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Record W4293319035 · doi:10.1093/jas/skac279

Digestible indispensable amino acid scores of animal and plant ingredients potentially used in dog diet formulation: how this protein quality metric is affected by ingredient characteristics and reference amino acid profile

2022· article· en· W4293319035 on OpenAlexaff
James R Templeman, Anna K. Shoveller

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIngredientAmino acidFood scienceMetric (unit)BiologyEssential amino acidQuality (philosophy)BiochemistryChemistryEngineering

Abstract

fetched live from OpenAlex

The ability of a diet or an ingredient to satisfy the indispensable amino acid (IAA) requirements of an individual is a reflection of protein quality (PQ). The concept of PQ is gaining recognition in the pet food industry as a way to identify candidate ingredients for diet formulation. The objective of this report was to use IAA digestibility data from swine and cecectomized rooster assays to generate digestible IAA scores (defined herein as DIAAS-like values) to predict the PQ of ingredients used in dog diets. However, as PQ equation development relies on a reference IAA profile, which is intended to be based on the physiological requirements of a specific population, we sought to generate DIAAS-like values using IAA requirements established by the National Research Council (NRC) as well as practical IAA recommendations presented by the Association of American Feed Control Officials (AAFCO) and European Pet Food Industry Federation (FEDIAF), to assess how these profiles may affect PQ. In total, 30 animal (75 unique inputs) and 27 plant ingredients (94 unique inputs) satisfied all inclusion criteria to be used in the final data set. Ingredients were initially categorized as animal or plant, and further categorized based on AAFCO Official Common and Usual Names and Definitions of Feed Ingredients to allow for additional, more distinct comparisons to be made. Data were analyzed using PROC GLIMMIX in SAS, with ingredient reference as a random effect, and ingredient category, regulatory body, and life stage as fixed effects. As expected, differences were observed in DIAAS-like values for nearly all ingredients and ingredient categories when determined using NRC, AAFCO, or FEDIAF IAA requirements or recommendations as the reference pattern. Moreover, applying reference patterns based on NRC adult maintenance IAA requirements consistently produced the lowest DIAAS-like values. Ultimately, while future studies assessing PQ should utilize NRC minimal requirements, individual ingredient and ingredient category differences in DIAAS-like values when using AAFCO and FEDIAF recommendations underpin the different regulatory approaches to establishing dietary nutrient recommendations that exist globally and support the need for harmonization of dietary recommendations.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.268
Teacher spread0.228 · 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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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