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-O 2 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-O 2 stress. As the O 2 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, O 2 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 O 2 , the estimated low O 2 threshold value determined from Fα, would benefit or damage fruit quality, compared with threshold + 0.3 kPa (1.2 kPa O 2 ) and the lowest recommended CA (1.5 kPa O 2 ). 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.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".