-CN genotype of individual cows, 5 milk pools diverging for presence of the 2 β-CN variants were obtained: (1) 100% A1; (2) 75% A1 and 25% A2; (3) 50% A1 and 50% A2; (4) 25% A1 and 75% A2; and (5) 100% A2. For each cheese-making day (n = 6), 25 L of milk (divided into 5 pools, 5 L each) were processed, for a total of 30 cheese-making processes. Cheese yield, curd nutrient recovery, whey composition, and cheese composition were assessed. For every cheese-making process, detailed milk protein fractions were determined through reversed-phase HPLC. Data were analyzed by fitting a mixed model, which included the fixed effects of the 5 different pools, the protein and fat content as a covariate, and the random effect of the cheese-making sessions. Results showed that the percentage of κ-CN significantly decreased up to 2% when the proportion of β-CN A2 in the pool was ≥25%. An increase in the relative content of β-CN A2 (≥50% of total milk processed) was also associated with a significantly lower cheese yield both 1 and 48 h after cheese production, whereas no effects were observed after 7 d of ripening. Concordantly, recovery of nutrients reflected a more efficient process when the inclusion of β-CN A2 was ≤75%. Finally, no differences in the final cheese composition obtained by the different β-CN pools were observed.
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Affiliation(s)
- Vania Vigolo
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Elena Visentin
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - Eva Ballancin
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - Nicolas Lopez-Villalobos
- School of Agriculture and Environment, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand
| | - Mauro Penasa
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
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Zacometti C, Tata A, Massaro A, Riuzzi G, Bragolusi M, Cozzi G, Piro R, Khazzar S, Gerardi G, Gottardo F, Segato S. Seasonal Variation in Raw Milk VOC Profile within Intensive Feeding Systems. Foods 2023; 12:foods12091871. [PMID: 37174409 PMCID: PMC10178752 DOI: 10.3390/foods12091871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
The study aimed to assess the seasonal variation in raw milk volatile organic compounds (VOCs) from three indoor feeding systems based on maize silage (n = 31), silages/hay (n = 19) or hay (n = 16). After headspace solid-phase microextraction (HS-SPME), VOC profiles were determined by gas chromatography (GC). Chemical and VOC (log10 transformations of the peak areas) data were submitted to a two-way ANOVA to assess the feeding system (FS) and season (S) effects; an interactive principal component analysis (iPCA) was also performed. The interaction FS × S was never significant. The FS showed the highest (p < 0.05) protein and casein content for hay-milk samples, while it did not affect any VOCs. Winter milk had higher (p < 0.05) proportions of protein, casein, fat and some carboxylic acids, while summer milk was higher (p < 0.05) in urea and 2-pentanol and methyl aldehydes. The iPCA confirmed a seasonal spatial separation. Carboxylic acids might generate from incomplete esterification in the mammary gland and/or milk lipolytic activity, while aldehydes seemed to be correlated with endogenous lipid or amino acid oxidation and/or feed transfer. The outcomes suggested that VOCs could be an operative support to trace raw milk for further mild processing.
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Affiliation(s)
- Carmela Zacometti
- Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy
| | - Alessandra Tata
- Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy
| | - Andrea Massaro
- Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy
| | - Giorgia Riuzzi
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
| | - Marco Bragolusi
- Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy
| | - Giulio Cozzi
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
| | - Roberto Piro
- Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, 36100 Vicenza, Italy
| | - Sara Khazzar
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
| | - Gabriele Gerardi
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
| | - Flaviana Gottardo
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
| | - Severino Segato
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
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6
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Butovskaya E, Gambi L, Giovanetti A, Fedrizzi G. Screening of antibiotic residues in raw bovine milk in Lombardy, Italy: Microbial growth inhibition assay and LC-HRMS technique integration for an accurate monitoring. Heliyon 2023; 9:e15395. [PMID: 37123980 PMCID: PMC10130878 DOI: 10.1016/j.heliyon.2023.e15395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 05/02/2023] Open
Abstract
Antibiotic residues in food of animal origin is a great concern for public health worldwide in terms of antibiotic resistance development, potential allergic reactions and disruption of intestinal flora equilibrium. In this study the presence of antibiotic residues in raw bovine milk samples collected from farms located in Lombardy region in Italy from 2018 to 2022 was assessed in the context of the national milk quality payment system. Samples were screened with microbiological growth inhibition test Delvotest ® SP NT and a very low positivity rate ranging from 0.1% to 0.07% over the four years was determined. A total of 79 positive samples were further analysed by LC-HRMS screening technique to confirm positivity and detect the specific antibiotic compound contaminating the sample. The β-lactam antibiotics resulted to be the most frequently detected, with the penicillin G being the most abundant compound. The data suggested that low levels of antibiotic contamination are consistently maintained over the last four years and the integration of the techniques used in this study is a valuable tool for a deep and precise monitoring of antibiotic residues in milk.
