Antioxidative Ability, Dioscorin Stability, and the Quality of Yam Chips from Various Yam Species as Affected by Processing Method
Bibliographic record
Abstract
The antioxidative ability, stability of storage protein dioscorin, and the quality of fried yam chips from different cultivars of Chinese yams influenced by various processing treatments were investigated. Total phenolic content and DPPH free radical scavenging effect were found to be the highest in Mingchien (MC) and the lowest in Keelung (KL) yam. Following processing, freeze-dried yams of all varieties showed the least decrease in total phenolic compounds and DPPH radical scavenging effect, while boiling caused the greatest decrease in both. Fresh yams of all varieties contained the highest dioscorin contents comparing with their counterparts. Boiling and deep-frying caused severe protein denaturation resulting in loss of dioscorin solubility after purification. Freeze-drying resulted in increase in protein surface hydrophobicity (So); nonetheless, it attained higher total phenol content, antioxidative capacity, and dioscorin stability of yams compared with other processing treatments. The peroxide values of all yam chips increased during the initial stage, then declined with advanced storage. Fracturability of all yam chips gradually decreased, due to the absorption of moisture, with increasing storage time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".