FOAM‐MAT FREEZE‐DRYING OF APPLE JUICE PART 1: EXPERIMENTAL DATA AND ANN SIMULATIONS
Bibliographic record
Abstract
ABSTRACT Freeze‐drying of foamed and nonfoamed apple juice was studied in order to assess if there is a reduction in process time due to foaming. Foams were prepared by whipping apple juice with methylcellulose or egg albumin at different concentrations. Foamed and nonfoamed juice samples having different thickness and different initial weight were frozen at −40C and then freeze‐dried at 20C during 48 h under vacuum. Sample weight loss and temperature were followed at different process times. A mathematical model based on artificial neural networks was developed to represent foam kinetics and temperature curves during freeze‐drying. Foaming reduced process time if the comparison was done at equal sample thickness. However, lower density of foamed materials decreases weight load to the dryer. Unfortunately, the optimization of the process did not permit the determination of a practical minimal foam sample thickness to enhance both drying rate and dryer throughput. PRACTICAL APPLICATIONS Fruit juice powders have a large application in the food and nutraceutical industries. These powders are used as instant beverages, ingredients for bakery or extruded products and to incorporate in pharmaceutical tablets. Freeze‐drying is an excellent process to obtain a high‐quality fruit juice powder because it offers extraordinary nutritional, structure and sensorial qualities when compared with products of alternative drying process: air, vacuum, microwave and osmotic drying. However, the process cost is expensive due to the long drying times under vacuum. Process acceleration through optimization is therefore necessary in order to obtain high quality in the final products but at lower costs. This study aims to decrease the cost of the freeze‐drying process by using foaming prior to processing to increase the drying rate.
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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 | 0.001 |
| 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 | 0.001 | 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".