As forage may affect the environmental sustainability of a given dairy chain, this study evaluated the discriminant capacity of fatty acids (FAs) and NMR metabolomic profiles of milk from three dairy chains, where forage components of cows diets were: maize silage (MS), grass-legume and maize silage (GMS), grass and lucerne hay (HAY). Canonical discriminant analysis (CDA) based on FAs and NMR metabolites highlighted a reliable discriminative performance for HAY samples that were correctly recognised, especially on the basis of C18:3n-3 and C17:0. The GMS samples were positively correlated with choline, C14:0 and C17:1 cis-9, while the MS ones were represented mainly by C16:1 cis-9. An overlap between MS and GMS samples was observed, even if a low-level fused CDA modelling improved their correct assignment. The footprint of maize silage on the milk metabolomic profile seemed not to be affected if partially replaced by a mix of legume and grass silages.

Use of GC–MS and 1H NMR low-level data fusion as an advanced and comprehensive metabolomic approach to discriminate milk from dairy chains based on different types of forage / Lanza, I.; Lolli, V.; Segato, S.; Caligiani, A.; Contiero, B.; Lotto, A.; Galaverna, G.; Magrin, L.; Cozzi, G.. - In: INTERNATIONAL DAIRY JOURNAL. - ISSN 0958-6946. - 123(2021), p. 105174.105174. [10.1016/j.idairyj.2021.105174]

Use of GC–MS and 1H NMR low-level data fusion as an advanced and comprehensive metabolomic approach to discriminate milk from dairy chains based on different types of forage

Lolli V.
;
Caligiani A.;Galaverna G.;
2021

Abstract

As forage may affect the environmental sustainability of a given dairy chain, this study evaluated the discriminant capacity of fatty acids (FAs) and NMR metabolomic profiles of milk from three dairy chains, where forage components of cows diets were: maize silage (MS), grass-legume and maize silage (GMS), grass and lucerne hay (HAY). Canonical discriminant analysis (CDA) based on FAs and NMR metabolites highlighted a reliable discriminative performance for HAY samples that were correctly recognised, especially on the basis of C18:3n-3 and C17:0. The GMS samples were positively correlated with choline, C14:0 and C17:1 cis-9, while the MS ones were represented mainly by C16:1 cis-9. An overlap between MS and GMS samples was observed, even if a low-level fused CDA modelling improved their correct assignment. The footprint of maize silage on the milk metabolomic profile seemed not to be affected if partially replaced by a mix of legume and grass silages.
Use of GC–MS and 1H NMR low-level data fusion as an advanced and comprehensive metabolomic approach to discriminate milk from dairy chains based on different types of forage / Lanza, I.; Lolli, V.; Segato, S.; Caligiani, A.; Contiero, B.; Lotto, A.; Galaverna, G.; Magrin, L.; Cozzi, G.. - In: INTERNATIONAL DAIRY JOURNAL. - ISSN 0958-6946. - 123(2021), p. 105174.105174. [10.1016/j.idairyj.2021.105174]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2903212
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