Quality of dried haskap berries (<i>Lonicera caerulea</i>L.) as affected by prior juice extraction, osmotic treatment, and drying conditions
Bibliographic record
Abstract
The effect of juice extraction conditions on the quality of haskap berries (Lonicera caerulea L.) dried at different temperatures (60, 100, and 140°C) was investigated. The conventional juice extraction (process A) consisted of a two-press process, where thawed berries were pressed, and osmotic treatment was applied before pressing again. This was compared with a modified extraction (process B), which applied osmotic treatment during fruit thawing and only one press was used for extraction. The quality parameters investigated included moisture content, pressed berry yield, extraction loss, drying yield, total anthocyanin content (TAC), vitamin C content, and the rehydration characteristics of the final dried berries. Pressing the berries to 70% juice yield resulted in a higher pressed berry yield and better physicochemical quality in the pressed product. The yield was 26.39 and 28.92% in the conventional and modified extraction, with moisture contents of 70.32 and 77.75%, respectively. The TACs of pressed berries from extraction processes A and B were 24.62 and 33.03 mg C-3-G g−1 DW and the vitamin C contents were 14.14 and 36.18 mg/100 g, respectively. Drying at 60°C until 25% moisture content was better than at higher temperatures, resulting in a better quality dried product. It revealed drying yields of 45.32 and 52.75%, TACs of 4.00 and 4.30 mg C-3-G g−1 DW, vitamin C contents of 2.97 and 4.91 mg /100 g, and rehydration ratios of 2.22 and 2.37 from processes A and B, respectively. Process B with the one-step extraction is recommended for higher pressed berry yield, higher drying yield, and enhanced quality of the pressed and dried products. It is also a more efficient process, in terms of time, cost, and energy.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".