Fatty acids and volatile flavor compounds in commercial plant‐based burgers
Bibliographic record
Abstract
Interest in plant-based meat alternatives (PMBAs) has been rapidly growing in both the food research community and the food industry due to higher consumer demands in recent years. However, scientific data regarding the health and aroma aspects of PBMA are rare. In this study, the fatty acids (FAs) and volatile flavor compounds (VFCs) were profiled in four types of plant-based burgers (PBs) and compared to beef burger (BB). Over 40 FAs and 64 VFCs were detected and quantified in the samples. Nonsignificant differences (α = 0.05) were observed in the percentages of most FAs between uncooked and cooked PBs. PBs contained lower percentages of saturated FAs and trans-FAs, higher percentages of unsaturated FAs, and lower ratio of n-6 to n-3 FAs comparing to the BB. The FA profiles in PBs are mainly determined by their ingredients. The VFC profile of cooked PBs was different from that of the uncooked ones. The ingredients, thermally induced Maillard reaction, and lipid oxidation had contributed to the formation of the flavor. For uncooked samples, the VFC profiles of PB 3 and PB 4 were similar to that of BB. While for cooked samples, PB 1 had similar VFC profile as BB. This illustrated the importance of the cooking process for aroma formation; however, ingredients, such as spices, remain an important source of VFCs in these burger samples. Ingredient optimization could be an effective strategy to enhance the flavor of PBs to resemble BB. PRACTICAL APPLICATION: This study provides the knowledge of health and aroma-related components in both raw and cooked PBs, including FA and VFC profiles. It also explains the source of those components. This will not only help consumer's decision making in choosing plant-based meat alternatives, but also help the related industry to choose proper ingredients to optimize the final products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".