Detecting color differences in lentil flour samples using an inexpensive, hand-held colorimeter
Bibliographic record
Abstract
ABSTRACT Our lentil breeding program was interested in determining the effects of the red lentil genotype in processed food products’ colour intensity. This required a colorimeter that could accurately assess colour from the CIE L*a*b* colour space on a small sample of milled flour. Flour colour is commonly assessed with industry standard HunterLab colorimeters that require a large sample size (∼6g flour/sample). In recent years a new spectrophotometer (Nix) has become available. It was designed to quickly and accurately obtain CIEL*a*b* values for everyday items and requires a small amount of sample for analysis (0.5 g flour/sample), but its use has not been tested in milled flour products. To test whether this instrument was appropriate for milled flour colour, eight red lentil genotypes were evaluated for red colour using both instruments. Mean colour scores with Nix (a* and b*) were comparable to those obtained with the HunterLab (r 2 = 0.9-0.95 ), L* scores were less comparable (r 2 = 0.58-0.61 ). Both instruments were able to differentiate and rank red colour intensity in milled flour from the different red lentil genotypes. Nix procedures are more repeatable and use less sample than HunterLab validating its use for assessing red colour intensity in lentil flour. Research Highlights Companies making foods using red lentil flour seek red lentil genotypes that exhibit red colour intensity. Nix Spectro2 is an inexpensive photospectrometer made for small volume samples. It has not been tested on flour products. The selected red lentil genotypes tested showed a range of red colour intensities. Nix Spectro2 performance was comparable to industry standard HunterLab colorimeter.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".