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Record W2014825741 · doi:10.5539/jfr.v3n3p132

Use of Avocado and Tomato Paste as Ingredients to Improve Nutritional Quality of Pork Frankfurter

2014· article· en· W2014825741 on OpenAlexvenueno aff
Martín Valenzuela‐Melendres, N. G. Torrentera-Olivera, Gustavo A. González‐Aguilar, Mónica A. Villegas‐Ochoa, Luis German Cumplido-Barbeitia, Juan Pedro Camou

Bibliographic record

VenueJournal of Food Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceAntioxidantChemistryPulp (tooth)BiochemistryMedicine

Abstract

fetched live from OpenAlex

The objective of this research was to study the effect of avocado pulp (A) and tomato paste (T) addition on the physicochemical, nutritional and sensory quality of pork frankfurters. Treatments were: 1) Control; 2) A10 = 10% A; 3) A20 = 20% A; 4) T10 = 10% T; 5) T20 = 20% T; and 6) A10+T10 = 10% A+10% T. Colour (L*, a* and b*), fatty acid profile, contents of phenols and flavonoids, and antioxidant capacity were measured. In the same way, sensory analysis was evaluated. Tomato paste decreased L* but increased (P < 0.05) a* and b* values. On the other hand, A did not affect L*, decreased a* and increased b*. Avocado pulp increased (P < 0.05) the proportion of monounsaturated fatty acids in the finished product. Antioxidant activity increased (P < 0.05) with incorporation of T, much higher than that observed by adding A. Frankfurters with T and with a combination of T and A had the best acceptance by the sensory panel. The use of T and A can be a good strategy to improve nutritional quality and antioxidant properties of pork frankfurters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.220
GPT teacher head0.388
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2014
Admission routes1
Has abstractyes

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