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Record W2993051456 · doi:10.5539/jas.v12n1p209

Development of a Drink Type Infusion From Coffee Pulp (Arabia coffea) Lempira Variety of Honduras

2019· article· en· W2993051456 on OpenAlexvenueno aff
Jhunior Marcía, Ricardo Antonio Alemán, Leonardo Antonio Chavarría, Ingris Mary Varela Murillo, Noreyda Patricia Alvarado, Ismael Montero-Fernández

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsFlavorPulp (tooth)Food scienceMathematicsTasteCoffeaGrindingSensory analysisChemistryPulp and paper industryDark chocolateHorticultureMaterials scienceBiologyEngineeringMedicineMetallurgyDentistry

Abstract

fetched live from OpenAlex

This work aimed to develop an infusion type beverage from Lempira coffee pulp for human consumption. Samples from the Honduran Coffee Institute IHCAFE were used. According to the results obtained from the sensory analysis, it was determined that 70.75% of the evaluators prefer the beverage made with mature grains, given that a balance in taste and acidity is obtained. As for the mass concentration of the packaged content, 60% of the universe of tasters prefer a concentration of 1.6 g of dried pulp per cup. Regarding the type of grinding of the grain, it was determined that 80% of the tasters prefer coarse grinding (701 to 900 µm), describing it as an extremely pleasant product in its flavor and color, with shades of Jamaica, tamarind and nuts. Checking that with this particle size there is a greater release of flavors and maintains a balance in terms of aromas, however in fine particles (350 to 500 µm), their fragrances stand out, but negatively affects their taste and the high sedimentation rate. Therefore, it was concluded that the quality of the infusion drink from coffee pulp is influenced by the type of grinding used for its preparation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.300
Teacher spread0.272 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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