Development of a food safety toolkit for dry common beans (Phaseolus vulgaris L.) in Uganda using a Hazard Analysis and Critical Control Point (HACCP) approach
Bibliographic record
Abstract
Common beans (Phaseolus vulgaris L) may be contaminated with heavy metals and aflatoxins. Cooked beans may also be contaminated with micro-organisms due to poor hygiene and sanitation practices. Hazard Analysis and Critical Control Point (HACCP), which is a globally recognised food safety program, was proposed as a suitable program to minimise/eliminate the risk of contamination. Therefore, the objective of this study was to develop a HACCP plan for dry common beans in Uganda and an accompanying food safety toolkit. The seven principles of HACCP as outlined by Codex Alimentarius were followed to develop a HACCP plan for the dry common beans value chain in Uganda. A decision tree diagram was further used to identify each potential hazard at each processing stage and Critical Control Points (CCPs) along the chain. The identification of the CCPs was further supported by an evaluation of the actual risk and severity of the hazard. For the CCP identified, reliable control mechanism and corrective actions were established to fulfill the requirements set by the critical limits to guarantee the safety of the products. Verification and records systems were proposed to determine the effectiveness and traceability of the HACCP plan. For identified CCPs, a co-creation methodology was used to develop the food safety toolkit. This was carried out in four sessions that included a background of the chain actors' ambitions to determine the suitability of the toolkit, assessment of CCPs, expert advice on the CCP and an exercise to develop concepts for each CCP. From the analysis, fourteen processing stages starting from land selection to cooking and serving were identified. Out of these, four stages were CCPs. These were land selection and preparation, storage, post-harvest drying, and cooking and serving. Hazards at the CCPs included heavy metals, mycotoxins, and micro-organisms such as S. aureus, E. coli, and Salmonella spp. A combination of good hygiene and sanitation practices and good agricultural practices were recommended as control measures against the hazards. To further equip the value chain actors with mitigation strategies, a food safety toolkit whose usefulness is to give the actors a systematic means to control identified CCPs was developed. In this regard, the toolkit and HACCP plan will complement each other. From the study results, implementation of the toolkit, followed by an assessment of its uptake and impact on livelihoods and food safety risks is recommended.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".