Application of histochemical and immunohistochemical techniques for detection of lung tissue in cooked sausage.
Bibliographic record
Abstract
Background: Using unauthorized tissues in sausage is a common food adulteration in some parts of the world. Aims: This study was designed to compare the accuracy of histochemical and immunohistochemical techniques for the detection of lung tissue in cooked sausage samples. Methods: Samples with different levels of sheep lung tissues (1, 2.5, and 5%) and a control group were prepared and stained histochemically using H&E, Masson trichrome, and Periodic Acid-Schiff (PAS) stainings, and immunohistochemically using two different commercially-available antibodies of TTF1 Pan-cytokeratin. Results: The highest positive results of lung tissue detection were achieved in sausage samples stained with anti-TTF1 immunohistochemical staining method. Both anti-TTF1 and anti-pan-cytokeratin immunohistochemical techniques detected all contaminated sausage samples treated with 50 g/kg lung tissues. Anti-TTF1 staining method had the highest odds ratio (7.4), followed by anti-pan-cytokeratin method (6.0). Reversely, PAS staining method had the lowest odds ratio (0.21), followed by Masson trichrome method (1.7). Additionally, anti-TTF1 method had the highest (1.8-31.0) confidence intervale (95%), while PAS had the lowest (0.02-2.1). Totally, the odds ratio of lung tissue detected by immunohistochemical methods were higher than those detected by histochemical staining. Conclusion: This is the first report on the comparison of histochemical and immunohistochemical techniques for lung tissue detection in cooked sausage. Anti-TTF1 immunohistochemical staining proved to be the most useful technique for the detection of unauthorized lung tissue in cooked sausages.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".