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Record W2350334369

Analyzing the Effect of Cassia Extracts on the Decrease in Nitrosamine Content of Harbin Dry Sausages

2015· article· en· W2350334369 on OpenAlexaff
LI Mu-chu

Bibliographic record

VenueXiandai shipin keji · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsScience North
Fundersnot available
KeywordsChemistryNitrosamineNitriteFood scienceCassiaTBARSChromatographyCarcinogenNitrateAntioxidantOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

In this study, cassia extracts were added to Harbin dry sausages. The nitrosamine content and related physicochemical properties, such as the pH value, nitrite residue content, peroxide value(POV), thiobarbituric acid(TBARS) content, and total volatile basic nitrogen(TVB-N) content in the sausages was determined 0, 2, 4, 6, 8, and 10 days after addition of extracts, byhigh performance liquid chromatography, in order to determine their relationship with the inhibition of nitrosamine production. The results of these analyses revealed that cassia extract exerted a significant inhibitory effect(P 0.05) on a number of nitrosamines in the dried sausages. This effect was observed to be more significant with the increase in the quantity of extract added. The rate of inhibition of nitrosodimethylamine(NDMA), nitroso piperidine(NPIP), nitroso-propylamine(NDPA), nitroso amide(NDBA), and nitroso diphenylamine(NDph A) was determined to be 26%, 63%, 50%, 59%, and 55%, respectively, in the group treated with 0.3 g/kg cassia extracts on day 10. The inhibitory effect of cassia extract on nitrosamines was not affected by the pH value. The cassia extract significantly(P 0.05) reduced the TBARS, POV, and TVB-N values and nitrite residue in Harbin dry sausages. This showed that the lipid oxidation, amine content, and nitrite residue content was closely linked with nitrosamine formation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.110
GPT teacher head0.283
Teacher spread0.173 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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