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Record W1975785819 · doi:10.1155/2015/526762

Food Processing and Maillard Reaction Products: Effect on Human Health and Nutrition

2015· review· en· W1975785819 on OpenAlex

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 · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaillard reactionBrowningChemistryFood scienceAromaFlavourHuman healthFood processingOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Maillard reaction produces flavour and aroma during cooking process; and it is used almost everywhere from the baking industry to our day to day life to make food tasty. It is often called nonenzymatic browning reaction since it takes place in the absence of enzyme. When foods are being processed or cooked at high temperature, chemical reaction between amino acids and reducing sugars leads to the formation of Maillard reaction products (MRPs). Depending on the way the food is being processed, both beneficial and toxic MRPs can be produced. Therefore, there is a need to understand the different types of MRPs and their positive or negative health effects. In this review we have summarized how food processing effects MRP formation in some of the very common foods.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.448
Teacher spread0.367 · 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