Multivariate Approach to the Measurement of Tomato Maturity and Gustatory Attributes and Their Rapid Assessment by Vis−NIR Spectroscopy
Bibliographic record
Abstract
Standard methods for determining quality and maturity are time- and labor-consuming and generally measure individual criteria at a specific time, without considering relationships among quality parameters. To propose a rapid and nondestructive analysis method describing multidimensional quality variables, an experiment was undertaken with mature green to overripe tomato fruits found on the North American retail markets. Factor analysis was used to analyze results. Four factors were considered, representing 81% of total variance. The first one, tomato maturity stage (TMS), is related to color, lycopene content, firmness, titratable acidity (TA), pH, and soluble solids (SS). Nondestructive rapid assessment by vis-NIR spectroscopy can predict TMS (r(2)=0.93). Factors 2 and 3 are both related to taste and should be considered simultaneously. Factor 2, called the gustatory index, is linked to electrical conductivity (EC), SS, TA, and pH. Factor 3, defined by SS, can be directly measured by a refractometer. Four categories of taste are proposed; the most desirable one ranks high both in soluble solids (above 4.5 degrees Brix) and in gustatory index (above 0). It was not possible to measure the gustatory index by vis-NIR spectroscopy (r(2)=0.17), but it can be estimated by EC, using a simple formula. The proposed limit between high and low gustatory index then corresponds to an EC of 5.4 mS/cm. Factor 4, variety, mostly discriminates the pink tomato type and field-grown samples from other varieties.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".