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Record W3112111248 · doi:10.7831/ras.8.0_323

Coffee Pulp: An Industrial By-product with Uses in Agriculture, Nutrition and Biotechnology

2020· article· en· W3112111248 on OpenAlexaff
Zelada-Castillo Lorena, Vásquez-Arqueros Alexander, Vieira Amandio, Siche Raúl

Bibliographic record

VenueReviews in Agricultural Science · 2020
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsSimon Fraser University
FundersConsejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
KeywordsBiotechnologyPulp (tooth)AgricultureFood industryFood processingMicroorganismBy-productBeneficial organismFood scienceBiologyEcologyMedicine

Abstract

fetched live from OpenAlex

The coffee shell or pulp is the first by-product obtained from the processing of coffee. It represents approximately 40 to 50% of the coffee berry’s weight. Currently, in much of the industry, it is a waste product with a major environmental impact on the water and soil, flora and fauna, and a problem to nearby populations in terms of odor and proliferation of insects and pathogenic microorganisms. This is a review that compiles alternative uses of coffee pulp in agriculture, food and nutrition, medicine and biotechnology. In food and agriculture, for example, the pulp can be used as organic fertilizer to improve degraded soils, in the biological control of plant pathogens, as food or substrate for microorganisms and worms, as feed for chickens, sheep, goats, fish and other animals, and in the productions of foods and beverages for human consumption. In biotechnology, coffee pulp can be used in the cultivation of edible fungi, production of enzymes, substrate for caffeine degrading microorganisms and for microorganisms that produce natural fungicides. Although many of these applications have been proposed and studied, there are also several novel uses that are in the early stages of development; for example, the use of pulp bioactive compounds to make food supplements, or to increase dietary fiber contents in foods and beverages, as well as for the production of biocontainers and biopackaging, alternatives to plastics and their serious environmental impact.

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 score0.701
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.095
GPT teacher head0.333
Teacher spread0.238 · 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

Citations38
Published2020
Admission routes1
Has abstractyes

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