Nutritional Characterization Based on Vegetation Indices to Detect Anthocyanins, Carotenoids, and Chlorophylls in Mini-Lettuce
Bibliographic record
Abstract
When obtaining new cultivars or monitoring the nutritional composition of lettuce, new techniques are necessary given the high cost and time required to conduct laboratory analyses of plant composition by conventional methods. The objective of this study was to evaluate different vegetation indices for the estimation of anthocyanin, chlorophyll, and carotenoids in mini-lettuce genotypes with different leaf colors and different typologies from red, green, and blue (RGB) images. The contents of pigments were evaluated in 15 lettuce genotypes, in addition to the soil plant analysis development (SPAD) index and vegetation indices in the visible range. The variability among genotypes was confirmed by the Scott-Knott test (p < 0.05) and multivariate analysis. Linear regressions were obtained between the green leaf index (GLI) and leaf pigments. GLI was a good predictor for estimating the contents of anthocyanin (r = −0.83; r2 = 0.75), carotenoid (r = −0.59; r2 = 0.43), chlorophyll a (r = −0.69; r2 = 0.48), chlorophyll b (r = −0.62; r2 = 0.39), and total chlorophyll (r = −0.77; r2 = 0.65) in red and green mini-lettuce. The high-performance phenotyping technique can be used to evaluate leaf pigments in breeding programs, as well as in crops for monitoring biofortification levels in lettuce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".