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Record W4399863750 · doi:10.1111/ijfs.17291

Preparation, pungency, and bioactivity of capsaicin: a review

2024· review· en· W4399863750 on OpenAlexaff
Qiuyan Zhang, Sirong Huang, Olugbenga P. Soladoye, Yuhao Zhang, Yu Fu

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2024
Typereview
Languageen
FieldNeuroscience
TopicIon Channels and Receptors
Canadian institutionsGovernment of CanadaAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
FundersNatural Science Foundation of ChongqingNational Natural Science Foundation of China
KeywordsPungencyCapsaicinChemistryFood scienceTraditional medicineMedicineBiochemistryPepper

Abstract

fetched live from OpenAlex

Abstract Capsaicin is the main pungent compound in red pepper responsible for its dual attributes of culinary utility and bioactive efficacy. This review aims to systematically review the preparation methods, pungency, and bioactivities of capsaicin. Capsaicin can be extracted by maceration, microwave, near-infrared extraction, supercritical fluid, and ultrasound-assisted extraction methods. Also, it can be synthesised through chemical, biosynthetic, and in vitro cell methods. Capsaicin can elicit a pungent sensation via activation of TRPV1 receptor. Furthermore, it has been reported to display various bioactivities, such as hypoglycaemic, hypolipidemic, analgesic, anti-cancer, anti-inflammatory, and intestinal health-protecting activities via several signalling pathways. Overall, this review can provide a theoretical reference for understanding the preparation, pungent sensation, and bioactivities of capsaicin.

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)0.001
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.064
GPT teacher head0.413
Teacher spread0.348 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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