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Affiliation(s)
- Elena Butovskaya
- Food and Feed Chemistry Department, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna “Bruno Ubertini” (IZSLER), Via A. Bianchi 9, 25124, Brescia, Italy
- Corresponding author.
| | - Lorenzo Gambi
- Produzione Primaria” Department, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna “Bruno Ubertini” (IZSLER), Via A. Bianchi 9, 25124, Brescia, Italy
| | - Alice Giovanetti
- Food and Feed Chemistry Department, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna “Bruno Ubertini” (IZSLER), Via A. Bianchi 9, 25124, Brescia, Italy
| | - Giorgio Fedrizzi
- Food Safety Department, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna “Bruno Ubertini” (IZSLER), Via A. Bianchi 9, 25124, Brescia, Italy
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Vigolo V, Niero G, Penasa M, De Marchi M. Effects of preservative, storage time, and temperature of analysis on detailed milk protein composition determined by reversed-phase high-performance liquid chromatography. J Dairy Sci 2022; 105:7917-7925. [DOI: 10.3168/jds.2022-22069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/02/2022] [Indexed: 11/19/2022]
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Manuelian C, Vigolo V, Burbi S, Righi F, Simoni M, De Marchi M. Detailed comparison between organic and conventional milk from Holstein-Friesian dairy herds in Italy. J Dairy Sci 2022; 105:5561-5572. [DOI: 10.3168/jds.2021-21465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/10/2022] [Indexed: 11/19/2022]
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9
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Vigolo V, Franzoi M, Penasa M, De Marchi M. β-Casein variants differently affect bulk milk mineral content, protein composition, and technological traits. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2021.105221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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10
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Manuelian CL, Vigolo V, Righi F, Simoni M, Burbi S, De Marchi M. MIR and Vis/NIR spectroscopy cannot authenticate organic bulk milk. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.1954559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Carmen L. Manuelian
- Dipartimento di Agronomia, Animali, Alimenti, Risorse Naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Vania Vigolo
- Dipartimento di Agronomia, Animali, Alimenti, Risorse Naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Federico Righi
- Dipartimento di Scienze Medico-Veterinarie, University of Parma, Parma, Italy
| | - Marica Simoni
- Dipartimento di Scienze Medico-Veterinarie, University of Parma, Parma, Italy
| | - Sara Burbi
- Centre for Agroecology, Water and Resilience, Coventry University, Ryton-on-Dunsmore, UK
| | - Massimo De Marchi
- Dipartimento di Agronomia, Animali, Alimenti, Risorse Naturali e Ambiente, University of Padova, Legnaro, Italy
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Milk infrared spectra from multiple instruments improve performance of prediction models. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Dadousis C, Cipolat-Gotet C, Stocco G, Ferragina A, Dettori ML, Pazzola M, do Nascimento Rangel AH, Vacca GM. Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties. J Dairy Sci 2021; 104:3927-3935. [PMID: 33589253 DOI: 10.3168/jds.2020-19587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/13/2020] [Indexed: 11/19/2022]
Abstract
Driven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCTeq), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV80). To assess model performance, coefficient of determination (R2VAL) and the root mean squared error of validation were recorded. The R2VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCTeq, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV80, the maximum R2VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCTeq (6%). Further investigation evidenced important variability among farms, with R2VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats.
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Affiliation(s)
- Christos Dadousis
- 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
| | - Maria L Dettori
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | | | - Giuseppe M Vacca
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
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Gutiérrez-Reinoso MA, Aponte PM, Cabezas J, Rodriguez-Alvarez L, Garcia-Herreros M. Genomic Evaluation of Primiparous High-Producing Dairy Cows: Inbreeding Effects on Genotypic and Phenotypic Production-Reproductive Traits. Animals (Basel) 2020; 10:ani10091704. [PMID: 32967074 PMCID: PMC7552765 DOI: 10.3390/ani10091704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Improving the genomic prediction methodologies in high-producing dairy cattle is a key factor for the selection of suitable individuals to ensure better productivity. However, the most advanced prediction tools based on genotyping show ~75% reliability. Nowadays, the incorporation of new indices to genomic prediction methods, such as the Inbreeding Index (II), can significantly facilitate the selection of reliable production and reproductive traits for progeny selection. Thus, the objective of this study was to determine the impact of II (low: LI and high: HI), based on genomic analysis, and its effect on production and reproductive phenotypic traits in high-producing primiparous dairy cows. Individuals with II between ≥2.5 and ≤5.0 have shown up to a two-fold increase in negative correlations comparing LI versus HI genomic production and reproductive parameters, severely affecting important traits such as Milk Production at 305 d, Protein Production at 305 d, Fertility Index, and Daughter Pregnancy Rate. Therefore, high-producing dairy cows face an increased risk of negative II-derived effects in their selection programs, particularly at II ≥ 2.5. Abstract The main objective of this study was to analyze the effects of the inbreeding degree in high-producing primiparous dairy cows genotypically and phenotypically evaluated and its impacts on production and reproductive parameters. Eighty Holstein–Friesian primiparous cows (age: ~26 months; ~450 kg body weight) were previously genomically analyzed to determine the Inbreeding Index (II) and were divided into two groups: low inbreeding group (LI: <2.5; n = 40) and high inbreeding group (HI: ≥2.5 and ≤5.0; n = 40). Genomic determinations of production and reproductive parameters (14 in total), together with analyses of production (12) and reproductive (11) phenotypic parameters (23 in total) were carried out. Statistically significant differences were obtained between groups concerning the genomic parameters of Milk Production at 305 d and Protein Production at 305 d and the reproductive parameter Daughter Calving Ease, the first two being higher in cows of the HI group and the third lower in the LI group (p < 0.05). For the production phenotypic parameters, statistically significant differences were observed between both groups in the Total Fat, Total Protein, and Urea parameters, the first two being higher in the LI group (p < 0.05). Also, significant differences were observed in several reproductive phenotypic parameters, such as Number of Services per Conception, Calving to Conception Interval, Days Open Post Service, and Current Inter-Partum Period, all of which negatively influenced the HI group (p < 0.05). In addition, correlation analyses were performed between production and reproductive genomic parameters separately and in each consanguinity group. The results showed multiple positive and negative correlations between the production and reproductive parameters independently of the group analyzed, being these correlations more remarkable for the reproductive parameters in the LI group and the production parameters in the HI group (p < 0.05). In conclusion, the degree of inbreeding significantly influenced the results, affecting different genomic and phenotypic production and reproductive parameters in high-producing primiparous cows. The determination of the II in first-calf heifers is crucial to evaluate the negative effects associated with homozygosity avoiding an increase in inbreeding depression on production and reproductive traits.
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Affiliation(s)
- Miguel A. Gutiérrez-Reinoso
- Departamento de Ciencia Animal, Laboratorio de Biotecnología Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile; (M.A.G.-R.); (J.C.)
- Facultad de Ciencias Agropecuarias y Recursos Naturales, Carrera de Medicina Veterinaria, Universidad Técnica de Cotopaxi (UTC), Latacunga 050150, Ecuador
| | - Pedro Manuel Aponte
- Colegio de Ciencias Biológicas y Ambientales (COCIBA), Universidad San Francisco de Quito (USFQ), Quito 170157, Ecuador;
- Instituto de Investigaciones en Biomedicina “One-health”, Universidad San Francisco de Quito (USFQ), Campus Cumbayá, Quito 170157, Ecuador
| | - Joel Cabezas
- Departamento de Ciencia Animal, Laboratorio de Biotecnología Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile; (M.A.G.-R.); (J.C.)
| | - Lleretny Rodriguez-Alvarez
- Departamento de Ciencia Animal, Laboratorio de Biotecnología Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile; (M.A.G.-R.); (J.C.)
- Correspondence: (L.R.-A.); (M.G.-H.); Tel.: +56-42-220-8835 (L.R.-A.); Fax: +351-24-3767 (ext. 330) (M.G.-H.)
| | - Manuel Garcia-Herreros
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), 2005-048 Santarém, Portugal
- Correspondence: (L.R.-A.); (M.G.-H.); Tel.: +56-42-220-8835 (L.R.-A.); Fax: +351-24-3767 (ext. 330) (M.G.-H.)
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El Jabri M, Trossat P, Wolf V, Beuvier E, Rolet-Répécaud O, Gavoye S, Gaüzère Y, Belysheva O, Gaudillière N, Notz E, Grosperrin P, Laithier C, Delacroix-Buchet A. Mid-infrared spectrometry prediction of the cheese-making properties of raw Montbéliarde milks from herds and cheese dairy vats used for the production of Protected Designation of Origin and Protected Geographical Indication cheeses in Franche-Comté. J Dairy Sci 2020; 103:5992-6002. [PMID: 32331888 DOI: 10.3168/jds.2019-17491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 02/19/2020] [Indexed: 11/19/2022]
Abstract
Franche-Comté is the primary producing region of Protected Designation of Origin cheeses in France. Normally, mid-infrared (MIR) prediction models for cheese-making property (CMP) traits are developed using individual bovine milks. However, considering the requests of all actors in the dairy sector, the present study aimed to assess the feasibility of MIR spectroscopy to develop CMP equations of Montbéliarde herd and dairy vat milks. For this purpose, 22 CMP traits were analyzed on samples collected in 2016 (half in February-March and half in May-June) from 100 commercial herds and 70 dairy vats (55 cheese dairies) located in Franche-Comté. These characteristics included 11 rennet coagulation traits and 8 lactic acidification traits measured in either soft cheese or pressed cooked cheese conditions and 3 laboratory curd yields. Models of MIR prediction for each of the 22 CMP traits were built using partial least squares regression with external validation by dividing the data set into calibration (70%) and validation (30%) sets. We confirmed that the variability of milk traits depends largely on the production scale and is higher for individual milk than for herd milk and even higher for vat milk. The best prediction models were obtained in herd milk samples for curd yields expressed in dry matter or fresh, with a coefficient of determination (R2) in external validation of 0.78 and 0.77, respectively. As with individual milk, these traits are closely related to the gross composition of the milk and therefore easier to predict by MIR spectroscopy. However, these curd yield traits were poorly predicted (R2 = 0.58) in vat milk samples due to their lower variability. In herd milk samples, prediction models of other CMP traits were poorly accurate except for the ratio of the time to obtain a standard firmness to the rennet coagulation time in soft cheese or pressed cooked cheese conditions, which showed R2 > 0.66 in external validation. Such trait is important in qualifying the behavior of milk during cheese production. Prediction models of other CMP traits for either herd or vat milk samples had poor accuracy, and further work is needed to improve their performance.
