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Record W4309694810 · doi:10.1016/j.animal.2022.100683

Impacts of production conditions on goat milk vitamin, carotenoid contents and colour indices

2022· article· en· W4309694810 on OpenAlexaff
C. Laurent, H. Caillat, C.L. Girard, Anne Ferlay, Sophie Laverroux, J. Jost, Benoı̂t Graulet

Bibliographic record

Venueanimal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food Canada
FundersRégion NormandieInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementInstitut National de Recherche en Sciences et Technologies pour l'Environnement et l'AgricultureAgence Nationale de la Recherche
KeywordsCarotenoidFood scienceVitaminMilk productionProduction (economics)Animal scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

The content, composition and variation of vitamin compounds in goat milk have been little studied. An experimental design was based on 28 commercial farms, selected considering the main feeding system (based on main forage and especially pasture access), goat breed (Alpine vs Saanen) and reproductive management (seasonal reproduction), in the main French goat milk production area. Each farm received two visits (spring and autumn) that included a survey on milk production conditions and bulk milk sampling. Milk vitamins (A, E, B2, B6, B9, B12) and carotenoid concentrations plus colour indices were evaluated. A stepwise approach determined the variables of milk production conditions that significantly altered milk indicators. The main forage in the diet was the major factor altering goat milk vitamin and carotenoid concentrations and colour indices. Bulk milk from goats eating fresh grass as forage was richer in α-tocopherol (+64%), pyridoxal (+35%) and total vitamin B6 (+31%), and b* index (characterising milk yellowness in the CIELAB colour space) was also higher (+12%) than in milk from goats eating conserved forages. In milk from goats eating fresh grass, concentrations of pyridoxamine, lutein and total carotenoids were higher than in milk of goats fed corn silage (+24, +118 and +101%, respectively), and retinol and α-tocopherol concentrations were higher than in milk of goats fed partially dehydrated grass (+45 and +55%). Vitamin B2 concentration was higher in milk of goats eating fresh grass than in milk of goats fed hay or corn silage as forage (+10%). However, bulk milk when goats had access to fresh grass was significantly poorer in vitamin B12 than when fed corn silage (−46%) and in γ-tocopherol (−31%) than when fed conserved forage. Alpine goats produced milk with higher vitamin B2 and folate concentrations than Saanen goats (+18 and +14%, respectively). Additionally, the milk colour index that discriminates milks based on their yellow pigment contents was 7% higher in milk from Alpine than Saanen herds, but milk from Saanen goats was richer in lutein (+46%). Goat milks were richer in vitamins B2 and B12 and folates, but poorer in vitamin B6 in autumn than in spring (+12, +133, +15 and −13%, respectively). This work highlights that goat milk vitamin and carotenoid concentrations and colour indices vary mainly according to the main forage of the diet and secondly according to the breed and season.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.242
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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