Nutritional, Microbial, and Allergenic Changes during the Fermentation of Cashew ‘Cheese’ Product Using a Quinoa-Based Rejuvelac Starter Culture
Bibliographic record
Abstract
Fermentation has been applied to a multitude of food types for preservation and product enhancing characteristics. Interest in the microbiome and healthy foods makes it important to understand the microbial processes involved in fermentation. This is particularly the case for products such as fermented cashew (Anacardium occidentale). We hereby describe the characterisation of cashew samples throughout an entire fermentation production process, starting at the quinoa starter inoculum (rejuvelac). The viable bacterial count was 108 -109 colony forming units/g. The nutritional composition changed marginally with regards to fats, carbohydrates, vitamins, and minerals. The rejuvelac starter culture was predominated by Pediococcus and Weissella genera. The ‘brie’ and ‘blue’ cashew products became dominated by Lactococcus, Pediococcus, and Weissella genera as the fermentation progressed. Cashew allergenicity was found to significantly decrease with fermentation of all the end-product types. For consumers concerned about allergic reactions to cashew nuts, these results suggested that a safer option is for products to be made by fermentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".