Dataset on improved nutritional quality and safety of grilled marinated and unmarinated ruminant meat using novel unfiltered beer-based marinades
Bibliographic record
Abstract
[1]. Grill food safety and quality is a major concern globally. Here in we present data demonstrating the use of novel unfiltered beer based marinades in improving the nutritional quality and safety of grilled ruminant meat. Grilling can lead to the formation of harmful compounds and modify the functional lipids in meats via oxidation, thereby affecting the nutritional quality and safety of the finished product. Lipid oxidation is a deteriorative process involving the degradation of lipid double bonds and the formation of new compounds. Some of these compounds can result in reduced meat quality and off-flavours affecting the sensory, nutritional quality and safety of grilled meat. Unfiltered beers, herbs and spices are known to be excellent sources of antioxidants and polyphenols which can suppress oxidation of functional lipids in grilled meat. Novel unfiltered beer based marinades were developed and used to marinate ruminant meat (beef and moose) prior to grilling. The effect of marination on the fatty acid profile, including saturated, mono- and polyunsaturated fatty acids, of grilled meat was analyzed by gas chromatography/mass spectrometry (GC/MS). In this data in brief article, we include 3 tables containing the fatty acid composition of unmarinated and marinated grilled ruminant meats (beef and moose), a figure showing the percent distribution of grilled meat fatty acid classes, and 2 figures on Pearson's correlation for the associations between phenolic contents, oxidation status and total conjugated linolenic acid (CLA) content. To the best of our knowledge, there is a paucity of information in the literature on the fatty acid composition of wild Cervid meat following preparation by grilling. Grill food safety and nutritional quality is of significant interest to researchers and consumers in the scientific and general food science communities. This article provides data on the fatty composition of grilled moose meat and could be of value to fill the paucity of information currently available in the scientific community on the observed fatty acid composition of grill moose meat. Furthermore, the article presents data on the effects of beer based marinade formulations on the quality of the fatty acid composition of grilled ruminant meats (beef and moose). The growing awareness of the benefits of dietary fatty acids in enhancing personal and population health by reducing the risk factors for cardiovascular diseases and neurodegenerative disorders means that consumers demand meat products with improved fatty acid composition [2,3]. Cervids such as moose (Alces alces) are popular as superior sources of low-fat lean meat with balanced omega 6:3 essential fatty acids compared to traditional farm raised or domesticated meat animals due to the forage they consume as a normal part of their diet [2,4,5]. Furthermore, session ale beers is currently a global phenomenon derived from unique combinations of grains, hops, fruits and herbs to produce low alcohol by volume beers with unique flavors popular among consumers. Two unfiltered session beers were used as base ingredients to produce two novel marinades infused with unique combination of antioxidant rich herbs and spices as a suitable system for the production of grilled foods with enhanced nutritional and sensory characteristics. Whilst there are a limited number of studies in the literature that have used unfiltered beers to evaluate the effects of these beers on suppression of lipid oxidation in grilled meat, none to the best of our knowledge has evaluated the effect of antioxidant rich unfiltered beer based marinades on fatty acid composition of grilled meat systems [6] [7]. As such, this data set presents the concept of using craft beers (specifically session ales) infused with unique combination of herbs and spices to produce unfiltered beer base marinades with enhanced ability to improve grill food sensory attributes and quality, and demonstrates that novel formulations of popular unfiltered India session ale and wheat ale based marinades infused with unique combinations of herbs and spices could be used to marinate beef and moose meats prior to grilling to preserve meat lipids including anticarcinogenic linoleic acid and essential ω3 and ω6 fatty acids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".