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Record W4388230792 · doi:10.1080/10408398.2023.2274949

Amadori compounds: analysis, composition in food and potential health beneficial functions

2023· review· en· W4388230792 on OpenAlexaff
Jialing Cao, Cheng Yang, Jian Zhang, Lianfu Zhang, Rong Tsao

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMaillard reactionAmadori rearrangementHuman healthFunctional foodFlavorChemistryFood productsBiotechnologyFood scienceBiochemical engineeringBiochemistryGlycationMedicineBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Amadori compounds (ACs) are key intermediates of the Maillard reaction, and found in various thermally processed foods. Simultaneous analysis of multiple ACs is challenging due to the complex amino acid and carbohydrate compositions, and the different food matrices. Most studies focus on the effects of ACs on food flavor and related sensory properties, but not their biological functions. However, increasing evidence shows that ACs possess various beneficial effects on human health, thus a comprehensive review on the various biological activities is warranted. In this review, we summarized the composition and content of ACs in different foods, their formation and degradation reactions, and discussed the latest advances in analytical methods of ACs and their biological functions related to human health. Limitations and research gaps were identified and future perspectives on ACs research were proposed. This review points to the needs of systematic and comprehensive in vitro and in vivo studies on human health related biological functions of ACs and their mechanisms of action, particularly the synergistic effects with other food components and drugs, and roles in intestinal health and metabolic syndrome.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
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 head0.093
GPT teacher head0.434
Teacher spread0.341 · 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 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

Citations21
Published2023
Admission routes1
Has abstractyes

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