Modeling and optimization of orange peel drying using thin-layer equations and artificial neural networks for standardized powder production
Bibliographic record
Abstract
• Microwave drying significantly reduced the drying time of orange peel. • The Wang and Singh model best fits the microwave drying kinetics. • The ANN model accurately predicts the moisture ratio of orange peel. • Microwave drying retained more color and functional properties. • Drying affected the bioactive compounds of orange peel. Globally, about 32 million tons of nutrient-rich orange peels are wasted annually due to the lack of optimized drying methods for efficient preservation and utilization. The present study addresses this gap by investigating the drying kinetics of orange peels were studied under convective hot air drying (CHAD, 90°C, 5 h) and microwave drying (MD, 180 W, 75 min) to optimize process parameters and improve the quality of dried powders for high-value applications. The results showed that MD significantly decreased drying time compared to CHAD. The drying data were analyzed using five thin-layer models. The Wang & Singh model best fitting the MD data and the logarithmic model best fitting the CHAD data. Additionally, moisture ratio predicted using a multi-layer feedforward artificial neural network (ANN) with backpropagation yielded high R 2 values for CHAD and MD, confirming the accuracy of model. Importantly, MD allowed superior retention of color and antioxidant properties of orange peels compared with CHAD, while requiring shorter drying time. This study presents a practical approach to sustainably valorizing citrus waste by developing optimized drying protocols that integrate experimental kinetics with machine learning predictions.
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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 | 0.000 | 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".