Bioactive composition and promising health benefits of natural food flavors and colorants: potential beyond their basic functions
Bibliographic record
Abstract
Purpose The purpose of this review is to address the consumer’s preferences that have varied greatly in the past decade appraising the use of flavor and aroma compounds in the development of functional foods rather than consuming artificial additives. A growing interest in natural flavoring agents and preservatives have made the researchers to explore the other bio-functional properties of natural flavors beyond their ability to give a remarkable flavor to the food. Design/methodology/approach In this review, five major flavoring agents used significantly in food industries have been discussed for their bioactive profile and promising health benefits. Vanilla, coffee, cardamom, saffron and cinnamon, despite being appreciated as natural flavors, have got impressive health benefits due to functional ingredients, which are being used for the development of nutraceuticals. Findings Flavoring and coloring compounds of these products have shown positive results in the prevention of several diseases including carcinoma and neurological diseases such as Alzheimer’s and Parkinson’s. Such effects are attributed to the presence of phenolic compounds, which possesses free radical scavenging, anti-inflammatory antiviral and antimicrobial properties. These properties not only show a preventive mechanism against diseases but also makes the food product shelf-stable by imparting antimicrobial effects. Originality/value This paper highlights the opportunities to increase the use of such natural flavoring agents over synthetic aroma compounds to develop novel functional foods. Phenols, carotenoids and flavonoids are the major health-promoting components of these highly valued aroma ingredients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".