Effect of MW-assisted roasting on nutritional and chemical properties of hazelnuts
Bibliographic record
Abstract
In order to enhance the flavor, texture, color, and appearance of hazelnuts, they are roasted during postharvest processing. In this study, raw hazelnuts (Corylus avellana L.) were roasted using microwave (MW) and MW-assisted hot air methods under various roasting conditions. The hazelnuts roasted were then examined to determine the percent DPPH radical scavenging activity, antioxidant capacity, total phenolic content, resistant starch, non-resistant starch, total starch, and protein concentration. The roasting experiments were done using a completely randomized factorial arrangement of two roasting types by three roasting times (9, 15, and 21 min) by three roasting temperatures (70, 90, and 110°C) using three replications within each experiment. These roasting methods were found to yield significant differences in antioxidant capacity, total phenolic content, resistant starch, non-resistant starch, and protein concentration between MW and MW-assisted hot air roasting processes, while no difference was found in percent DPPH radical scavenging activity and total starch. The results obtained may be of great importance to the food research community and industrial hazelnut roasting technologies.Keywords: microwave roasting; microwave-assisted hot air roasting; antioxidant activity; resistant starch; protein concentration(Published: 17 December 2015)Citation: Food & Nutrition Research 2015, 59: 28916 - http://dx.doi.org/10.3402/fnr.v59.28916
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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 | 0.001 |
| 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".