Untargeted metabolomic analysis of strawberries exposed to pulsed electric fields and cold plasma before postharvest storage
Bibliographic record
Abstract
After being harvested, strawberries experience a decline in nutrients and anthocyanins, which is further exacerbated by their vulnerability to plant pathogen-related decay. As postharvest losses encompass up to 50 % of total production, the development of a physical method complementary to refrigeration to reduce these losses is a goal pursued globally, given a global market of US$19 B per year. In this study, two non-thermal technologies, pulsed electric fields (PEF) and cold plasma (CP), were used to evaluate their effectiveness in maintaining phytochemical integrity in exposed strawberries. A PEF treatment of 1 pulse at 1 kV/cm field strength in 3 L of tap water could significantly alter the volatile and metabolomic composition of the fruit, while simultaneously reducing its firmness during cold storage. However, subjecting the fruit to a CP treatment at 15 % (210 watts) for 1 min did not impact the quality parameters. Furthermore, unlike the PEF treatment, the strawberries retained their firmness during storage and exhibited a consistent volatile and metabolomic profile. Based on these results, CP treatment enhances firmness and maintains the compounds found in strawberries, meanwhile, while PEF treatment might not be ideal for preserving the physicochemical parameters of fruit
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".