Dry Matter Content and Stability of Carotenoids in Kale and Spinach During Drying
Bibliographic record
Abstract
Drying of spinach ( Spinacia oleracea L.) and kale ( Brassica oleracea L. var. acephala D.C.) is required to determine percentage of dry matter (%DM) and pigment concentration of fresh leaves. ‘Melody’ spinach and ‘Winterbor’ kale were greenhouse-grown in hydroponic nutrient solutions containing 13 or 105 mg·L −1 N. Using vacuum freeze dryers and convection ovens, plant tissues were dried for 120 h at five different temperature treatments: 1) freeze drying at −25 °C; 2) freeze drying at 0 °C; 3) vacuum drying at +25 °C; 4) oven drying at +50 °C; and 5) oven drying at +75 °C. Spinach leaf tissue %DM was affected, but kale %DM was unaffected by drying temperature. Spinach and kale leaf tissue %DM were both affected by N level. The high N spinach decreased from 7.3 to 6.4%DM when drying temperature increased from +25 to +75 °C. The low N spinach decreased from 12.7 to 9.6%DM as the drying temperature increased from −25 to +50 °C. Kale averaged from 14.8%DM for the high N treatment and from 21.8%DM for the low N treatment. However, drying temperature did not have a significant impact on measured %DM in kale. Lutein, β-carotene, and chlorophyll levels for both spinach and kale leaf tissue were affected by drying temperature. Measured concentrations of all pigments decreased over 70% as the drying temperature increased from −25 to 75 °C. The largest pigment fresh and dry weight concentrations for spinach and kale were measured at drying temperatures below +25 °C. The spinach and kale samples dried between −25 and +25 °C were not significantly different from each other in %DM or pigment concentration measured on a dry or fresh weight basis. Thus, drying leaf tissue for accurate pigment analysis requires temperatures below +25 °C using vacuum or freeze drying technology.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".