Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 560977
Title Dairy farming system markers: The correlation of forage and milk fatty acid profiles from organic, pasture and conventional systems in the Netherlands
Author(s) Liu, Ningjing; Pustjens, Annemieke M.; Erasmus, Sara W.; Yang, Yuzheng; Hettinga, Kasper; Ruth, Saskia M. van
Source Food Chemistry 314 (2020). - ISSN 0308-8146
DOI https://doi.org/10.1016/j.foodchem.2019.126153
Department(s) BU Toxicology, Novel Foods & Agrochains
BU Authenticity & Bioassays
Food Quality and Design
VLAG
Publication type Refereed Article in a scientific journal
Publication year 2020
Availibility Full text available from 2021-06-01
Keyword(s) Classification - Correlation analysis - Fatty acids - Forage - Milk - Organic
Abstract

The relationships between the fatty acid (FA) composition in forage and milk (F&M) from different dairy systems were investigated. Eighty milk samples and 91 forage samples were collected from 40 farms (19 organic, 11 pasture and 10 conventional) in the Netherlands, during winter and summer. The FA profiles of F&M samples were measured with gas chromatography. The results showed that the F&M of organic farms were significantly differentiated from the F&M of other farms, both in summer and winter. The differences are likely due to the different grazing strategies in summer and different forage composition in winter. The Pearson's correlation results showed the specific relationship between individual FAs in forages and related milk. A PLS-DA model was applied to classify all milks samples, resulting in 87.5% and 83.3% correct classifications of training set and validation set.

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