Evaluation of acid and salt diffusion in selected vegetables as influences by process variables and high pressure treatment
Bibliographic record
Abstract
This research was focused on the evaluation of diffusion phenomenon in selected vegetables for the purpose of acidification and salting in order to aid in acidification thermal processing and ohmic heating applications, respectively. Acidification helps to carryout thermal processing of low acid foods at lower temperatures and hence results in reduced energy costs with improved nutrient retention, while salting helps to accelerate the ohmic heating process. A central composite rotatable design (CCRD) was employed for the design of experiments, using solute concentration (salt or acid), treatment time and solution to particle ratio as process variables. In addition, the effect of high pressure on acid and salt diffusion, and finally de-acidification and desalting techniques were also evaluated.ANOVA and RSM techniques were used for testing the significance of variables and model equations were developed to relate the output to process variables. It was found under conventional processing conditions that acid concentration and time had significant effect (p < 0.05) on acid diffusion, whereas salt diffusion, in addition, was significantly affected by solution to particle ratio. Solute concentration, solution to particle ratio (when significant) and contact time increased the magnitude of component diffusion into the vegetable particle. Numerical optimization was carried out with each process variable within experimental range for identifying suitable conditions to minimize time and concentration of acid and salt. With application of high pressure (200 to 400 MPa) for a short time, the final acid concentration in the sample varied between 0.27 - 0.28 % with pH in the range 3.98 to 3.79. Likewise during salting, the salt concentration achieved varied between 0.19 - 0.31 %. These results suggested that high pressure processing has a good potential in acidification and salting applications, and could possibly be used for infusing nutritional compounds like vitamins and antioxidants to foods which may have low nutrient concentrations. In de-acidification and desalting experiments, the acid concentration decreased from 0.146 - 0.049 % (pH increased from 4.52 to 5.2), and salt concentration decreased from 0.10 - 0.04 % on soaking the samples for 30 min. De-acidification and desalting results indicated that it is possible to remove the excess acid and salt from the food product.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".