Fresh‐Cut Onion: A Review on Processing, Health Benefits, and Shelf‐Life
Bibliographic record
Abstract
The ready-to-eat produce market has grown rapidly because of the health benefits and convenience associated with these products. Onion is widely used as an ingredient in an extensive range of recipes from breakfast to dinner and in nearly every ethnic cuisine. However, cutting/chopping of onion is a nuisance to many consumers due to the lachrymatory properties of the volatiles generated that bring tears to eyes and leave a distinct odor on hands. As a result, there is now an increasing demand for fresh-cut, value-added, and ready-to-eat onion in households, as well as large-scale uses in retail, food service, and various food industries, mainly due to the end-use convenience. Despite these benefits, fresh-cut onion products present considerable challenges due to tissue damage, resulting in chemical and physiological reactions that limit product shelf-life. Intensive discoloration, microbial growth, softening, and off-odor are the typical deteriorations that need to be controlled through the application of suitable preservation methods. This article reviews the literature related to the fresh-cut onion, focusing on its constituents, nutritional and health benefits, production methods, quality changes throughout storage, and technologies available to increase product shelf-life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".