Use of Artificial Neural Network to Predict Temperature, Moisture, and Fat in Slab‐Shaped Foods with Edible Coatings During Deep‐Fat Frying
Bibliographic record
Abstract
ABSTRACT: An artificial neural network (ANN) was developed to predict heat and mass transfer during deep‐fat frying of infinite slab‐shaped foods coated with edible films. Frying time, slab half‐thickness, film thickness, food initial temperature, oil temperature, moisture diffusivity of food and film, fat diffusivity through food and film, thermal diffusivity of food, heat transfer coefficient, initial moisture content of food, and initial fat content of food (mf o ) were inputs. Temperature at the center (T 1 ), average temperature (T ave ), fat content (mf ave ), and moisture content (m ave ) of food were outputs. Four ANNs with 50 nodes each in 2 hidden layers with learning rate = 0.7 and momentum = 0.7 provided most accurate outputs, that is maximum absolute errors for T 1 and T ave were < 1.2 °C, < 0.004 db for m ave , and < 0.003 db for mf ave . The predictions of mf varied linearly with mf.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".