Optimization of maltodextrin (<scp>10DE</scp>)—Sucrose moderated microwave osmotic dehydration of mango cubes under continuous flow spray mode (<scp>MWODS</scp>) conditions
Bibliographic record
Abstract
Abstract The process of microwave osmotic dehydration of mango was optimized under continuous flow medium spray conditions (MWODS) with maltodextrin (10DE) moderated sucrose solutions. Optimization was carried out using a response surface methodology with a central composite rotatable (CCRD) design with three input variables at five levels (temperature, 33°C to 66.7°C; sucrose:maltodextrin ratio from 100:0 to 80:20; and solute concentration, 33 to 66.7%). The response parameters used for optimization were moisture loss (ML), solids gain (SG), weight reduction (WR), ML/SG ratio, color and texture values. For each response, RSM models (p < .05) were developed. As expected, all output variables were responsive to process variables and addition of maltodextrin to sucrose was found to have a significant effect on reducing the SG and increasing ML/SG, and total solute concentration had significant effects on ML, SG and quality parameters. The process was optimized by desirability approach and MWODS at 56°C with total osmotic solute 46% concentration and 84:16 sucrose:maltodextrin proportion had the highest desirability value. Selection of constraints was an influential factor as well. Practical Applications Osmotic dehydration (OD) has many advantages, but is a slow process. Carrying out OD in MW environment accelerates the process, enhances moisture loss (ML) and limits the solids gain (SG). The quality of OD foods is related to ML/SG ratio and the MWOD process enhances ML/SG. This can be further enhanced by incorporating high molecular solutes like maltodextrins. This research optimizes the maltodextrin moderated MWOD process under continuous medium flow conditions. The process offers significant potential for reducing the treatment time (to 30 min) and improve the ML/SG ratio. The resulting product can be finish dried, used as intermediate moisture food or frozen (dehydrofreezing).
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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) | 0.000 | 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 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".