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Record W7160974944

Determination of the Factors Affecting Micronutrient Bioavailability in Canola Meal-Based Poultry Diets

2023· article· en· W7160974944 on OpenAlexaboutno aff
Saluja Karki

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaBioavailabilityIngredientMealMicronutrientRapeseed
DOInot available

Abstract

fetched live from OpenAlex

Canola meal (CM) is a key protein- rich feed ingredient that can be used in poultry diets, containing high levels of micronutrients. However, the bioavailability of the essential micronutrients choline and selenium (Se) is not fully understood, particularly with respect to processing methods and ingredient origin. This study aimed to assess the bioavailability of sinapine-derived choline in broilers and Se in laying hens, and to explore how processing conditions and geographic sources affect their utilization. In the first experiment, a 2 × 3 factorial arrangement was used within a randomized complete block design (RCBD) to assess the effects of processing methods: expeller- pressed canola meal (ECM) versus solvent-extracted canola meal (SCM), at inclusion levels of 5, 10, and 15%. A 26-day feeding trial evaluated growth performance, energy utilization, and ileal digestibility of sinapine and choline in Ross 308 broilers. Liquid chromatography-mass spectroscopy was used for the quantification of sinapine, choline, and trimethylamine oxide (TMAO). Data were analyzed using mixed-model procedures, with processing method, inclusion level, and their interaction as fixed effects and block as a random effect. Results showed processing significantly affected choline availability, with ECM providing higher ileal choline digestibility and a lower sinapine- to- choline ratio than SCM. During the starter phase, ECM at 10% improved early growth, but 15% inclusion reduced energy utilization, likely due to increased dietary fiber. Overall performance did not differ significantly. Furthermore, in the second experiment, solvent-extracted canola meal produced in Canada (CM) and solvent-extracted double-zero rapeseed meal originating from China (OO), incorporated at levels of 8%, 16%, and 24%, were evaluated alongside three control diets comprising base ingredients, absence of canola meal, and supplementation with organic Se (Sel-Plex)  at 0, 0.25, and 1 ppm. The study was conducted on laying hens employing a factorial design, and the data were analyzed utilizing mixed-model procedures. The relative bioavailability of Se was estimated through a slope-ratio assay, using Sel-Plex as the reference source. This trial assessed production performance, energy utilization, egg quality, Se deposition in eggs, and relative Se bioavailability. Celite was used as an indigestible marker, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used for the quantification of Se. Results showed hens fed CM had higher feed intake, greater egg production, and better energy utilization than those fed OO. Increasing CM levels raised egg Se concentration, while higher OO levels reduced Se deposition. Relative Se bioavailability was higher for CM than for OO when compared against Sel-Plex, indicating more efficient Se deposition into eggs. Overall, findings demonstrate that micronutrient bioavailability in canola meal depends heavily on processing method and origin. Expeller processing enhances choline availability in broilers, while Canadian CM is a potent natural Se source for egg enrichment in laying hens. These results underscore the importance of considering bioavailability, not just nutrient concentration, in feed formulation and support the strategic use of canola meal as a functional poultry ingredient.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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 designObservational
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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