Oxygen Concentration Affects Chlorophyll Fluorescence in Chlorophyll-containing Fruit and Vegetables
Bibliographic record
Abstract
A new chlorophyll fluorescence (F) sensor system called FIRM (fluorescence interactive response monitor) was developed that measures F at low irradiance. This system can produce a theoretical estimate of Fo at zero irradiance for which we have coined a new fluorescence term, Fα. The ability of Fα to detect fruit and vegetable low-O2 stress was tested in short-term (4-day) studies on chlorophyll-containing fruit [apple (Malus sylvestris (L.) Mill. var. domestica (Borkh.) Mansf.), pear (Pyrus communis L.), banana (Musa ×paradisiaca L.), kiwifruit (Actinidia deliciosa C.S. Liang & A.R. Ferguson), mango (Mangifera indica L.), and avocado (Persea americana Mill.)] and vegetables (cabbage (Brassica oleracea L. Capitata Group), green pepper (Capsicum annuum L. Grossum Group), iceberg and romaine lettuce (Lactuca sativa L.)). In all of these fruit and vegetables, Fα was able to indicate the presence of low-O2 stress. As the O2 concentration dropped below threshold values of 0 to 1.4 kPa, depending on the product, the Fα value immediately and dramatically increased. At the end of the short-term study, O2 was increased above the threshold level, whereupon Fα returned to approximately prestressed values. A 9-month study was undertaken with `Summerland McIntosh' apple fruit to determine if storing the fruit at 0.9 kPa O2, the estimated low O2 threshold value determined from Fα, would benefit or damage fruit quality, compared with threshold + 0.3 kPa (1.2 kPa O2) and the lowest recommended CA (1.5 kPa O2). After 9 months, the threshold treatment (0.9 kPa) had the highest firmness, lowest concentration of fermentation volatiles (ethanol, acetaldehyde, ethyl acetate) and lowest total disorders. Sensory rating for off-flavor, flavor and preference indicated no discernible differences among the three treatments.
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 | 0.000 |
| 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".