Impact of HACCP Based Food Safety Management Systems in Improving Food Safety of Sri Lankan Tea Industry
Bibliographic record
Abstract
A study was conducted to identify and assess the major food safety violations in low grown orthodox black tea manufacturing process while assessing impact of HACCP based food safety management system (FSMS) in tea industry. Stratified random sampling was used where qualitative data was weighted averaged against GMP requirements and converted in to quantitative values to be used in statistical analyses. The impact of HACCP based FSMS in improving food safety was evaluated using representative sample. Organization and management responsibility was strongly correlated with establishment design and facilities while quality assurance had a strong or moderate correlation with all the factors. Pest control and personal hygiene was not satisfactorily developed according to the results. Establishment design and facilities (ED&F) was the major root cause for the food hygiene problems identified where continuous attention and top management commitment as well as additional capital investments were needed to improve design and facilities of manufacturing plants in the sector. Similarly, Quality assurance systems were not in complete compliance with food safety, mostly due to the incomplete system developments, lack of expert knowledge in the industry as well as inappropriate practices. However, HACCP based FSMS have created enabling environment to improve GMP requirements while increasing food safety implementation in tea industry. Nevertheless, factories with HACCP based FSMS had better infrastructure and systematic operations with trained operators rather than factories without any HACCP based FSMS. The efficacy of processing, recording and personnel hygiene were satisfactorily improved in factories which had implemented HACCP based FSMS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".