Accurate prediction of nutritional value of sorghum grain using image analysis
Bibliographic record
Abstract
1. This study evaluated the application of L (lightness)*a (redness) and *b (blueness) colour analysis and chemical compositions to predict the nutritional value of sorghum grain.2. A total of 12 varieties of sorghum grain were analysed for L*a*b colours, chemical composition, energy and total and digestible amino acid content. Regression models based on the linear, non-linear and the interaction effects of inputs were applied to predict the nutritional value of sorghum grains either using L*a*b colour or chemical composition, as the model inputs.3. The results illustrated a significant relationship between a*b and/or chemical compositions with energy content in the samples of sorghum grain. The provided estimation equations presented high goodness of fit in terms of R2adj ranging from 0.744 to 0.999.4. Total and digestible amino acids of sorghum grain were estimated based on a*b and chemical compositions data with the goodness of fit ranging from 0.641 to 0.999 (R2adj).5. In conclusion, the L*a*b colour analysis may be used for developing equations to predict nutritional value of sorghum grain as an alternative approach to the conventional time-consuming and costly chemical and bioassay methods.
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) | 0.000 | 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 | 0.000 | 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".