Effect of Hot Air Thin Layer Drying Temperature on Physicochemical and Textural Properties of Dried Horseradish
Bibliographic record
Abstract
The horseradish (Armoracia rusticana) is an important crop as ingredients for many commercial food products and medicinal uses. However, the horseradish is perishable and preservation of this crop for supplying year-round is necessary. Drying can be used to preserve the horseradish but establishing a suitable drying method and conditions are important for product quality of dried horseradish. The objective of this study was to investigate the effects of hot air-drying temperatures on the physicochemical and textural properties of dried horseradish samples to determine the appropriate hot air-drying conditions. The sliced horseradish samples were dried at 55, 65, 75, 85 and 95 ℃ using a conventional hot air drier. The physicochemical and textural properties of the dried horseradish samples were determined, and the results were compared for different drying temperatures. The ANOVA analysis indicated that the drying temperature affected the physicochemical and textural properties of dried horseradish samples significantly (p<0.05). The experimental results indicated that the moisture content (9.18-6.15%,wb), aw (0.06-0.03), porosity (31.47-12.13%) and rehydration ratio (82.74 -44.47%) decreased and piece density (0.63-0.84 g/mL), hardness (193.63 -298.31N), cohesiveness (1.04-1.32), gumminess (202.13-390.12) and chewiness (635.08-1223.55) of dried horseradish samples increased with the drying temperature. Power law model indicated that the porosity was linearly corelated to the rehydration ratio of dried horseradish samples. The overall results revealed that relatively lower (55-65 ℃) drying temperature range was suitable to keep the quality of the dried horseradish samples. The findings of this study are expected to be helpful for the commercial drying of horseradish samples using hot air-drying method.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".