OPTIMIZATION OF BLANCHING PROCESS FOR CARROTS
Bibliographic record
Abstract
ABSTRACT Investigations were carried out to study the effects of selected blanching treatments on the quality of carrots over a temperature range of 80–100C. The blanching treatments selected were steam, water, 0.05 N acetic acid solution and 0.2% calcium chloride solution. These blanching treatments were evaluated with respect to the inactivation time of peroxidase (POD) and catalase, and the process was optimized on the basis of the maximum yield of carrot juice and minimum loss of vitamin C andβ‐carotene. The most effective blanching treatment was 5 min in hot water at 95C. At this time–temperature combination, POD and catalase were completely inactivated and the yield of carrot juice and vitamin C andβ‐carotene contents were found to be 55%, 8.192 mg/100 g and 3.18 mg/100 g, respectively. The kinetics of thermal inactivation of POD in carrot juice using various enzyme inactivation models available in the literature was critically evaluated. The Weibull distribution model provided a good description of the kinetics of the inactivation of POD in carrot juice over the temperature range of 80–100C. PRACTICAL APPLICATIONS Blanching is an important unit operation before processing fruits and vegetables for freezing, pureeing or dehydration. The findings of this study would be useful in determining the process parameters for blanching carrots with maximal retention of nutrients. The enzyme residual activity curve indicates the destructive effect of heat on the affected enzymes. A successful modeling will enable the processors to modulate their process according to different time–temperature combinations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".