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Record W2996322062 · doi:10.1111/1541-4337.12517

Hempseed in food industry: Nutritional value, health benefits, and industrial applications

2019· review· en· W2996322062 on OpenAlexaboutno aff
William Leonard, Pangzhen Zhang, Danyang Ying, Zhongxiang Fang

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsCannabis sativaFood industryBiotechnologyBusinessHealth benefitsFood processingNutraceuticalMarketingFood scienceBiologyMedicineTraditional medicine

Abstract

fetched live from OpenAlex

Hemp (Cannabis sativa L.) seeds have been consumed in Asian communities since prehistoric times. Recently, Australia, Canada, and the United States have legalized the cultivation and consumption of hempseed at low (<0.3%) tetrahydrocannabinol levels, and there's a growing interest in hempseed due to its nutritional value and pharmaceutical potential. This review aims to summarize the chemical composition, nutritional value, and potential health benefits of hempseed, as researched via in vitro and in vivo trials. The application of hempseed in the food industry is limited due to its poor performance on some functional properties, so the latest processing methods developed to improve these properties were compared. Additionally, manufacturing technologies incorporating hemp seeds into existing food products are also elaborated. This review would promote further in-depth research on this recently approved food resources and maximize its utilization in new food product development.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.255
GPT teacher head0.367
Teacher spread0.112 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations254
Published2019
Admission routes1
Has abstractyes

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Same venueComprehensive Reviews in Food Science and Food SafetySame topicCassava research and cyanideFrench-language works237,207