New preservation and detection technologies for edible mushrooms: A review
Bibliographic record
Abstract
Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".