Analyzing the Effect of Cassia Extracts on the Decrease in Nitrosamine Content of Harbin Dry Sausages
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".