Processing Capability of Maize Varieties Through Free Sorting and CATA Methodologies and Physicochemical Characteristics
Bibliographic record
Abstract
Maize varieties have specific food processing abilities, with reference to the production of gambari-lifin, lifin, mawe and ogi, four major intermediate products in Benin. Except for the gambari-lifin, these products are widely known in the most of African countries. The recent development of gambari-lifin in relation with the maize grains quality suggests the screening of appropriate maize cultivars for minimizing failure during processing. Panelists comprising 77 maize food processors sorted fifteen maize varieties of which fourteen improved and one local ecotype, and then described each group with their own words. Additionally, 70 maize food processors performed the CATA (Check All That Apply) questions test with a list of sensory terms on the maize varieties. Furthermore, selected physicochemical and rheological parameters were determined on seven representative maize varieties. Multidimensional scaling (MDS) and hierarchical cluster analysis and multiple factorial analyses (MFA) were performed on sensory descriptors and instrumental data. Based on MDS, four groups of maize varieties were identified being specifically appropriate for one or more of these intermediate products. Grains size and weight, endosperm texture and in a lesser extent colour were the major group descriptors of maize varieties. Vitreous character or average size were positively correlated to processing yield as far as gambari-lifin is concerned while floury character was associated to “ability for pasting”. This study confirms that food processors perception is very helpful and useful tools for maize breeders since it early provides consistent information for the end-uses products.
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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 | 0.000 | 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".