Osmotic dehydration of waxy skinned berries - a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Waxy skinned berry fruits contain essential nutrients and play an important role in human health and nutrition. However, these fruits encounter two major problems; firstly, they are extremely perishable in nature that limits their consumption throughout the year and to address this bottleneck, osmotic dehydration (OD) can be an efficient way which brings the second problem associated with berries. The waxy skin of some valuable berries acts like a barrier and reduces the mass transfer (MT) during OD. The restricted MT during OD can be improved by applying pretreatments, accelerators during OD as well as optimizing OD parameters. Therefore, this review is first of its kind aims to provide a comprehensive discussion on the concept of OD, pretreatments and processing parameters associated with the improved MT in the waxy skinned berries. In terms of maintaining the sensory attributes, innovative non-thermal pretreatments are found better than classical thermal, mechanical, and chemical/enzymatic pretreatments. Moreover, factors affecting the MT rate and efficiency of OD processes, along with mathematical and computational models to optimize OD of waxy skinned berries are given. The advantages of OD processes on the quality of the products, in terms of bioactive compounds, texture, sensory properties, and color, were additionally summarized. The novelty of this review lies in its exclusive focus on the OD of berry fruits with a waxy layer on their surface and the strategies to ease off the process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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