Kinetics of cashew apple drying through mechanistic models and analysis of the effects of drying conditions on the retention of bioactive compounds
Bibliographic record
Abstract
Abstract Global cashew nut production is nearly 4 million tons per year, valued at 7 billion US dollars. Remarkably, almost the entire cashew apple crop, amounting to 20 million tons annually, goes to waste. However, the cashew apple contains valuable nutraceutical compounds, including tannins, polyphenols, and carotenoids, estimated to be worth 150 million US dollars annually. Due to the highly perishable nature of cashew apples, degradation is a significant issue. In response, the current work has established drying as an effective preservation technique for these bioactive components. The effect of drying temperature on bioactive compounds has been thoroughly investigated. The non‐random two liquid (NRTL) activity coefficient model effectively captures the thermodynamics of the drying process. To facilitate the selection and design of drying equipment, two mechanistic mass transfer models were developed. The first model employs the Maxwell‐Stefan framework to account for internal diffusion, with external mass transfer resistance appearing as a boundary condition. While this model works well for products like grapes, it proved inadequate for explaining the drying behaviour of cashew apples. Consequently, a second model was developed, postulating rapid moisture transport by capillary action within the cashew apple. This model effectively captures the effects of a wide range of operating conditions, using only external mass transfer resistance as the tuneable kinetic parameter. This mechanistic model is more suitable for dryer design compared to conventional phenomenological models like the logarithmic model and the two‐term exponential model.
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 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 | 0.000 | 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) | 0.000 | 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".