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Record W4416793699 · doi:10.1155/ijfo/7084868

Sorghum and Sorghum‐Based Products: Nutritional Composition, Prebiotic Potential and Health Benefits in Gut Microbiota Interactions

2025· article· en· W4416793699 on OpenAlex
Warnakulasuriya Mary Ann Dipika Binosha Fernando, Abdulraheem R. Adisa, Kalmee Pramoda Kariyawasam, Haththotuwa Gamage Amal Sudaraka Samarasinghe, H.D. Barnes, Dona Pamoda W. Jayatunga, B. G. D. Nissanka Kolitha De Silva, Vijay Jayasena

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Food Science · 2025
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPrebioticSorghumHealth benefitsMicronutrientCropFunctional foodGut floraSweet sorghum

Abstract

fetched live from OpenAlex

family and is the fifth most important crop globally. Sorghum grains (SGs) are rich in health-promoting macro- and micronutrients and phytochemicals. SGs are commonly consumed as food or as ingredients in food, especially in African countries. Therefore, food products such as ogi, bread and flour have been and are still being developed from SGs to provide nutritional and health benefits. However, the nutritional and prebiotic potential of SGs, especially the pigment pericarps, has not been fully exploited. This review describes micronutrients in different varieties of sorghum and the health benefits of sorghum consumption, especially its interaction with the human gut microbiota. It further provides a comprehensive update on the properties and health benefits of improved sorghum-based food products. Finally, the influence of processing methods on SGs is summarised.

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.

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)0.000
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 score0.585
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.310
Teacher spread0.285 · 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