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Affiliation(s)
- M El Jabri
- Institut de l'Elevage, F-75012 Paris, France
| | | | - V Wolf
- Conseil Elevage 25-90, F-25640 Roulans, France
| | - E Beuvier
- INRAE, URTAL, F-39800, Poligny, France
| | | | - S Gavoye
- ACTALIA, F-39800 Poligny, France
| | - Y Gaüzère
- Ecole Nationale d'Industrie Laitière et des Biotechnologies, F-39800 Poligny, France
| | - O Belysheva
- Ecole Nationale d'Industrie Laitière et des Biotechnologies, F-39800 Poligny, France
| | | | - E Notz
- Centre Technique des Fromages Comtois, F-39800, Poligny, France
| | | | - C Laithier
- Institut de l'Elevage, F-75012 Paris, France
| | - A Delacroix-Buchet
- Université Paris Saclay, INRAE, AgroParisTech, GABI, F-78350 Jouy-en-Josas, France.
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15
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Jouyban A, Farajzadeh MA, Afshar Mogaddam MR. In matrix formation of deep eutectic solvent used in liquid phase extraction coupled with solidification of organic droplets dispersive liquid-liquid microextraction; application in determination of some pesticides in milk samples. Talanta 2020; 206:120169. [DOI: 10.1016/j.talanta.2019.120169] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/20/2019] [Accepted: 07/21/2019] [Indexed: 12/25/2022]
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16
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RESEARCH OF ORGANOLEPTIC PARAMETERS OF DUTCH CHEESE, PRODUCED FROM MILK OF COWS OF DIFFERENT BREEDS. EUREKA: LIFE SCIENCES 2019. [DOI: 10.21303/2504-5695.2019.00843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The aim of this research is to study an influence of types of different breeds of milk cows on organoleptic properties of Dutch cheese at different rations of their food. It allows to receive cheese with prognosticated quality characteristics and to manage marketing strategies at cheese realization.
The work presents the results of studying organoleptic properties of Dutch cheese, produced of milk of cows of different breeds, traditionally bred in the Northern-Eastern region of Ukraine.
For studying an influence of cows feeding on exclusive properties of hard cheese, traditional food rations of cows were added with forages of a silage-hay type with Lucerne as a main source of proteins (44 % of the daily norm). Rations were practically identical with traditional ones by the content of energy and main food value factors and in general corresponded to norms of cows feeding. It has been demonstrated, that the high mark of cheese, up to 45 points for the taste and smell and total point mark 99 of 100 possible was obtained as a result of changing food rations at the expanse of introducing Lucerne silage in them. The data on optimization of food rations of certain cow breeds allow managing quality characteristics of milk and products of and are expedient for making cheese with unique regional characteristics.
At conducting the comparative organoleptic assessment, there has been revealed a distinct dependence between a point mark of quality parameters of cheese on a breed of milk cows, and also on food rations of them. At the silage-hay food ration of animals, the received Dutch cheese had higher quality characteristics after 60 days of storage than cheese samples, obtained of cow milk at the traditional food ration.
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Franzoi M, Manuelian CL, Rovigatti L, Donati E, De Marchi M. Development of Fourier-transformed mid-infrared spectroscopy prediction models for major constituents of fractions of delactosated, defatted milk obtained through ultra- and nanofiltration. J Dairy Sci 2018; 101:6835-6841. [PMID: 29753470 DOI: 10.3168/jds.2017-14343] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 04/03/2018] [Indexed: 11/19/2022]
Abstract
Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.
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Affiliation(s)
- Marco Franzoi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Carmen L Manuelian
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | | | | | - Massimo De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
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