EFFECT OF POLYOLS VERSUS SUGAR ON THE SHELF LIFE OF VANILLA ICE CREAM
Bibliographic record
Abstract
Ice cream defects can be classified into many different categories such as defects in flavor, body and texture, color and shrinkage and melting quality characteristics. Quality degradation of packaged ice cream primarily involves body and texture, with the most common problem being the development of a coarse, icy texture. Accelerated shelf-life testing, which involves extremes of heat shock exposure under controlled conditions, is useful in evaluating shelf life; however, it will not provide information about the specific shelf life of the product, given the uncertain nature of the conditions to which it will be exposed. A 6-month study was conducted as a comparison of two vanilla ice cream samples, with the exception of the use of sugar as the control or maltitol as the experimental polyol variable to provide a profile of consumer or customer complaints received, combined with the evaluation of products purchased at or near the sell-by date. A random sample of 19 persons were used as taste panelists and were asked to rate the samples on the basis of sensory characteristics. The evaluation consisted of four distinctive categories such as taste, texture, appearance and overall sample. In this study, no significant differences for coarseness were found for each sample over the 6-month period. This shows that the no sugar-added ice cream retained the texture characteristics of regular ice cream during the 6-month testing period.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".