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Record W4317726056 · doi:10.1177/10820132231152279

Green alternative for sodium metabisulfite substitution: Comparison of bacterial and fungal proteases effect in hard biscuit making

2023· article· en· W4317726056 on OpenAlexaff
Farzad Saberi, Fatemeh Kouhsari, Nicola Gasparre

Bibliographic record

VenueFood Science and Technology International · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSodium metabisulfiteProteasesFood scienceChemistryProteaseSodiumFood spoilageEnzymeBacteriaBiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Sodium metabisulfite is one of the most employed reducing agents in hard biscuit making. The recent results about its adverse effects on human health have pushed us to look in new safer and greener directions. Two different proteases, from distinct strains (bacterial and fungal), were selected and their effects on the dough thermomechanical performances, texture, and structure of the hard biscuits were compared with those obtained from the sodium metabisulfite. Doughs treated with fungal protease showed higher stability during mixing and higher consistency throughout the heating stage. On the other hand, bacterial protease had the greatest weakening effect on protein with a marked reduction of starch gelatinization. Doughs processed with fungal enzymes reached similar values to those containing sodium metabisulfite. In terms of hardness, no significant ( p < 0.05) differences were found between biscuits made with bacterial protease and sodium metabisulfite. Analysis of the volumetric characteristics highlighted that bacterial enzymes gave higher values of specific volume and surface area. Regarding the structure of the hard biscuits, sodium metabisulfite produced a more uniform structure with fewer and smaller pockets as compared with the samples treated with proteases.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.072
GPT teacher head0.324
Teacher spread0.251 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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