Instant controlled pressure drop (DIC) coupled to intermittent microwave/airflow drying to produce shrimp snacks: Process performance and quality attributes
Bibliographic record
Abstract
Effects of operating parameters of the instant controlled pressure drop (DIC) texturing process followed by intermittent microwave (MW) and airflow drying were studied for the manufacture of novel swell-dried shrimp snacks. The DIC processing parameters were the absolute pressure of saturated steam (P = 0.4, 0.55, and 0.7 MPa) and the treatment time (t = 70, 100, and 130 s). Intermittent MW drying with active time tON of 30 s and tempering time tOFF of 60 s followed DIC-texturing of blanched shrimp samples (30 g cubes of 1 cm3). The MW density power ℘̇ levels tested were 6, 12, and 24 W/g wb (wet basis). The airflow condition was fixed at 3.2 m/s, 20 °C, and 276 Pa water vapor pressure. Drying performance along with the organoleptic, structural, and functional quality parameters was measured for the dried product. The results showed that the highest DIC processing parameters of P = 0.7 MPa for 170 s were the best texturing conditions, while the MW power level of 24 W/g wb yielded the most effective drying performance when 90% of moisture was removed in 10 min, with organoleptic quality attributes and structural properties better than those of conventionally dried shrimp. Furthermore, the aroma was better preserved, and the exceptionally high absolute expansion ratio (Ɛabs = 13.84) and porosity (φ = 92.83%) allowed enhancement of the desired crispness of the snacks as well. The highly porous microstructure also results in improved rehydration performance.
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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 | 0.000 |
| 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".