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 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) | 0.001 | 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 | 0.001 | 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".