Changes in Soluble Sugar Accumulation and Activities of Sucrose-Metabolizing Enzymes during Fruit Ripening of Jackfruit
Bibliographic record
Abstract
Jackfruit (Artocarpus heterophyllus Lam.) is an important food crop widely grown in the tropical region. However, little is known about sugar metabolism during fruit ripening of jackfruit. Here we examined sugar profiles (sucrose, glucose and fructose) and corresponding enzyme activities (SPS, E.C.2.4.1.14; SuSy, EC 2.4.1.13; IV, EC 3.2.1.26) of four soft type and four firm type varieties of jackfruit during four stages of fruit ripening. We found that during fruit ripening, there was a rapid increase in contents of total soluble sugar and sucrose, whereas increases in glucose and fructose contents were much slower. Ratios of glucose versus fructose varied among different varieties and ripening stages but within the range of 0.9 to 1.2 in the ripe fruits. Five of these varieties exhibited markedly high levels of SuSy activity for sucrose synthesis at early ripening stage, and then decreased towards fully ripe stage. All soft type varieties exhibited a conspicuous peak of AIV activity and had overall higher AIV activities than NIV during ripening. The changing patterns for other enzymes varied among varieties. Our studies support the notion that sucrose was the major sugar species contributing to the fruit sweetness, followed by fructose and glucose. We also demonstrated that AIV and NIV were probably the primary enzymes responsible for sucrose hydrolysis during ripening, while SPS and SuSy were responsible for sucrose synthesis. We propose that during fruit ripening of jackfruit, glucose is released from starch hydrolysis, followed by sucrose hydrolysis leading to increase in both glucose and fructose contents.
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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) | 0.000 | 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 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".