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Caracterización nutricional y funcional de la harina de mashua

2020· article· es· W3046088902 on OpenAlexvenueno aff
María Verónica González Cabrera, Georgina Ipatia Moreno Andrade, Sandra Lopez

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMathematicsHumanitiesArt

Abstract

fetched live from OpenAlex

La presente investigación propone una caracterización de la harina de mashua (Tropaeolum tuberosum) desde el punto de vista nutricional y funcional para establecer la utilidad de su aplicación. Se inició con la obtención de la harina luego de un proceso de selección, limpieza, lavado y troceado, secado, molienda y tamizado se procedió a analizar las muestras tomando en cuenta los métodos establecidos por las Normas Técnicas Ecuatorianas INEN y la AOAC (Official Methods of Analysis of the Association). El análisis proximal realizado a las muestras de harina de mashua reflejan un contenido de humedad promedio de 10,46±0,09%, el aporte de proteína se encuentra en un rango de 12,34±0,08%, mientras que el contenido de cenizas presenta un valor promedio de 4,66±0,10%. En cuanto al contenido de grasa se obtuvo un resultado promedio de 0,81±0,24% y con respecto a la fibra un promedio de 7,07±0,009%. Para carbohidratos el resultado promedio fue de 64,67±0,14%. Del análisis funcional de la harina de mashua se obtiene un valor de pH igual a 5,5±0,02 y un porcentaje de acidez de 1,08 ±0,025%, además la capacidad de retención de agua CRA mostró un porcentaje que varía entre 5,05 y 8,63% para un rango de temperaturas de 50 a 80°C, demostrándose con este resultado que la temperatura incide significativamente en la CRA. Las muestras de harina de mashua analizadas cumplen con los parámetros establecidos y son de calidad físico-química y funcional aceptable.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.819

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.218
Teacher spread0.201 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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