Fresh‐Cut Onion: A Review on Processing, Health Benefits, and Shelf‐Life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it