FOAM‐MAT FREEZE‐DRYING OF APPLE JUICE PART 1: EXPERIMENTAL DATA AND ANN SIMULATIONS
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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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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 it