Non-destructive measurement of tomato quality using visible and near-infrared reflectance spectroscopy
Bibliographic record
Abstract
Experiments were conducted to assess the feasibility of determining the quality attributes of tomato (Lycopersicon esculentum Mill cv 'DRK 453' and 'Trust') based upon visible/near-infrared reflectance (VIS/NIR) spectroscopy. A partial least squares regression (PLS) method was used to build prediction models. Excellent prediction performance was achieved for lycopene content (LC), colour value a*/b* ratio, tomato colour index (TCI), and firmness. Coefficient of determination (R2) for each of the parameters was respectively 0.96, 0.99, 0.99, and 0.97. All these R2 were significant at 1% level. The root mean square errors of prediction (RMSEP) for all the parameters were low indicating the high quality of the fit of the prediction models. The values were 2.15, 0.06, 1.52, and 1.44 for LC, a*/b* ratio, TCI, and firmness, respectively. However, the models for prediction of titratable acidity, soluble solids content (SSC) and acid-Brix ratio showed relatively poor reliability, with R2 value of 0.49, 0.03 and 0.65, and RMSEP of 0.43, 0.15 and 0.08, respectively. Further, a model built by the PLS2 method showed good performance in simultaneously predicting a*/b* ratio, TCI, firmness, and LC of tomato, with R2 values of 0.99, 0.99, 0.97, and 0.92, and RMSEP of 0.06, 1.75, 1.44, and 3.03, respectively. Once again here all the R2 values were significant at 1% level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".