Effect of processing on the concentrations of boar taint compounds skatole and androstenone in different types of sausage
Bibliographic record
Abstract
In order to valorize tainted meat from entire male pigs, two options exist in the production of different meat products: either to dilute tainted meat or to mask off-odors in the final product. Processing steps may also reduce the concentrations of boar taint compounds. This study investigated the impact of different processing methods on the concentrations of boar taint compounds skatole and androstenone in meat products. Three different types of German sausages, such as raw fermented (salami), boiled (wiener), and cooked meat sausages (liver sausage), were produced from boar meat and fat with either “high” or “low” taint level. Heating during processing, especially the production of wiener and liver sausage, reduced androstenone concentrations between 44 and 87%. In salami, androstenone concentrations were not reduced during production process. In contrast, skatole reductions of up to 26% were observed for salami and up to 44% for wiener, whereas liver sausage was not affected. Practical applications The risk of offensive boar taint is one of the main disadvantages of pork production with entire males. If the detection of tainted carcasses at the slaughter line can be improved, a valid strategy to valorize such carcasses is crucial. The study revealed that open heating during processing has the potential to reduce androstenone, whereas smoking of the products seems to reduce skatole concentrations in the final product.
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) | 0.000 | 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 | 0.000 |
| 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".