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High Protein Functional Breads for Sustainable Nutrition: A Futuristic Review

2024· review· en· W4402856123 on OpenAlexfundno aff
Uththara Wijewardhana, Madhura Jayasinghe, Isuru Wijesekara, K. K. D. S. Ranaweera

Bibliographic record

VenueCurrent Functional Foods · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsnot available
FundersUniversity of Sri JayewardenepuraInternational Foundation for ScienceAgriculture and Agri-Food CanadaMassey University
KeywordsProtein qualityFood scienceHigh proteinBiotechnologyPopulationStaple foodNutrientDietary fibrePhytochemicalBiologyLegumeBread makingAgronomyAgricultureBotanyMedicine

Abstract

fetched live from OpenAlex

Bread is consumed by almost every household worldwide as a dietary staple. Most commercial bread products are made with refined wheat flour and have an incomplete nutritional profile. Refined wheat bread is comparatively lower in protein with an unbalanced amino acid profile and is low in fibre, vitamins, minerals, and phytochemicals. The enrichment of bread to increase nutritional quality and functionality while preserving sensory properties has been a point of interest for decades. Legumes and pulses are nutrient-dense plant ingredients capable of increasing and balancing the nutritional value of bread, especially the protein quality. The review aims to explore possible legumes and pulses for bread enrichment and recent developments in the study area, balancing the amino acid profile of bread, the behaviour of legume anti-nutritional factors in bread making, enhancing protein metabolism, associated challenges, and future directions. The Enrichment of bread with legumes and pulses will ensure a high protein intake, a balanced amino acid profile, and additional vitamin, mineral, and phytochemical content compared to refined wheat bread. The development and commercialization of enriched functional bread products will benefit a vast population, especially in developing countries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.001

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 head0.097
GPT teacher head0.330
Teacher spread0.233 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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