Nondestructive Measurement of Fresh Tomato Lycopene Content and Other Physicochemical Characteristics Using Visible−NIR Spectroscopy
Bibliographic record
Abstract
Measurement of fresh tomato fruit overall quality, and particularly lycopene content, is challenging in the context of high-volume production. An experiment was conducted to simultaneously measure various quality parameters of tomato in a nondestructive manner using vis-NIR reflectance spectroscopy and chemometrics. The sampling set included different cultivars that are obtainable from both retailers' shelves and two greenhouse producers. Results indicate that lycopene content was accurately predicted [r(2) = 0.98; root mean square error of cross-validation (RMSECV) = 3.15 mg/kg], along with color variables such as Hunter a (r(2) = 0.98), L, and b (r(2) = 0.92). Tomato color index (TCI) was better predicted (r(2) = 0.96) than the a/ b ratio (r(2) = 0.89). Firmness prediction, with an r(2) of 0.75, is comparable to what is reported in the literature for other fruits and may have a practical interest. Prediction of internal quality such as pH, soluble solids, titratable acidity, and electrical conductivity was less accurate, partly due to a low variability of these parameters among samples. Predictions were robust with regard to cultivars, except for pink variety tomato. The 400-1000 nm range gave results almost as accurate as the 400-1500 nm range.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".