Sensory Descriptive Profiling and Consumer Acceptance of Made-in-transit (MIT) Set Yoghurt
Bibliographic record
Abstract
Shelf-life loss during the distribution of food is a growing problem for the food industry as manufacturers centralize production into large manufacturing units and expand their markets. Adaptation of made-in-transit (MIT) concept that changes the transportation of food from merely relocating products to a productive system would permit production during distribution. This concept could maximize product shelf-life and providing the consumer with the freshest product. Alteration of some yoghurt processing parameters (e.g. milk base, heat treatment, starter culture concentration and fermentation temperature) was able make the yoghurt suitable for an MIT product. Therefore, this work is to determine the sensory characteristic of two manufacturing methods for MIT set yoghurt. Manufacturing method (1) consisted of a skim milk base fortified with milk protein concentrate (MPC) inoculated with a 0.2% (v/v) inoculum of S. thermophilus STM5 and L. acidophilus LA5 (STLA) in a ratio of 1:1. Manufacturing method (2) consisted of a skim milk base fortified with sodium caseinate (NaCN) inoculated with a 0.002% (v/v) inoculum of STLA. In both manufacturing methods, fermentation was at 25°C for 168 h. Sensory evaluation of the yoghurts manufactured by each method was compared with standard set yoghurt. There were no significant differences (p > 0.05) between the two MIT set yoghurts on sensory evaluation (descriptive test) yet they were significantly different (p < 0.05) to the standard set yoghurt. MIT set yoghurts scored better than standard set yoghurt for overall acceptance.
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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 | 0.000 | 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".