2020
DOI: 10.3390/ani10071231
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Determination of Fatty Acids Profile in Original Brown Cows Dairy Products and Relationship with Alpine Pasture Farming System

Abstract: This study aimed to evaluate the relationships between fatty acids and the pattern that most contributes to discriminate between two farming systems, in which the main difference was the practice, or not, of alpine summer-grazing. Milk and cheese were sampled every month in two farms of Original Brown cows identical under geographical location and management during no grazing season point of view in the 2018 season. Fatty acids concentrations were determined by gas chromatography. The principal component analy… Show more

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Cited by 29 publications
(30 citation statements)
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“…Colour and texture generally change with the aging of dairy products [ 62 , 63 ], even if the yellow colour in milk and cheese also highly depends on their carotenoid content, which is generally higher in spontaneous pasture [ 64 ]. Our finding confirms that mountain pasture dairy products are recognizable and distinguishable from valley floor stall products [ 17 , 18 ]. Nevertheless, product sensory differences due to different milk production were probably influenced by differences induced by maturation [ 65 ], as the pasture cheeses were ripened 100 days more than the stall ones.…”
Section: Discussionsupporting
confidence: 86%
“…Colour and texture generally change with the aging of dairy products [ 62 , 63 ], even if the yellow colour in milk and cheese also highly depends on their carotenoid content, which is generally higher in spontaneous pasture [ 64 ]. Our finding confirms that mountain pasture dairy products are recognizable and distinguishable from valley floor stall products [ 17 , 18 ]. Nevertheless, product sensory differences due to different milk production were probably influenced by differences induced by maturation [ 65 ], as the pasture cheeses were ripened 100 days more than the stall ones.…”
Section: Discussionsupporting
confidence: 86%
“…Mahalanobis distance was used to verify multivariate normality and identify the presence of multivariate outliers. The relative importance of each behavior in classifying the camels’ location was evaluated by using Wilks’ lambda (the smaller the Wilks’ lambda score, the more important the variable to the Df) and by the discriminant loadings (correlations between each independent variable and the discriminant scores associated with the Df) [ 32 , 33 ]. Moreover, the discriminant scores were calculated as a weighted linear combination of the behaviors.…”
Section: Methodsmentioning
confidence: 99%
“…They were used to establish the cutting point for classifying samples during the cross-validation. The leave-one-out cross-validation was performed as an index of Df performances, calculating the probability for each item to be accurately classified in the correct location [ 32 , 33 ].…”
Section: Methodsmentioning
confidence: 99%
“…Then, the principal component analysis (PCA) was used to describe relationships between body condition, hormones, and metabolites and convert the numerous variables into a few profiles describing the hormonal and metabolic framework. The very low or very high correlations between variables were checked using correlation matrices in order to identify suitable variables for the PCA [36][37][38]. Moreover, the Determinant of the correlation matrix and the Bartlett's test of Sphericity were also used.…”
Section: Statistical Considerations and Data Analysismentioning
confidence: 99%