Canning quality of popular common bean germplasm in Eastern and Central Africa
Bibliographic record
Abstract
Common bean (Phaseolus vulgaris L.) genotypes popular in eastern and central Africa were evaluated to determine their suitability for the canning industry. The genotypes were planted at the National Agricultural Research Laboratories (NARL), Kawanda-Uganda in the second rainy seasons (July-September) of 2015, 2016 and off season of 2017 (November- February). Two samples per genotype were evaluated at the canning facilities at Kawanda and Michigan State University (MSU) using a protocol based on home canning. One sample per genotype from the 2017 harvest was evaluated at Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre (AAFC-LRDC) using the industry canning protocol. Data (n=134) was collected on seed moisture content, dry and soaked bean weight, hydration coefficient (HC) and visual quality, including colour retention, appearance, brine clarity, bean splitting and freedom starch/clumps on replicated samples. Additional data on unreplicated samples were collected on 100-seed weight, seed solids for canning, hydration coefficient after soaking (HCS), hydration coefficient after blanching (HCB), drain weight (%), matting, appearance, seed color, texture, and cooking quality traits including hard seed and partially hydrated seed (%) and HC after cooking. Analysis of variance of data from MSU and Kawanda showed significant (P≤0.01) differences among genotypes for the assessed parameters. Majority of the genotypes expressed good soaking ability considering that their HC were above the 1.8 recommended for canning and 28% combined the two mentioned traits with good overall canning quality visual rating. Apart from 26, all other varieties had good HC based on data from Canada. About 24% of genotypes belonging to various market classes consistently combined this trait with good visual quality. The most outstanding genotypes based on these traits included SAB659 (red mottled), MAC44 (red mottled), NABE21 (cream), NABE12C (cream) and VAX5 (cream), KK8 (red mottled), Bihogo (yellow) and VAX4 (black). These genotypes were superior to the white beans: MEXICO 142, Awash1, and Awash Melka, that were considered as high-quality controls. Results indicated that genotypes of diverse backgrounds, with good canning quality traits exist among the currently utilised varieties and breeding lines. This diversity could be exploited for breeding and varietal promotion in the canning industry. Key words: Common bean, canning, hydration coefficient, visual quality, drained weight
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".