Improving Dehydration Efficiency and Quality in Highbush Blueberries via Combined Pulsed Microwave Pretreatment and Osmotic Dehydration
Bibliographic record
Abstract
The impact of processing time, temperature, and sample on solution ratio parameters, along with pulsing microwave pretreatment, was assessed in the osmotic dehydration of waxy skin highbush blueberries. Fresh blueberries were pre-treated with 20% microwave power for 90 s before being subjected to osmotic dehydration for 8 h in a 60 °Brix sucrose solution, with three different sample to solution ratios (1:4, 1:7, and 1:10). Changes in water loss, solid gain, total anthocyanin content, total phenolic content, and total soluble solid content during osmotic dehydration, as well as color and texture changes, were investigated at four temperature levels (room temperature, 60 °C, 65 °C, and 70 °C). The highest rate of reduction in the total soluble solid content in the osmotic solution was observed during the initial hours (0–4 h) of the process. The most effective combination for reducing the total soluble content of the osmotic agent involved the microwave-pretreatment of the blueberries at 70 °C, using a sample to solution ratio of 1:4, resulting in a decrease of 11.98%, compared to 7.83% for non-pretreated samples. The solid gain was found to be affected by the sample to solution ratio × temperature × pretreatment at a 1% probability level (p ≤ 0.01). The temperature, osmotic solution ratio, and microwave pretreatment interacted together to affect the quality parameters of the osmotically dehydrated blueberries, including total anthocyanin content, total phenolic content, and color. Higher temperatures, along with microwave pretreatment, showed the worst effects on the quality characteristics mentioned. Microwave pretreatment did not change the texture significantly in comparison with non-pretreated blueberry samples. The enhancing effect of microwave pretreatment and higher temperatures on the efficiency of the osmotic dehydration process was obvious. An optimized microwave pretreatment can reduce both the required processing time and temperature for the osmotic dehydration of waxy skinned blueberries, which in turn can lead to the higher quality preservation of processed blueberries and lower energy consumption. This could be especially useful for the large-scale processing of waxy skinned berries.
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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".