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Record W1996899489 · doi:10.1080/21551197.2014.927307

The Effect of Varying Ingredient Composition on the Sensory and Nutritional Properties of a Pureed Meat and Vegetable

2014· article· en· W1996899489 on OpenAlexaff
Nila Ilhamto, Heather Keller, Lisa M. Duizer

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2014
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of GuelphUniversity of Waterloo
Fundersnot available
KeywordsSeasoningFood scienceIngredientSkimmed milkFlavorStarchRecipeCorn starchComposition (language)Chemistry

Abstract

fetched live from OpenAlex

The objective of this research was to investigate the effects of ingredients and preparation methods on the sensory and nutritional properties of pureed turkey and carrots. Turkey samples varying in added liquid and muscle composition were developed. Seasoning application methods were also studied. Pureed carrots were formulated with no added thickener, added modified corn starch, rice cereal, or skim milk powder. Small changes in added liquid and seasoning application altered the perceived texture of the turkey. Pureed carrots with added modified corn starch were more slippery and firm than other samples. The addition of skim milk powder or rice cereal did not alter sensory properties but led to higher protein contents when compared to unthickened carrots. In-house formulations did not differ in sensory ratings of appearance and flavor when compared to commercial products but contained more carbohydrates. Modest changes in recipes for pureed products can improve sensory appeal and nutrient density; quality in-house products are feasible with only minor alterations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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