Intrinsic and extrinsic factors influencing <i>Bacillus cereus</i> spore inactivation in spices and herbs: Thermal and non‐thermal sterilization approaches
Bibliographic record
Abstract
The presence of Bacillus cereus in spices and herbs has posed a detrimental effect on food safety. The absence of thorough testing, comprehensive reporting, and vigilant surveillance of the illness has resulted in a significant underestimation of the true prevalence of foodborne illness caused by B. cereus. B. cereus spores are resistant to thermal processing (superheated steam, microwave, radiofrequency, infrared) that remains a significant challenge for the spice industry. Non-thermal techniques, such as cold plasma, gamma irradiation, and electron beam irradiation, have gained significant interest for their ability to inactivate B. cereus spores. However, these technologies are constrained by inherent limitations. The composition of B. cereus spores, including dipicolinic acid, divalent cations, and low water content in the core, contributes significantly to their resistance properties. This review delves into the different factors that impact B. cereus spores in spices and herbs during sterilization, considering both intrinsic and extrinsic factors. This review also discussed the various techniques for inactivating B. cereus spores from spices and highlighted their effectiveness and constraints. It also provides valuable insights for enhancing sterilization strategies in the spices and herbs industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".