Effect of processing on vital chemical components of button mushroom
Bibliographic record
Abstract
Abstract The processing conditions for mushroom ( Agaricus bisporous ) were optimized for optimal nutrient retention. Pretreatments, namely blanching (hot water, frying, microwave, and steam blanching) and osmotic dehydration were considered as variables at different temperatures and salt concentrations (5–20%). After blanching, the different times of deactivation of catalase enzyme were noted for both with and without calcium chloride (2%) treatments. Phytochemicals such as total phenolic content, ascorbic acid, flavonoids, and the free radical scavenging activity were analyzed. Microwave blanching (mushroom treated with 2% calcium chloride) at 1080 W for 30 s was found to be the most appropriate among all the other types of blanching techniques. Further, osmotic dehydration was performed at different salt concentrations and time for the microwave blanched sample. Samples with 15% sodium chloride treatment for 10 min retained, the maximum nutritional compounds according to experimental analysis. Mathematical modeling using the Peleg model was used and mushrooms treated with 10% salt concentration was found to have the least root mean square error values, which was thus chosen to be the most appropriate salt concentration. The functional components of button mushroom are thus most appropriately preserved by treating them with calcium chloride (2%), microwave blanched (1080 W, 30 s), followed by osmotic dehydration (10–15% NaCl for 10 min). Practical applications Thermal processing of mushrooms is the most common technique used in the processing of mushrooms commercially. By understanding the best possible technique the nutritional losses to the product could be reduced extensively, adding to nutritional security. Mathematical modeling used could be used to generate the desired response economically for a variable set of conditions for mushroom processing.
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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".