Effect of Chemical Pretreatment on Drying Kinetics and Physio-chemical Characteristics of Yellow European Plums
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Bibliographic record
Abstract
Drying of plums to prunes is an important postharvest processing step, as it results in a product with higher nutrient density, increased shelf life, and significantly greater antioxidant and fiber content. However, due to the waxy layer present on the plums surface having low permeability toward moisture, plum dries very slowly which is an energy-demanding process. Therefore, to breakdown waxy layer on the surface and enhancement of skin moisture diffusivity, two genotypes (V91074 and V95141) of Yellow European Plums (YEPs) were dipped in 1% (w/v) of Ascorbic Acid (AA), Citric Acid (CA), and Potassium Meta-bisulfite (KMS) solution for 1 min at 40°C. The pretreated YEPs were dried at three different temperatures (50°C, 60°C, and 70°C) until a final moisture content of approximately 30% (wet basis) was reached. It was observed that treated samples (AA and KMS) dried faster as compared to untreated samples, except for CA treatment where no significant difference in drying time was observed. One model cannot be selected for describing the thin layer drying characteristics of YEPs. Five out of 11 models used were found to be a perfect fit for genotype V91074 and genotype V95141, respectively. Pretreatment had a significant effect on effective moisture diffusivity (Deff). Deff for untreated and treated plum samples ranged between 4.6 × 10−11 to 8.6 × 10−11 (m2/s) and 4.9 × 10−11 to 9.1 × 10−11 (m2/s). The drying temperature had a significant effect on phenolic content and antioxidant activity, whereas no significant effect of pretreatment was observed.
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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 | 0.000 |
| 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 it