Sensory Evaluation as a Tool in Determining Acceptability of Innovative Products Developed by Undergraduate Students in Food Science and Technology at The University of Trinidad and Tobago
Bibliographic record
Abstract
This paper discusses the comprehensive and practical training that was delivered to students in a university classroomon how sensory evaluation can be used to determine acceptability of food products. The report presents how studentsused their training on sensory evaluation methods and analysis and applied it to improving and predicting acceptabilityof new innovative products that they developed. Students were exposed to and trained on performing some of the majorsensory test methods, including discrimination, descriptive, and affective tests. They were also exposed to exerciseswhich involved them physically setting up a test area, presenting samples that were coded and properly displayed,collating data from sensory evaluation questionnaires, statistical analysis of data collated and the use of the results ofthe analysis to make decisions on product acceptability and improvement. Students successfully applied their trainingand were able to not only get feedback on the specific food characteristics of their products that could be improved butwere also able to conclude that the products they presented to the panelists were acceptable and that the panelists had ahighly positive attitude towards eating the products and even purchasing if these were to become available in themarket. Since appropriate statistical analysis was applied for the different sensory evaluation methods used for each ofthe different products, valid information and conclusions that can prove product quality and acceptability was gatheredand can be presented to any product development and marketing departments in any food and beverage company thatmay wish to adopt and produce these products.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".