The occurrence of mass transport processes during the leaching of amygdalin from bitter apricot kernels: detoxification and flavour improvement
Bibliographic record
Abstract
Summary Because of their appreciable protein content apricot kernels could be used as a good source of food. However, their wide use for human or animal nutrition is dependent on their adequate detoxification. This is because apricot kernels have a strong bitter flavour that is caused by the presence of amygdalin, a toxic cyanogenic glycoside. The aim of the present work was to investigate an unusual method of detoxifying apricot kernels by soaking them in water. The method was energetically relatively inexpensive as uncrushed kernels and cold water were used. Furthermore, the use of intact kernels minimized the loss of nutritious soluble matter during the detoxification. On the basis of a simple but adequate mechanistic model, a mathematical description of the extractive detoxification of apricot kernels is proposed. The two‐resistance mass transfer system is based on external film mass transfer and an effective solid diffusion. A linear equilibrium curve arises from the assumption that the concentration profile inside the solid phase can be well approximated with a second order polynomial calculation, this then leads to an analytical formulation of the mathematical model. The liquid‐phase mass transfer coefficient and the internal diffusion coefficient in the kernels can be then determined. Finally, the model is used to describe extractor behaviour and predict process performance under real operation conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".