Effects of drying methods on quality attributes of peach (<i>Prunus persica</i>) leather
Bibliographic record
Abstract
In this article, the effect of four drying techniques namely hot air drying (AD), infrared drying (IRD), hot air-assisted radio frequency drying (RFD), and microwave-assisted hot air drying (MWD) on quality attributes of dried peach (Prunus persica) leather (PL) was investigated. Drying tests were conducted at 70°C, air velocity of 1.0 m/s and at fixed power level of 4 W/g for RFD, IRD, and MWD. Moisture distribution, texture, rehydration ratio, color, and microstructure of PL were investigated. The results showed that the samples dried by MWD had the shortest drying time (180 min) followed by IRD (210 min), RFD (210 min) and AD (300 min). Study on microstructure and flavor analysis reveals that IRD gave the best results. Sensory tests using electronic tongue and electronic nose that evaluate the odor and taste profiles of dried PL indicates that IRD produced the best quality among the four drying techniques.
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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".