Role of thermal and non‐thermal drying techniques on drying kinetics and the physicochemical properties of shiitake mushroom
Bibliographic record
Abstract
BACKGROUND: Fresh shiitake mushrooms are rich in nutrients, but have high water content, a fast metabolism after harvest, and deteriorate extremely easily. Therefore, the drying of shiitake mushrooms has become a research direction. However, the role of different drying techniques on shiitake mushroom quality is limited. Therefore, the purpose of this study was to investigate the effect of thermal and non-thermal drying on the drying kinetics, and the physicochemical properties of the end product. RESULTS: Results showed that shiitake mushroom treated with non-thermal drying (vacuum freeze-drying) had an attractive color, low shrinkage, and uniform honeycomb structure, while the drying time was the longest and not conducive to the formation of shiitake mushroom aroma. But shiitake mushroom treated with thermal drying presents an attractive fragrance. In thermal processing technology, compared with hot air convection drying (HAD), infrared hot air convection drying (IRHAD) shortens the drying time by 37.5%, and had the highest oxidation resistance, polysaccharide content and the lowest color change. Relative-humidity drying (RHD) samples had the lowest shrinkage compared with other thermal processing technology. The five polysaccharides exhibited similar preliminary structural characteristics, but the polysaccharides obtained by IRHAD have the highest antioxidant properties. CONCLUSION: These results showed that compared with thermal drying technology, non-thermal drying technology is not suitable for shiitake mushroom processing. In thermal processing technology, IRHAD is a potential drying method to obtain high-quality dried shiitake mushrooms and shiitake mushroom polysaccharide (SMP). However, it is necessary to increase the pretreatment technology to achieve the attractive appearance of non-thermal drying technology. © 2021 Society of Chemical Industry.
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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 | 0.001 |
| 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".