MétaCan
Menu
Back to cohort
Record W2124436081 · doi:10.5897/ajb09.970

Investigation of enzyme modified cheese production by two species of Aspergillus

2010· article· en· W2124436081 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

VenueAFRICAN JOURNAL OF BIOTECHNOLOGY · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsMcGill University
Fundersnot available
KeywordsAspergillus nigerAspergillus oryzaeFlavorFood scienceChemistryOdorLipaseFlavourRipeningEnzymeBiochemistryFermentationOrganic chemistry

Abstract

fetched live from OpenAlex

Enzymatic biotransformation of dairy protein and fat is the basis of most commercial cheese flavour ingredient processes; such products are commonly referred to as enzyme modified cheese (EMC). EMCs have approximately 15 - 30 times the flavor intensity of natural cheese. They are available as pastes or spray-dried powders. Aspergillus oryzae and Aspergillus niger are two kinds of molds that were used in this study for production of enzyme modified cheese. The results showed that A. niger and A. oryzae have lipase enzyme activities of about 43.3 and 10 U/g (U = 1 mol/min), respectively, while the proteolytic activity was 143 U/g for A. oryzea and 38 U/g for A. niger. The EMC produced using both A. oryzae and A. niger had the best score of flavor and odor after 3 days of storage; however the cheese produced by only A. oryzea had good flavor after this period of time and the cheese treated with A. niger only just had a strong odor. The results of this study showed that the mixture of A. oryzea and A. niger can be used to produce EMC in much shorter ripening period and with better flavor.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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