Characterization of isotherms and thin-layer drying of red kidney beans, Part I: Choosing appropriate empirical and semitheoretical models
Bibliographic record
Abstract
Desorption and adsorption isotherms and drying characteristics of red kidney beans were studied using static and dynamic methods, respectively. The desorption and adsorption isotherms were determined at 60, 50, 40, 30, 20, and 10°C with 32–91% relative humidity (RH). The constant RHs were generated using six saturated salt solutions at constant temperatures. The drying characteristics were determined using a thin-layer dryer with drying air at 50, 40, and 30°C with 35 and 50% RH. The dimensions of the kidney beans before and after drying were measured and shrinkage and sphericity of the beans were calculated. A new method to evaluate the best-fitted equation to characterize the thin-layer drying data was developed. The best-fitted equations to describe the desorption and adsorption isotherms were the modified Chung–Pfost and modified Guggenheim–Anderson–deBoer. The red kidney beans only experienced a falling rate drying period and had a largest shrinkage in the length direction during drying. The Henderson and Pabis model and the modified Page model were the best-fitted models to describe the thin-layer drying data. Using only the values of R2 and mean squared error to evaluate the semitheoretical and empirical models might not be enough. The method developed in this study could help develop a semitheoretical or empirical model with a higher accuracy of drying constant, which could be used to estimate the effective water diffusivity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".