Modeling the kinetics, energy consumption and shrinkage of avocado pear pulp during drying in a microwave assisted dryer
Bibliographic record
Abstract
Abstract Drying kinetics, energy utilization (EU) and shrinkage level of avocado pear pulp during drying were investigated and modeled to determine the condition that enhances the quality of the dried product. Drying was carried out using a microwave assisted dryer with data lodger. The system was set at a constant power of 200 W, air velocity of 1.4 m/s, and temperatures of 50, 60 and 70 °C with pulp thickness being 5 mm. Fifteen thin-layer drying models, five non-linear shrinkage models and ANN methods were tested for describing the drying behaviour of avocado pulp using statistical parameters. The results revealed that drying took place in the falling rate period with the above temperatures reducing the moisture content of the pulp from 64.12 to 2.16 % wet basis within 15,360, 11,520 and 5,130 s, respectively. The drying rate and effective diffusivity increased with increase in temperature and ranged from 6.05 × 10 −3 to 1.70 × 10 −2 kg/kgs and 3.11 to 9.34 × 10 −9 m 2 /s, respectively. The activation energy of the pulp was 50.34 kJ/mol. Among the drying models tested, Page and Aghashilo models provided the best statistical parameters for describing the drying behaviour of the pulp, while ANN demonstrated great ability to predict MR and SR more accurately with high and low R 2 and RMSE. A non-linear shrinkage model developed also had the best fit qualities for describing the shrinkage behaviour of the pulp. The energy utilized (EU) , specific energy utilized (S EU ) , heat transfer coefficient (h tc ) and mass transfer coefficient (M tc ) of the pulp ranged from 7.36 to 3.19 kWh, 11.21 to 5.76 × 10 −2 Wh/kg, 0.1054 to 7.98 × 10 −7 W/mK and 2.06 to 4.28 × 10 −6 m/s respectively and were statistically (5 %) influenced by temperature. The EU model developed had the best description behaviour of the energy relationship with other factors, having high R 2 and low RMSE and SSE values.
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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 | 0.000 |
| 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".