Comparative evaluation of drying characteristics and antioxidant quality of raspberry by different drying methods
Bibliographic record
Abstract
Abstract Four drying methods, hot air drying (HAD), microwave constant temperature drying (MWD), microwave vacuum temperature‐controlled drying (MIVD‐T50), and freeze drying (FD), were investigated for their effects on raspberries in terms of drying time, color, microstructure, rehydration, water activity, phenolic compounds, flavonoids, anthocyanins, and DPPH scavenging ability. It was found that microwaves combined with vacuum took the shortest time. The color of MIVD‐T50 is similar to that of FD and much better than that of HAD and MWD. A smooth surface and fluffy and porous structure inside were observed in the FD and MIVD by scanning electron microscopy (SEM) images. FD has the highest energy consumption, followed by HAD, MIVD‐T50 and MWD. The retention rates of total phenols and flavonoids in HAD, MWD and FD were lower than those in MIVD‐T50, and FD had the advantage of retaining anthocyanin content. In general, MIVD‐T50 has significance for the commercial production of raspberry. Practical applications The use of natural antioxidants in food processing not only enhances the shelf life of products but also improves their nutritional value. Raspberry is a rich source of antioxidants and is widely used in food processing. However, the drying process is crucial in preserving its antioxidant quality, and different drying methods affect the antioxidant content differently. According to the results of the experiments, the drying method has a considerable impact on the energy usage and quality of dried raspberries. Compared with the HAD and MWD treatments, both FD and MIVD‐T50 showed better nutrient content preservation and antioxidant activity, which may be caused by the lower temperature drying process of FD or the stronger efficiency of MIVD‐T50. However, the drying time of FD is longer, and the energy consumption is higher. Therefore, MIVD‐T50 is a promising raspberry drying technology with low energy consumption, a short drying time, and high quality. It has huge development potential and broad commercial scale production market prospects.
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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 | 0.001 | 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) | 0.001 | 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".