High-throughput color determination of red raspberry puree and correlation of color parameters with total anthocyanins
Bibliographic record
Abstract
BACKGROUND: Red raspberry fruit color is a key driver of consumer preference and a major target of breeding programs worldwide. Screening for fruit color typically involves the determination of anthocyanin content and/or the assessment of color through a colorimeter. However, both procedures are time-consuming when the analyses involve hundreds or thousands of samples. The objectives of this study were to develop a high-throughput method for red raspberry puree color measurement and to test the correlations between color parameters and total anthocyanin content. Color coordinates were collected with a colorimeter on 126 puree samples contained in Petri dishes and with the Tomato Analyzer Color Test (TACT) module to assess the same samples prepared in Petri dishes and in 96-well plates. An additional 425 samples were analyzed using only 96-well plates. Total anthocyanins were extracted from all 551 samples. RESULTS: = 0.93 for a*) were obtained for color parameters measured with TACT using Petri dishes and TACT using plates. Of the color parameters measured with the colorimeter, only L*, a*/b*, and hue significantly correlated with total anthocyanins (p < 0.05), but, except for L* (R = - 0.79), the correlations were weak (R = - 0.23 for a*/b* and R = 0.22 for hue). Conversely, all correlations with total anthocyanins and color parameters measured with TACT were significant (p < 0.001) and moderately strong (e.g., R = - 0.69 for L* and R = 0.55 for a*/b*). These values were indicative of darker colors as total anthocyanin content increased. CONCLUSIONS: While the colorimeter and TACT-based methods were not fully interchangeable, TACT better captured color differences among raspberry genotypes than the colorimeter. The correlations between color parameters measured with TACT and total anthocyanins were not strong enough to develop prediction models, yet the use of TACT with 96-well plates instead of Petri dishes would enable the high-throughput measurement of red raspberry puree color.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".