Novel Nondestructive Technique to Determine Optimum Harvesting Stage of ‘Ataúlfo’ Mango Fruit
Bibliographic record
Abstract
A portable spectrometer was validated to determine optimum harvesting stage of ‘Ataúlfo’ using dry matter and skin color as fruit indicators. To build the model, samples were collected as follows: a. Unripe; b. Green Mature 1; c. Green Mature 2; d. Green Mature 3; and e. Fully mature. Fruit were scanned with a near infrared spectrometer at three temperatures (15, 25, and 35 °C). Skin color (‘a’ value) was measured with a Minolta 400 colorimeter. DM was attained in a conventional oven by drying samples for 72 h at 60 °C. Model was built and validated three times. The best model linearity was obtained on skin color ‘a’ (R2 = 0.98), whereas for DM the R2 was only 0.70. For the first validation, the best predicted value was skin color ‘a’ with an R2 = 0.9144, followed by DM with an R2 = 0.7056. On the second validation, the adjusted predicted value for skin color ‘a’ had an R2 = 0.8798, while DM had an R2 = 0.4445. When comparing NIR versus Heat Units Accumulation, in Nayarit, ‘Ataúlfo’ skin color average difference between the spectrometer vs the colorimeter was only -0.04. For ‘Ataúlfo’ from Sinaloa, skin color average difference was only -0.06, but the correlation was higher (R2 = 0.90). In conclusion, measuring skin color with the NIR spectrometer has potential as a nondestructive technique to determine the optimum harvesting stage of ‘Ataúlfo’ mango.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".