Journal of Agricultural Science, Vol. 1, No. 2, December 2009, all in one file
Bibliographic record
Abstract
Physical methods for mechanically in-shell Brazil nut sorting by color, size, density and inner deterioration were developed to assess nut quality and reduce aflatoxin contamination.Nuts were able to be sorted by color utilizing the standard shell chromaticity components L*, a* and b* at ranges of 31.51 to 48.64, 4.09 to 10.07 and 11.90 to 22.95, respectively.It produced a homogeneous color batch and segregated the off-standard ones (darkest, lightest and stained).By size sorting utilizing three oval sectioned trays with the following dimensions 40x25, 35x23 and 20x19.5 mm [length x width] and vibration, nuts were sorted into three sizes Groups: I, II and III for large, medium and small, respectively.Through density sorting the light nuts (shell/nut <1.5) which have inner mass reduction by fungi growth and/or dehydration, were separated with two compressed air streams from the healthy ones.Utilizing near infrared (NIR) spectrophotometry, the nuts inner deterioration was detected, with no need of de-shelling them at the wavelength range of 2200 to 2500 nm.Any nut measurement detected, lower or higher than those sorting settings, was considered off-standard and rejected.Pools of the final standard and off-standard nuts were analyzed for aflatoxins by LC-MS/MS.No toxin was detected in the final standard batch up to the method LOQ (0.08, 0.09, 0.10 and 0.12 μg/kg for AFB 1 ,AFB 2 , AFG 1 , AFG 2 , respectively).On the other hand, the off-standard rejected batch had 16.4 μg/kg of AFB 1 .These methods are important tools for building an in-shell Brazil nut sorting machine to assess nut quality and reduce aflatoxin contamination.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.581 | 0.498 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".