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Record W2391319367

Influence of different initial pH and heating time on characteristic of the porcine bone protein hydrolysate Maillard products

2013· article· en· W2391319367 on OpenAlexaff
Sun Fang-d

Bibliographic record

VenueScience and Technology of Food Industry · 2013
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsScience North
Fundersnot available
KeywordsMaillard reactionChemistryBrowningHydrolysateFlavorFood scienceNitrogenXyloseChromatographyBiochemistryOrganic chemistryHydrolysisFermentation
DOInot available

Abstract

fetched live from OpenAlex

To study the influence of initial pH and heating time on products of Maillard reaction,porcine bone protein hydrolysates and glucose-xylose model systems were employed. The results showed that,with the increase of initial pH,intermediate products of Maillard reaction appeared first increased and then decreased and reached maximum at pH8.0,amino-acid nitrogen loss rate increased with the increase of initial pH,with the prolongation of heating time,intermediate products of Maillard reaction and amino-acid nitrogen loss rate increased at the same time. L* value and degree of browning showed complementary decreasing and increasing trend when initial pH and heating time increased. The best sensory qualities of Maillard products were obtained at the initial pH of 7.0 ~7.5,40 ~60min. In summary,initial pH7.0 and heating time 40min were selected as the optimum reaction conditions. The flavor product obtained by this conditions had natural fragrance flavor and attractive color.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.237
Teacher spread0.229 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2013
Admission routes1
Has abstractyes

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