Air-Frying Is a Better Thermal Processing Choice for Improving Antioxidant Properties of Brassica Vegetables
Bibliographic record
Abstract
Brassica vegetables have demonstrated many health benefits over the years due to their composition of phenolic, flavonoid, and glucosinolate contents. However, these bioactive molecules can be easily depleted during gastronomic operations. Therefore, a sustainable method that improves their phenolic content and antioxidant activity is required for both the processors and consumers. Thermal processing has been demonstrated as a method to improve the phenolic content and antioxidant status of Brassica vegetables. In the current study, four different thermal processing methods, including freeze-drying, sautéing, steaming, and air-frying, were employed for five different Brassica vegetables, including kale, broccoli sprouts, Brussels sprouts, red cabbage, and green cabbage. The total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities were assessed using radical scavenging activity (DPPH and ABTS•+), reducing power (FRAP), and the chelating ability of metal ions. Among the methods tested, air-frying at 160 °C for 10 min showed the highest TPC, TFC, and antioxidant activity of the Brassica vegetables, while sautéing showed the lowest. The steam treatments were preferred over the freeze-drying treatments. Within the vegetables tested, both kale and broccoli sprouts contained higher antioxidant properties in most of the employed processing treatments. The results also indicated that there is a strong correlation between the TPC, TFC, and antioxidant activity (p < 0.05). This study indicates that air-frying could be used as a sustainable thermal processing method for improving biomolecules in Brassica vegetables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".