Construction and optimization of quantitative analysis models for pigments in broccoli (Brassica oleracea L. var. italica) based on near-infrared spectroscopy technology
Bibliographic record
Abstract
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R 2 of 0.992, RMSEC of 0.478 mg g −1 DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R 2 of 0.976, RMSEC of 0.098 mg g −1 DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R 2 of 0.790, RMSEC of 1.777 units g −1 DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
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 | 0.001 | 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 | 0.000 | 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".