Processing Capability of Maize Varieties Through Free Sorting and CATA Methodologies and Physicochemical Characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it