CHARACTERISTICS AND DIFFERENCES OF POLYPHENOL OXIDASE, PEROXIDASE ACTIVITIES AND POLYPHENOL CONTENT IN DIFFERENT POTATO (SOLANUM TUBEROSUM) TUBERS
Bibliographic record
Abstract
Potato enzymatic browning is a serious issue during processing.It not only affects the appearance of potato products but also reduces the nutritional value of potato tubers.In the present study, seven different potato cultivars' tubers were evaluated by measuring the browning index (BI) at different times after cutting.Initial PPO and POD activity and total phenol content, which related to enzymatic browning of plant tissues, were also determined.Results showed significant differences in these factors between the different cultivars.There was significant correlation between BI and PPO, POD activities, but no significant correlation with total phenol content.The activities of PPO and POD and the total phenolic content were higher in the epidermis and perimedullary tissues than pith tissues, which is consistent with their phenotypes.Further, qRT-PCR analysis revealed that the PPO genes were induced by wounding and were more highly expressed in browning-susceptible tubers than browning-resistant tubers, suggesting that browning-susceptible cultivars have higher StuPPO gene expression levels than browning-resistant cultivars.In addition, StuPPO1 and StuPPO2 were the most highly expressed PPO genes in both browning-susceptible/resistant cultivar' tubers, indicating that StuPPO1 and StuPPO2 were the major contributors to the increase in PPO activity and the browning degree in potato tubers.This work suggests that the enzymatic browning of potato tubers is positively correlated with PPO and POD activity.StuPPO1 and StuPPO2 were the main genes responsible for enzymatic browning in potato tubers.
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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".