Production of Pork Sausages Using <i>Pleaurotus eryngii</i> with Different Treatments as Replacements for Pork Back Fat
Bibliographic record
Abstract
The effects of four treatments of Pleurotus eryngii (king oyster mushroom) as replacements for pork back fat were evaluated for the physicochemical, technological, and sensory properties; nitrite content; and amino acid profile in pork sausages. Five batches were manufactured: one control (formulated with pork back fat) and four treatments with raw, boiled, deep-fried, and fried P. eryngii to replace the pork back fat in sausages. The results indicated that the fat content and energy value decreased, while the protein, moisture, total dietary fiber content, cooking loss, and water-holding capacity of the modified sausages increased. All samples were judged acceptable for their sensory characteristics, with the best one being the sausage containing deep-fried P. eryngii. The raw and fried P. eryngii decreased the residual nitrite content in the sausages. Boiled P. eryngii enhanced the essential amino acids content in the sausages, while the other P. eryngii treatments improved the nonessential amino acid content. In summary, P. eryngii may potentially replace fat in sausages. PRACTICAL APPLICATION: In manufacturing pork sausages, the mushroom P. eryngii can substitute pork back fat to improve the nutritional quality by reducing fat and energy value, while enhancing the protein and total dietary fiber content in the sausages. Raw and fried P. eryngii decreased the residual nitrite content in the sausages. This study provides a basis for preparing healthier alternatives to traditional sausages by substituting pork fat with mushrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".