Development of a predictive model for ‘Lapins’ sweet cherry dry matter content using a visible/near infrared spectrometer and its potential application to other cultivars.
Bibliographic record
Abstract
This work was conducted to determine if a useful model could be developed for a portable visible/near-infrared spectrometer for non-destructively predicting dry matter content in ‘Lapins’ and other cultivars of sweet cherries. Absorptions at a range of 948–957 nm (default selected by the instrument model-building program) or 858–1008 nm (user selected) were used to create models associating absorption values with actual measured dry matter contents of cherries. The best model was created using the user selected waveband and this model was determined to be highly predictive of dry matter, with a resolution of 0.5% dry matter content. External validation of this model was carried out using three different sweet cherry cultivars (Staccato™, Sentennial™, and Sovereign™) and the model was found to be robust, i.e., quite accurate in predicting dry matter content in these other cultivars, with R2 values of 0.96, 0.94, and 0.99, and root mean square error of prediction values of 0.51, 0.74, and 0.56, respectively. The results indicate that dry matter in sweet cherries can be predicted accurately and non-destructively using visible/near-infrared spectroscopy. In the case of the cherry cultivars tested in this paper, a model developed using ‘Lapins’ fruit was reliable in predicting dry matter in these other cultivars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".