An exploration of the effects of low-pressure plasma discharge on the physico-chemical properties of chia (Salvia hispanica L.) flour
Bibliographic record
Abstract
<p>This work explores the preliminary feasibility of employing low-pressure cold plasma technology for the modification of the properties of chia flour. Chia flour was exposed to low-pressure plasma in the air for 5 min, 10 min, and 15 min, at two different power levels (40 W and 60 W). The oils extracted from untreated and treated chia flour were exhaustively characterized for fatty acid composition, nutritional value, and rancidity indices using thermal calorimetric methods (DSC/TGA). The results indicated a significant change in the color of flour with an increase in lightness. Infrared and ultraviolet spectroscopy indicated changes in the tocopherol groups of the oil extracted from plasma-treated chia flour. However, the oil extracted from plasma-treated chia flour revealed a loss of conjugated dienes and the formation of trans-fatty acids as seen in conventional hydrogenation of edible oils. DSC and TGA results revealed better oxidative stability of low-pressure plasma-treated oils than in control, which was linked to a relative increase of MUFA in the former.</p>
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".