Microwave Irradiation for Dry-Roasting of Hazelnuts and Evaluation of Microwave Treatment on Hazelnuts Peeling and Fatty Acid Oxidation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>European hazelnut is an important nut crop in Italy, where about 121,750 tons of in-shell nuts are produced every year. Roasting is the most important practice for hazelnut preservation and commonly is carried out in commercial electrical ovens at 120-160°C for 10-20 min. This needful practice is time and energy expensive, so the development of new processing methods is required to reduce processing costs and to obtain top quality roasted nuts. The aim of this study was to develop a simple microwave treatment for hazelnuts peeling and roasting.</p> <p>With this aim, some physical (colour, temperature, moisture) and chemical (taste, lipoxygenase activity, fatty acids, vitamins, sensory attributes) features of inshell nuts and kernels of three Italian hazelnut varieties (Tonda di Giffoni, Tonda Romana and Nocchione) after conventional oven or microwave roasting were evaluated.</p> <p>Results showed that microwave roasting of kernels for 450 s gave a higher peeling score than the conventional oven treatment. This paralleled with better colour and taste scores for microwaved roasted kernels. Furthermore, a 360-450 s microwave roasting was able to inactivate almost completely lipoxygenases, avoiding adverse effects on fatty acids hydroperoxides and PUFA content. A shorter microwave treatment (360 s) was enough to obtain good peeling and sensory scores of inshell hazelnuts.</p> Taken together our results indicated that microwave technology can be successfully applied to both kernels and inshell hazelnuts to obtain suitable peeling and high quality roasted nuts.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it