Effects of Potassium Application on Flavor Compounds of Cherry Tomato Fruits
Bibliographic record
Abstract
A pot experiment was conducted to determine the effects of potassium (K) application on volatile compounds, taste compounds, and firmness of fresh tomato fruits. Each pot was filled with 8 kg of clean sand. The experiment consisted of six K application rates with 0, 1.25, 2.5, 5.0, 10.0, and 20.0 mmol K L−1 in the nutrient solution. Volatile compounds, soluble sugars, soluble solids, titratable acidity, and firmness of fresh tomato fruits were measured. The results show that the concentrations of 3-methylbutanal, 1-penten-3-one, hexanal, cis-3-hexenal, 2-methyl-4-pentenal, trans-2-hexenal, 2E-4E-hexadienal, 6-methyl-5-hepten-2-one, phenylacetaldehyde, phenylethanol, soluble sugars, and soluble solids tended to increase at first and then decrease between 0 to 10.0 mmol K L−1. K application rate obtaining the highest values of the concentrations ranged from 1.4 to 3.0 mmol K L−1, with the exception of cis-3-hexenal (1.1 mmol K L−1), phenylacetaldehyde (4.5 mmol K L−1), and phenylethanol (4.8 mmol K L−1). By contrast, increasing K supply increased the concentration of titratable acidity, decreased the ratios of soluble sugars to titratable acidity and soluble solids to titratable acidity. Close correlations were observed between the concentrations of various volatile compounds, soluble sugars, and soluble solids. Based on contributions of these compounds to tomato flavor, we assume that moderate K supply (1.4–3.0 mmol K L−1) improves tomato flavor, whereas tomato fruits with either no K or high K fertilization have poor flavor due to having undesirable levels of flavor compounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".