Intensive pulsed light pretreatment combined with controlled temperature and humidity for convection drying to reduce browning and improve quality of dried shiitake mushrooms
Bibliographic record
Abstract
BACKGROUND: The change of surface color caused by browning during the drying process of shiitake mushrooms seriously affects its market circulation. Intensive pulsed light (IPL) as a non-heat-treatment method can reduce enzyme activity by changing the enzyme structure. Therefore, in this study, the use of IPL pretreatment before drying was aimed to reduce the adverse reactions caused by the browning reaction during the drying processing of shiitake mushrooms. RESULTS: Shiitake mushrooms pretreated with 25 pulses of IPL energy of 400 J reduced the initial polyphenol oxidase enzyme activity, the browning index, and browning degree values by 42.83%, 43.02%, and 47.54% respectively. The IPL pretreatment enhanced the polysaccharides and reducing sugars contents and it reduced 5-hydroxymethylfurfural generation in the dried shiitake mushrooms. The pretreatment also improved the surface color, the antioxidant activity, and retained the umami taste characteristics in the dried shiitake mushroom. CONCLUSION: The IPL pretreatment combined with controlled temperature and humidity for convection drying could be a suitable method to improve the quality of dried shiitake mushrooms. Therefore, this study provides a new pretreatment method for materials that are prone to browning during drying. © 2021 Society of Chemical Industry.
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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".