Dataset of the volatile compounds detected in unmarinated and marinated grilled ruminant meats with novel unfiltered beer-based marinades to improve their nutritional quality, safety, and sensory perception
Bibliographic record
Abstract
The objective of this data in brief article is to present the associated data set regarding the published paper Novel unfiltered beer-based marinades to improve the nutritional quality, safety, and sensory perception of grilled ruminant meats in Food Chemistry [1]. Grilling is a popular cooking method; however, the high temperatures required can modify grilled meat quality and safety. In this data set, we include 5 tables containing the volatile composition of unmarinated and marinated grilled ruminant meat (beef and moose). Novel unfiltered beer-based marinades infused with herbs and spices were used for meat marination, and the volatiles present in the meat following grilling extracted by solid phase microextraction and subsequently analysed by gas chromatography/mass spectrometry (SPME-GC/MS). The volatile profile includes alcohols, aldehydes, ketones, acids, esters, alkylfurans, nitrogenated compounds, terpenes (mono-, sesqui- and oxygenated terpenes), sulfur derivatives, benzene derivatives, and phenol derivatives. This dataset provides valuable information on meat volatile composition useful to understand certain aspects of the quality and safety of grilled meat following preparation with unfiltered beer-based marinades. For more insight please see [1].
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 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 | 0.001 | 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 | 0.001 |
| 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".