Impact of Instant Controlled Pressure Drop (DIC) Treatment on Drying Kinetics and Caffeine Extraction from Green Coffee Beans
Bibliographic record
Abstract
The present work is directed towards the impacts of Détente InstantanéeContrôléeDIC (French, for instant controlled pressure-drop) in terms of decaffeination and drying of Ethiopian green coffee beans (GCBs).DICconsisted in subjecting the product to a high-pressure saturated steam during some seconds and ended with an abrupt pressure drop towards a vacuum. A conventional aqueous extraction and a hot air-drying took place after DIC treatment. Inthis study, Response Surface Method (RSM) was used withDIC saturated steam pressure P, thermal treatment time t, and initial moisture content W asthe independent variables. Both direct DICextract recovered from the vacuum tank and the aqueous extracts wereanalyzed and quantified using the reversed phase-HPLC. With decaffeination ratiosas dependent variables, P and Wwere the most significant operating parameters; whilet was much weaker.Total decaffeination ratio could reach 99.5% after DIC treatment at specificconditions of W=11.00% db, P=0.1 MPa, and t=35swhile it was only 58% when achieved with untreated raw material. The effective diffusivity and the starting accessibility were calculated from the diffusion/surface interaction kinetic model of hotair drying after DIC treatment. They dramatically increased with P and t while W had a weak impact.Thus, at the optimized DICconditions, and increased from 0.33 to 12.60 10-10m² s-1and from 0.75 to 11.53 g/100 g db, respectively. Drying time needed to reach 5% db became 60 min instead of 528 min for untreated raw material. RSM analysis showed that the DIC saturated steam pressure P and the initial moisture content W were the most significant variables both affecting the decaffeination ratio; the impact of the total thermal processing time t was much weaker. Total decaffeination ratio could reach 99.5% after DIC treatment at specific conditions of W=11.00% db, P=0.1 MPa, and t=35 s while it was only 58% when achieved with untreated raw material. Using diffusion/surface interaction model of hot-air drying kinetics just after DIC treatment, we could observe that DIC expansion dramatically improved the drying kinetic parameters, with P and t as the most significant DIC operating parameters while the impact of W was much weaker. Thus, the optimized DIC treatment allowed the effective diffusivity and the starting accessibility to increase from 0.33 10-10 m² s-1 and 0.75 g/100 g db to 12.60 10-10 m² s-1 and 11.53 g/100 g db, respectively. Drying time needed to reach 5% db became 60 min instead of 528 min for untreated raw material.
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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".